FORMAT REQUIREMENTS:

The group paper should

• be typed and double spaced; 

• flow as a well documented, coherent, committee paper; 

• give citations for all sources; 

• have correct formats for the bibliography, footnotes and references; 

• have on the first page of the paper, the title of the paper, the author’s names and respective tutorial groups; and 

• have an executive summary. 

Part A: answer according to case study of youngstown bank. (see attachment). explain what kind of risk is the bank facing.

Part B: refer to case study of   ‘The last pit stop? Time for bold late-cycle moves.‘ McKinsey & Company, October 2019 (see attachment).

1

BUS307 COMMERCIAL BANKING TJ 2020

GROUP PROJECT: The last pit stop? Time for bold late-cycle

moves. McKinsey & Company, October 2019

GENERAL REQUIREMENTS

Working in groups of 2 or 3, develop logical and coherent responses to

the questions raised below. The opinions or decisions that are presented

should be supported by references to appropriate texts, articles and

current banking practice.

Although all students are expected to play an important role in the

development of the paper, the final submission should be presented as a

comprehensive group project.

The following lists of deadline and requirements should be adhered to.

Failure to do so will result in a lower grade on the project.

SUBMISSION DATE: Session 5. (See LMS for specific date.)

SUBMISSION INFORMATION:

All group members must sign up in to a team on LMS, on the sign up

sheets available after your first workshop. (If this is not done, a mark will

not be allocated to all group members.)

Only one member of the group is required to submit the assignment via

the Assignment Submission Link on LMS, which can be found under the

Assessment Block.

2

FORMAT REQUIREMENTS:

The group paper should

• be typed and double spaced;

• flow as a well documented, coherent, committee paper;

• give citations for all sources;

• have correct formats for the bibliography, footnotes and references;

• have on the first page of the paper, the title of the paper, the author’s
names and respective tutorial groups; and

• have an executive summary.

3

Part A: [20 marks]

Quality is the most important determinant of the grade awarded but it is

suggested that approximately 1,500-2,000 words be a suitable length.

The group paper should have an executive summary.

SPECIFIC QUESTIONS

Discuss the interrelationships among the different sources of bank risk

exposure.

Why would the construction of a bank risk-management model to

measure and manage only one type of risk be incomplete?

Part B: ‘The last pit stop? Time for bold late-cycle moves.‘
McKinsey & Company, October 2019

[ 30 marks]

Quality is the most appropriate determinant of the grade awarded but it

is suggested that approximately 2,000-2,500 words be a suitable length.

The group paper should have an abstract.

Analyse the article, ‘The last pit stop? Time for bold late-cycle moves.’

McKinsey & Company, October 2019 from the perspective of banks.

SPECIFIC QUESTIONS

“What explains the difference between the 40% of banks that create

value and the 60% that destroy it?” Compare and contrast the different

approaches taken and available to banks aspiring to create value.

Ian Finlayson

December 6, 2019.

CASE STUDY: YOUNGSTOWN BANK
(Adapted from Greenbaum, Thakor, & Boot. 2016. Contemporary Financial
Intermediation 3rd Ed.)
Introduction

John Standard has been the CEO of Youngstown Bank since the summer of 1998.
Before taking this position, he had been a vice president of operations for Interbank, a large
regional bank. One of the primary reasons that he was hired by Youngstown Bank was his
experience with a large operating department. At the time, Youngstown Bank had been
going through some difficulties related to inefficient operating procedures, and Mr.
Standard had acquired a reputation at Interbank for strong motivational and organizational
skills. His management of Youngstown has been almost flawless, and the institutional
culture of the bank takes great pride in the fact that the bank is a very “tight ship.”

Youngstown Bank has been in business in Youngstown, Arizona, since 1910. When
John Standard was brought in as CEO in 1998, the stock price was at 4½, down from a high
of 10. The previous CEO was the son of the founder, and he had resisted the replacement of
legacy systems with more modern information processing infrastructure, allowing the
operating departments to languish in mediocrity. Prior to Mr. Standard’s arrival, people
barely even knew what the bank’s policies were on loans! The only kinds of products
Youngstown Bank offered were simple fixed-rate loans. John Standard changed all that. He
put together a set of standard procedures for loans and loan commitments, and attempted to
tailor the bank’s policies to the risk and liquidity needs of its customers. And the stock
price responded; by the end of 1999, Youngstown Bank’s stock price had doubled to $9,
and continued to rise through 2000.

But starting in 2001, the bank’s stock price has been languishing. Even though the
bank’s basic structure has not changed and profitability is good, the stock price has simply
not moved upward over time, although the stock prices of some competing banks have
moved up significantly. The major shareholders in the bank aren’t too upset yet, but there
have been a few grumblings. Standard realizes that there could be major trouble down the
line unless he can find a way to get the share price up. He decides to call in his chief
financial officer (CFO), Bryan Shelton, to discuss the stock price situation.

The Initial Meeting

Standard : Come on in, Bryan, and have a seat. Let’s get right down to business here.
I’m worried about our stock price performance lately. You’ve been with Youngstown
Bank for three years now – what was the stock price when you got here?
Shelton : It was right around 37, I think.
Standard : Well, it is just over 40 now. We closed at 40 ¼ yesterday. That’s only 3
dollars in 3 years! What is going on? I don’t understand it. Why is our stock price so
low? Take a look at how our market-to-book ratio compares with that of our
competitors. It is in the dirt! (See Exhibit A). Why?

Shelton : That’s a good question. Considering how precisely we control everything,
and considering that our profits and cash flows are still looking good, I don’t know of
any reason why the stock should be down. I’m tempted to just say that the market is
failing to recognize our value. Maybe they’ll come around when we post good
numbers again next quarter.
Standard : Well, you might be right, but I’m uncomfortable. Maybe the market is
reacting to something that we don’t know about. I think we should look into this
some more, and try to get to the bottom of it. [ The meeting ends on that note, and
Mr. Shelton says that he will look into the matter carefully and report back. He agrees
that they should meet a week later to discuss the issue again. ]

The Second Meeting

Shelton : Well, I’ve looked into this some more, and frankly I’m still puzzled. Take a
look at these numbers. Our current balance sheet looks good, and compares very
favorably with the way it looked during 2000, the heyday of our stock price rise (see
Exhibit B). Our key rations look just fine, too, compared to 2000 (see Exhibit C).
Moreover, we also seem to be doing well relative to industry averages (see Exhibit
D).
Standard : This all looks great, just like I thought it would. Look at this one. ( He
points at Exhibit D. ) Our return on assets is great. So what do you think?
Shelton : Well, one of the people I had helping me to put these numbers together for
you suggested that we might want to think about our loan commitments, which don’t
appear on our balance sheet. Maybe those are dragging our stock price down.
Standard : That doesn’t make sense. Our policies on loan commitments haven’t
changed, have they? What kind of data do you have on those?
Shelton : Well, take a look at these. ( He pulls out Exhibits E and F. ) These show the
history of interest rates and the fees that we charge for loan commitments. I checked
on the kinds of borrowers who’ve been buying these commitments, and the quality of
the borrowers seems to be in line with our history. To tell you the truth, I’m still
struggling with what all this stuff means. I don’t see that anything has changed
anywhere. But our stock price. . .
Standard : Well, all I can tell you is keep working on it. See if you can find anything
here that will help explain why our stock price is low. Is there something that we’ve
overlooked? Is the bank in some danger that we’ve failed to realize? [ Again, the
meeting ends and they agree to meet in a week. This time, Standard has some specific
questions to which he wants answers. Shelton plans to go over everything carefully,
looking for some explanation for the poor performance of the stock price, an
explanation that takes into account all the facts about the bank’s situation. ]

CASE STUDY: YOUNGSTOWN BANK EXHIBITS

The Numbers

Exhibit A YOUNGSTOWN BANK, INC.

Market-to-Book Ratio Comparison to Industry
Year Youngstown BancFirst Industry
1991 0.51 1.21 1.18
1992 1.00 1.11 1.08
1993 1.43 1.23 1.13
1994 1.47 1.32 1.21
1995 1.60 1.43 1.31
1996 2.13 1.87 1.53
1997 1.35 1.41 1.41
1998 1.18 1.11 1.20
1999 1.35 1.32 1.27
2000 1.41 1.31 1.34
2001 1.21 1.40 1.47
2002 0.95 1.65 1.53
2003 0.81 1.89 1.66
2004 0.78 1.86 1.63

CASE STUDY: YOUNGSTOWN BANK EXHIBITS

Exhibit C YOUNGSTOWN BANK, INC.

Comparison of Performance for 2000 and 2005
2000 2005
Net Income (in thousands of dollars) 8,607 16,820
Return on Assets (in percentage) 0.73 0.80
Total Liabilities to Total Assets 0.94 0.97 0.94
Total Liabilities to Common Equity 27.71 14.56

Exhibit D Various Industry Ratios for 2005
(Averages for Similarly Sized Banks)

Youngstown Average
Return on Assets 0.8 0.6
Total Liabilities to Total Assets 0.94 0.97
Total Liabilities to Common Equity 14.56 21.3

Exhibit B YOUNGSTOWN BANK, INC.
Year-End Balance Sheets
(in Thousands of Dollars)

2000 2005
Assets
Cash & Due 125,000 129,000
Marketable Securities 200,000 400,000
Loans:

Real Estate 190,000 385,000
Commercial and Industrial 315,500 744,000
Consumer 140,500 153,742
All Other 131,400 142,300

Less Unearned Income:
Allowances for Possible Loan Losses 1,316 1,500

Total Loans 776,084 1,423,542
Other Assets 78,000 150,000
Total Assets 1,179,084 2,102,542
Liabilities and Equity
Liabilities:

Deposits 1,000,020 1,775,420
Federal Funds Purchased 75,000 102,000
Other Liabilities 63,000 90,000
Total Liabilities 1,138,020 1,967,420

Equity Capital:
Preferred and Common Stock 11,000 35,122
Surplus 14,064 42,000
Undivided Profits and Reserves 16,000 58,000
Total Equity Capital 41,064 135,122

Total Liabilities and Equity 1,179,084 2,102,542
Note: Volume of outstanding loan commitments in 2000 was $1,000,500 and 2005 was $4,320,000.

CASE STUDY: YOUNGSTOWN BANK EXHIBITS

Exhibit E Interest Rate History
(Annualized Interest Rates in Percentage)

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1991 7.95 8.00 8.00 8.00 8.27 8.63 9.00 9.01 9.41 9.94 10.94 11.55
1992 11.75 11.75 11.75 11.75 11.65 11.65 11.54 11.91 12.90 14.39 14.55 15.30
1993 15.25 15.63 18.31 17.77 15.57 12.63 11.48 11.69 12.23 14.79 16.06 17.10
1994 20.16 19.43 18.05 17.15 19.61 20.03 20.39 20.50 20.06 18.45 16.84 16.75
1995 15.75 16.56 16.50 16.50 15.50 15.50 14.26 14.39 13.50 12.52 11.85 11.50
1996 11.16 10.98 10.50 10.50 10.50 10.50 10.50 10.89 11.00 11.00 11.00 11.00
1997 11.00 11.00 11.21 11.93 12.39 12.60 13.00 13.00 12.97 12.58 11.77 11.06
1998 10.61 10.50 10.50 10.50 10.31 9.78 9.50 9.50 9.50 9.50 9.50 9.50
1999 9.50 9.50 9.10 8.83 8.50 8.50 8.16 7.90 7.50 7.50 7.50 7.50
2000 7.50 7.50 7.50 7.75 8.14 8.25 8.25 8.25 8.70 9.07 8.78 8.75
2001 8.75 8.51 8.50 8.50 8.84 9.00 9.29 9.84 10.00 10.00 10.05 10.50
2002 9.80 9.10 8.20 7.80 7.20 6.30 5.32 5.01 7.73 5.21 5.09 8.30
2003 9.20 8.30 7.40 7.10 6.20 5.50 5.10 4.80 4.50 6.20 9.10 8.10
2004 6.10 3.00 3.00 3.00 4.00 6.83 9.23 9.30 10.20 8.50 7.43 8.91

Exhibit F Loan Commitment Prices
(Average in Basis Points)

Commitment Fee Annual Servicing Fee Usage Fee
1994 12.5 12.5 25.0
1995 12.0 12.0 25.0
1996 12.0 12.0 25.0
1997 12.5 12.0 22.5
1998 12.5 12.5 22.5
1999 12.5 12.5 21.5
2000 12.5 12.5 22.5
2001 12.5 12.5 25.0
2002 12.0 12.5 25.0
2003 12.5 12.5 25.0
2004 14.0 12.5 27.5

October 201

9

The last pit stop?
Time for bold
late-cycle moves
McKinsey Global Banking Annual Review

2019

Literature title

2

Conten

t

0

4

Executive summary

0

7

The late cycle:
Welcome to uncertainty

23

Time for bold moves:
Levers to improve performance in the late cycle

41

The right moves for the right bank

5

5

Conclusion

3 The last pit stop? Time for bold late-cycle moves

A decade on from the global financial
crisis, signs that the banking industry has
entered the late phase of the economic
cycle are clear: growth in volumes and
top-line revenues is slowing with loan
growth of just four percent in 2018—the
lowest in the past five years and a good
150 basis points below nominal GDP
growth. Yield curves are also flattening.
And, though valuations fluctuate, investor
confidence in banks is weakening
once again.

Industry veterans have been through
a few of these cycles before. But,
notwithstanding the academic literature,1
this one seems different. Global return
on tangible equity (ROTE) has flatlined
at 10.5 percent, despite a small rise in
rates in 2018. Emerging market banks
have seen ROTEs decline steeply, from
20 percent in 2013 to 14.1 percent in
2018, due largely to digital disruption that
continues unabated. Banks in developed
markets have strengthened productivity
and managed risk costs, lifting ROTE
from 6.8 percent to 8.9 percent. But on
balance, the global industry approaches
the end of the cycle in less than ideal
health with nearly 60 percent of banks
printing returns below the cost of equity. A
prolonged economic slowdown with low or
even negative interest rates could wreak
further havoc.

What explains the difference between
the 40 percent of banks that create
value and the 60 percent that
destroy it? In short, geography, scale,
differentiation, and business model.

1 Carmen M. Reinhart and Kenneth S. Rogoff, This Time is Different: Eight Centuries of Financial Folly,
Princeton, NJ: Princeton University Press, 2009.

On the first, we find that the domicile
of a bank explains nearly 70 percent
of underlying valuations. Consider the
United States, where banks earn nearly
ten percentage points more in returns
than European banks do, implying
starkly different environments. Then
comes scale. Our research confirms that
scale in banking, as in most industries,
is generally correlated with stronger
returns. Be it scale across a country, a
region, or a client segment. Having said
that, there are still small banks with niche
propositions out there generating strong
returns, but these are more the exception
than the rule. Underlying constraints of
a business model also have a significant
role to play. Take the case of broker
dealers in the securities industry, where
margins and volumes have been down
sharply in this cycle. A scale leader in the
right geography as a broker dealer still
doesn’t earn cost of capital.

Domicile is mostly out of a bank’s control.
Scale can be built, though it takes time;
attractive acquisitions and partnerships
are currently available for most banks.
But on their individual performance
irrespective of scale or business model,
banks can take immediate steps to
reinvent themselves and change their
destiny, inside the short window of
a late cycle. Three universal organic
performance levers that all banks
should consider are risk management,
productivity, and revenue growth.
All while building the talent and the
advanced data analytics infrastructure
required to compete.

Executive
summary

What explains the
difference between the
40 percent of banks that
create value and the
60 percent that destroy
it? In short, geography,
scale, differentiation,
and business model.

4The last pit stop? Time for bold late-cycle moves

Worldwide, risk costs are at an all-time low
with developed market impairments at just
12 bps. But just as counter-cyclicality has
gained prominence on regulators’ agendas,
banks also need to renew their focus on risk
management, especially the new risks of an
increasingly digital world. Advanced analytics
(AA) and artificial intelligence (AI) are already
producing new and highly effective risk tools;
banks should adopt them and build new ones.
On productivity, marginal cost-reduction
programs have started to lose steam. The need
of the hour is to industrialize tasks that don’t
convey a competitive advantage and transfer
them to multi-tenant utilities. Industrializing
regulatory and compliance activities alone
could lift ROTE by 60 to 100 bps. Finally, on
generating elusive revenue growth, now is
the time to pick a few areas—client segments
or products—and rapidly reallocate top
customer-experience talent to attack the most
valuable areas of growth and take share as
competitors withdraw and customer churn
increases late in the cycle.

What’s the right next step? Every bank is
uniquely bound by both the strength of its
franchise and the constraints of its markets or
business model. Using these two vectors, we’ve
identified four archetypes that banks around
the world can use to identify their starting
positions and develop their late-cycle priorities:

Market leaders. Twenty percent of banks
globally capture almost 100 percent of the
economic value added by the entire industry.
These at-scale banks typically serve a large
share of a geography, region, or FXVWRPHU�
segment and operate in favorable market
conditions. Their clearest imperative is to
reinvest capital and resources intelligently in

innovation and further scale for the next cycle.

Resilients. Nearly 25 percent of banks have
maintained leadership in challenging markets,
including many in Europe. Resilients should
focus on expanding beyond their direct set of
customers and products through ecosystem
plays and differentiating further through
innovation.

Followers. About 20 percent of banks have
not achieved scale, and are weaker than peers,
despite favorable market dynamics. They are
at risk from a downturn and must act promptly
to build scale in their current businesses, shift
business models to differentiate, and radically
cut costs.

Challenged banks. About 35 percent of
banks globally are both sub-scale and suffer
from operating in unfavorable markets. Their
business models are flawed, and the sense
of urgency is acute. To survive a downturn,
merging with similar banks or selling to a
stronger buyer with a complementary footprint
may be the only options if reinvention is not
feasible.

Risk costs are lower than ever, and yet
60 percent of banks destroy value. That’s
a poor state of affairs in which to enter a
downturn and it calls for bold actions. This is
likely the last pit stop in this cycle for banks to
rapidly reinvent business models and scale up
inorganically. Imaginative institutions are likely
to come out leaders in the next cycle. Others
risk becoming footnotes to history.

This report is based on data and insights from
McKinsey Panorama, McKinsey’s proprietary
banking research arm, as well as the
experience of clients and practitioners from all
over the world.

5 The last pit stop? Time for bold late-cycle moves

“…rather than depend on
forecasts of the future,
we depend on reading
the present. I believe one
of the greatest predictors
of what the market’s
going to do … is where
it stands in the cycle.”1
Howard Marks, Oaktree Capital Co-founder

6The last pit stop? Time for bold late-cycle moves

The late cycle:
Welcome to
uncertainty

Economic forecasts often miss their target, but Howard
Marks reminds us that knowing where we stand today can
help us understand the balance of probabilities for the future.
As the recovery from the global financial crisis completes
its tenth year, the signs of decelerating growth across the
world are unmistakable. That, along with declining operating
performance and corrections in valuations, points to what1
many would call “late-cycle” trends. To quote Fidelity’s Market
Insights report, “the economy looks like it may be approaching
its late cycle phase, which is typically when growth slows,
inflation rises, […] and the yield curve flattens, meaning the
gap between short- and long-term rates shrinks.”2 From
the evidence we see, it definitely feels “late cycle” for the
banking sector.

To be sure, there have been some downturns during this
decade-long recovery, but this time the breadth and depth of
the slowdown signal that we have entered the final stages of the
economic cycle. To build momentum and power through rising
headwinds and uncertain currents, banks must take a hard look
at where they stand in the competitive landscape and assess
both the constraints and opportunities that arise late in the
cycle, with attention to how structural factors affect business
lines and geographies differently.

Emerging late-cycle trends increase uncertainty
While growth over the past decade was slower than prior to
the global financial crisis, the pace has picked up a bit recently,
as banks repaired balance sheets and took advantage of low

1 “Howard Marks, CFA: Getting the Odds on Your Side,” CFA Institute, 19 Feb 2019,
https://blogs.cfainstitute.org.

2 Fidelity Market Insights report, “Is it getting late for stocks?”

This time the breadth
and depth of the slow

–

down signal that we have
entered the final stages
of the economic cycle.

7 The last pit stop? Time for bold late-cycle moves

interest rates to increase lending volumes. Growth in bank
assets is no longer keeping up with growth in nominal GDP,
however, and this, historically, has been a turning point for
banks as the cycle begins to wind down. In 2018, global bank
lending grew by 4.4 percent, the slowest rate over the past
five years and well below nominal GDP growth of 5.9 percent.
Except for the US, where economic growth remains strong, the
expansion of lending volumes is slowing in both developed and
emerging markets (Exhibit 1).

Another late-cycle development is faster margin compression.
Margins have been declining for some time, but the pace of
deterioration has quickened recently. Average margins before
risk in developed markets declined from 234 bps in 2013 to
225 bps in 2018, despite rising interest rates in markets such
as the US and Canada. In emerging markets, margins fell
even more sharply from 378 bps in 2013 to 337 bps in 2018
(Exhibit 2).

More inklings of the end of the cycle come from productivity
and risk costs. While the two main levers have served well to
increase returns for most of the decade, they have become
less effective in recent years. Annual gains in productivity
have been more modest since 2016 in both developed and
emerging markets, with the average ratio of cost to assets
(C/A) as of 2018 reaching 144 bps in developed markets
and 129 bps in emerging markets. For the first time in a

long while, emerging market banks have made bigger
productivity gains, lowering their C/A ratio by 38 bps since
2013, compared to just 12 bps among developed market
banks (Exhibit 2).

On the risk front, in developed markets the impairment rate of
13 bps is by far the lowest in the past two decades, while the
rate of unemployment is also low at 3.5 percent, which suggests
that risk costs are unlikely to moderate further. In emerging
markets, impairment rates have been hovering around 70 bps
since 2015 in line with the long-term average. The deterioration
of ten bps in risk cost provisions in 2018 in China—the largest
emerging market—is of particular note.

These deteriorating metrics have yet to show up in global
ROTE, which has hovered around 10.5 percent for ten years.
In the last couple of years, tax cuts and higher base rates in
developed markets such as the US have offset the underlying
deterioration in operating margins in most other markets. In
the meantime, ROTEs in developed and emerging markets
continue to converge rapidly, due largely to the erosion of
profitability in emerging markets. Thinning margins, declining
asset quality, and higher capital needs have pushed the
average ROTE for emerging markets down from 20.0 percent
in 2013 to 14.1 percent in 2018. Developed market ROTEs,
however, have been rising gradually, from an average of
6.8 percent in 2013 to 8.9 percent in 2018. As noted above,

4.4%
2018 growth in global bank lending, the slowest rate
over the past five years and well below nominal GDP
growth of 5.9 percent.

8The last pit stop? Time for bold late-cycle moves 8The last pit stop? Time for bold late-cycle moves

Across all markets, margin contraction has accelerated, while the rate of gains from
productivity improvements has diminished

Global banking productivity, cost/assets!
2013–18, bps

Global banking before risk margins , revenues/assets”
2013–18, basis points (bps)

Exhibit 2

!Revenue to average assets, based on a sample of ~1,000 largest banks in terms of assets. “Operating expenses over average assets.
Source: SNL; McKinsey Panorama

2013 2015 201

8

20

0

2

50

300

350

400

–2 –7

+1

–42

2013 20152014 2016 2017 2018

170

1

60

150

1

40

1

30

Emerging markets

Developed marke

ts

The rate of global lending growth has moderated, lagging behind nominal GDP growth in
recent yea

rs

Exhibit 1

Global bank lending by market and GDP growth rate (2018 constant FX rate), %

Compound annual growth rate, %

Global

GDP

Global
bank
lending

Note: Constant foreign-exchange rate (FX) used to remove FX volatility in results. 2018 is an estimate.
Source: McKinsey Panorama, Global Banking Pools.

2013–17 2017–18

4

5 4

6

Global bank lending!

–2

0
2
4
6
8

10

12

2006 2008 2010 2012 2014 2016 2018

Global GDP

9 The last pit stop? Time for bold late-cycle moves

Global returns on tangible equity (ROTEs) have !atlined at 10.5 percent since 2013, despite a
pickup in yields
ROTE,! 2013–18, % 2013–18 ROTE! momentum, percentage points

2014 2016 201720152013 2018

2013 2018Margin
before
risk!

Fines,
taxes,

and other”

LeverageRisk
cost#

Prod-
uctivity$

2013 2018Margin
before
risk!
Fines,
taxes,
and other”
LeverageRisk
cost#
Prod-
uctivity$
0
5
10

15

20

Emerging
world

Emerging world

Decelerating growth

Developed world

Developed
world

Global

–6.2

–1.9

–4.7

–

2.5

–0.1 –

1.6

5.9

3.3
2.3

1.62

0.0

6.8
8.9

14.1

%Based on a sample of ~1,000 global largest banks in terms of assets. Pro&t after tax over tangible equity. !Operating income/assets. #Impairments/assets. $Operating
cost/assets. “Includes regulator &nes, customer redress, impairment of goodwill, gains/losses from discontinued operations, and restructuring charges.

Accelerating growth

Exhibit 3

Customers increasingly prefer digital channels

Online banking usage rate,! 2013–18, % of population
Gap between ‘willingness to purchase via digital
channels’ and ‘actual sales behavior’ in 2018,” %

!Share of individuals within bankable population using any internet-enabled device for Internet banking (including desktop, portable or mobile computer, tablets, smartphones,
etc.; apps are also included). “Consumer survey, responses to question asking: “For the products acquired in the last 2 years, please indicate the main channel you used to buy
the product”; % of respondents having bought the product in last 2 years that have done it via a digital channel (selected “Internet Banking”, “Tablet Banking” or “Mobile
Banking”). #Only includes United Arab Emirates. $Only includes Australia and New Zealand.
Source: 2018 Retail Banking Consumer Survey (n = 45,000), McKinsey Digital Banking Pools

Asia–Pacific

North
America

Average 40

Europe Middle
East#

Asia–
Paci%c$2014 2016 201720152013 2018

0
20
40
60

80

Middle East

Europe

North America

35
38

55

33

Exhibit 4

10The last pit stop? Time for bold late-cycle moves

the main factors driving this improvement have been higher
productivity and declining risk costs. Within developed
markets, however, it has been a tale of two worlds: North
America, with ROTEs of 16 percent in 2018, and Western
Europe, where even after a 54-bps recovery, banks delivered
ROTEs of just 6.5 percent in 2018 (Exhibit 3).

Capital markets are responding to the change in the pace
of banks’ earnings growth globally with valuations declining
between 15 and 20 percent since the start of 2018. The
drop in valuation suggests that investors anticipate a sharp
deceleration in earnings growth. This outlook is corroborated
by earnings forecasts with sell-side analysts modelling in
approximately 6.5 percent for the next three years, a far cry
from the 13 percent growth seen in the previous four years.
In the meantime, with the outlook for interest rates in most
markets pointing towards downward revisions, the trajectory of
banks’ earnings revisions continues downwards.

Late-cycle pressures compounded by ongoing disruption
The digital disruption examined in previous editions of
McKinsey’s Global Banking Annual Review continues
unabated, fueled by structural shifts on both the demand
side, through changing consumer behaviors, and supply side,
through a growing number of product providers.

The new consumer
Globally, online banking usage rates increased on average
by 13 percentage points from 2013 to 2018, and there is
room for further growth across all geographies, particularly
as consumers’ willingness to transact over digital channels
exceeds actual digital usage by more than 30 percentage
points in many markets (Exhibit 4). Consumers have become
accustomed to real-time and personalized services and expect
the same of digital banking solutions.

While this behavior is most acutely felt in retail banking and
asset management, we are starting to see the same trends
emerge in corporate banking as well as in capital markets
and investment banking (CMIB). A classic example is a
trend within transaction banking where clients increasingly
demand a single window and real-time multi-currency
multi-asset view of a firm’s payments positions with reduced
settlement times with each passing year. They also expect
banking services to be increasingly linked into their internal
finance and treasury functions. Within retail banking,
where customer loyalty has traditionally been strong,
rates of customer attrition are rising, as digital technology
and changing regulations make it relatively painless for
customers to change banks. For instance, churn rates for
current accounts in the US have risen from 4.2 percent in

13pp
average increase across the globe in online banking
usage rates between 2013 to 2018

11 The last pit stop? Time for bold late-cycle moves

2013 to 5.5 percent in 2017, and in France they have risen
from 2.0 percent in 2013 to 4.5 percent in 2017.

New standards for propositions
The rise in customer churn rates results not only from changes
in customer expectations but also from the superior levels of
service offered by new entrants. These new entrants have
also benefited from regulatory changes which have lowered
barriers to entry, as well as the continued inflow of capital
from investors willing to bet on challengers taking a large
share of the profit pool traditionally captured by incumbents.
These innovative disruptors have been able to offer customer-
centric propositions that better meet customers’ needs with

engaging and intuitive user interfaces, which, in some cases,
can connect to other platforms and become part of a broader
ecosystem. All the while, bringing pricing down with increasing
transparency for the end consumer. For instance, in China, Ping
An has built an ecosystem that includes healthcare, automotive,
entertainment, and tourism services, while in the US, Amazon
offers businesses the traditional banking suite (that is, current
accounts, credit cards, unsecured loans), while connecting
them to the Amazon ecosystem, which includes non-financial
products and services.

A supportive regulatory environment for competition
Regulators in diverse markets are also contributing to
disruption in banking, especially as they take steps aimed
at increasing transparency and boosting competition by
lowering barriers to entry. Among the most disruptive of these
initiatives are moves to open access to customer financial data
(on consent) to non-bank service providers. These have been
a significant catalyst for new competition from technology-
oriented entrants. Open banking, one of the most prominent
regulatory developments affecting innovation and competition
in the banking sector, is at varying stages of adoption in
35 markets (

Exhibit 5

), relating to products that account for
approximately 90 percent of revenue pools in those markets.
The UK and parts of Continental Europe already have fully
operational open banking regimes; for instance, in the UK,
where open banking is being rapidly rolled out, the number of
new entrants in the market has increased by 65 percent in the
past year alone.

3 Excluding Ant Financial.

Platform and fintech disruption at pace
The rapid disruption of Asia’s banking sector is well known,
especially with a swift rise in competition from fintechs and
digital platform companies contributing to a sharp deterioration
of more than 600 bps in ROTE over the past five years.
Developed market banks, wary of the Asian scenario playing
out in their home markets, dream of an end to this fintech
cycle. However, people’s perception of trust towards fintechs
and tech companies continues to improve. For example,
McKinsey’s Future of Banking July 2019 Consumer Survey
found that most respondents trust big tech companies to
handle their financial needs, including Amazon (65 percent)

and Google (58 percent). With this backdrop, it is no surprise
that investments in the fintech space grew by 29 percent3 in
2018, on the heels of 46 percent annual growth since 2014
(Exhibit 6). The challenge for incumbents is intensifying
as the fintech landscape also matures, with new rounds of
funding shifting towards larger organizations and the number
of fintech unicorns globally topping 40 (worth approximately
$150 billion).

When we study disruption across industries, there are always
clear stages to the lifecycle of a typical attack—from faint
signals of experimentation to validated business models to
critical mass or at-scale plays. And repeatedly, the reason
many incumbents fail, irrespective of their strong ingoing
balance sheet and market share, is because of their inability to
acknowledge a trend. Conversely, what’s the secret of those
incumbents that do survive—and sometimes even thrive?
One aspect surely relates to the ability to recognize and
overcome the typical pattern of response (or lack thereof) that
characterizes companies in the incumbent’s position. This most
often requires acuity of foresight and a willingness to respond
boldly before it’s too late, which usually means acting before
it is obvious you have to do so. The good news is that across
many of the banking products highlighted in

Exhibit 7

, attackers
in most markets are still at an early stage. So, if they aren’t to
go the way of the lodging, travel, or publishing industry, where
most incumbents got totally disrupted, those in banking need
to act now.

When we study disruption across
industries, there are always clear stages
to the lifecycle of a typical attack.

12The last pit stop? Time for bold late-cycle moves

Investors continue to fund !ntechs globally, while !ntechs continue to have a material cost
advantage over traditional banks

Global !ntech deal funding amount,!
2014–18, $ billion

Operating–expense comparison across banks with di”erent
digitization levels, 2018, basis points, operating costs to
assets

2014 2015 2016 2017 2018

Traditional”

136

+46% per annum

+29%
Cost leader digital banks#

–72 basis points

!Excluding Ant Financial. “Based on sample of top 1,000 banks’ data. #Cost leader direct bank peer group includes ING Bank Australia, UmweltBank, Granit Bank,
Rakuten Bank, Sumishin SBI Netbank, Sbanken ASA, Gjensidige Bank.
Source: CB Insights, KPMG Pulse of Fintech, McKinsey Panorama Digital Attacker Database & Insights, SNL, Reuters

5.8

12.2

18.3 18.3

23.5

64

Exhibit 6

Regulatory changes continue to lower barriers to entry, with adoption of open banking under
way in 35 economies
Economies that have adopted or are adopting open banking

!Markets in which banks and non-banks lead the move to open banking, with regulators playing a more consultative role.
Source: Press search; expert interviews

Stage 1 Initial steps: Industry
consultations, draft regulations

Regulatory
led

Malaysia
United States
Canada
Mexico

Italy
Spain
Netherlands
Norway

Belgium
India
Japan
Australia

Hong Kong SAR
China

United Kingdom
Germany
France
Sweden

Finland
Ireland
Czech Republic

Denmark
Hungary

Market
led!

Mainland China
Thailand
Singapore

Indonesia
South Korea
Switzerland

Turkey
Russia
Taiwan China

Argentina
Brazil
South Africa

United Arab Emirates

Stage 2 Transpose
to national law

Stage 3 Grant
licenses

Exhibit 5

13 The last pit stop? Time for bold late-cycle moves

In developed markets, large technology platform companies
have made notable forays into the financial services market.
Examples are plenty—from Apple’s launch of Apple Card (a
credit card albeit issued by a bank) to Facebook’s launch of
Calibra as a wallet for its proposed cryptocurrency Libra, as
well as Amazon’s venture into small and medium size enterprise
(SME) lending. Exacerbating the situation, fintechs and big tech
players are attacking the highest ROTE segments of banking,
representing approximately 45 percent of the global banking
revenue pool. This will put additional downward pressure on
banking ROTEs and cash generation at a time when cash is
needed most. Further, as advanced analytics and artificial
intelligence continue to improve, digitization of paper-intensive
businesses such as mortgages, CMIB, and commercial and
transaction banking will increase (Exhibit 7).

The budget challenge to fund innovation and change
The biggest challenge at present for traditional banks is the
need to invest in overhauling operating models to compete
head-to-head with digital innovators. Both banks and fintechs
today spend approximately seven percent of their revenues

on IT; but while fintechs devote more than 70 percent of their
budget to launching and scaling up innovative solutions, banks
end up spending just 35 percent of their budget on innovation
with the rest spent on legacy architecture. Another key
trend is the sharp variance in IT spending across developed
and emerging markets, with developed markets spending
approximately three times more as a percentage of revenue.
This trend has strengthened over time, with IT spending as a
share of revenues in developed markets increasing by 70 bps
since 2013, while emerging market spending decreasing
by 40 bps of revenues over the same period. It comes as
no surprise, therefore, that emerging markets have seen a
high degree of fintech disruption, especially from platform
companies that have led with significant spending on
customer-facing technology. This should serve as a wake-up
call to those incumbents in emerging markets that still enjoy
superior returns.
However, the pressures from increased competition and the
resulting decline in margins and returns limit the amount of
capital that can be put to work on change. With late cycle

Fintechs and tech players are aiming at higher ROE segments

Big tech and !ntech return on equity (ROE) by engagement,
2018, $ billion (total 2018 global banking revenue pool = $5.4
trillion!)

Big tech and “ntech focus area, ($2.4 trillion
revenue = 45% of global revenue pool!)

!Before risk cost.# Capital markets and investment banking.
Source: McKinsey Panorama, Global Banking Pools

Emerging markets Developed markets

CMIB#

Mortgage

Commercial and
transaction banking

Wealth
management

Consumer
“nance

Asset
management

Retail deposits
and payments

CMIB#
Mortgage
Commercial and
transaction banking
Wealth
management
Consumer
“nance
Asset
management
Retail deposits
and payments

58

218

102 405

478 447

352

1

11

59

400

2

46

319

1,159 1,033

High

ROE

Low

High

Intensity of customer
engagement/data

Low High

ROE
Low
High
Intensity of customer
engagement/data
Low
Exhibit 7

14The last pit stop? Time for bold late-cycle moves

macroeconomics of slowing growth and lower rates thrown in,
these pressures are likely to increase. Continuing regulatory
costs don’t help either. Further complicating this challenge
globally is the wide spread in free cash flow generation
between top- and bottom-quartile banks. This spread peaked
in 2018, creating completely different playing fields in the
capacity to invest for the future. The top-quartile banks, with
their healthy cash flows, are in the most privileged position they
have been in in the recent past. Bottom-quartile organizations,
by contrast, will likely struggle to generate the free cash flow
required to keep them in the race for customers’ wallets—
especially the digital one (

Exhibit 8

)

This cycle has also felt different
We have seen how the omens of the end of the cycle are mounting.
And we have detailed how digital pressures are already influencing
some metrics such as profitability in Asia and may soon affect
other regions. Yet the impending downturn, if we may call it that,
may be the most predicted recession in history. Surely banks are
ready for it?

35%
of budget spent on innovation by banks, compared
to 70 percent of budget spent by fintechs.

As growth has slowed, the gap in free cash !ow (FCF) generation between top–quartile banks
and bottom–quartile banks has widened

FCF to tangible equity, % FCF, $ billion

Bank FCF after taxes!

Top quartile

Bottom quartile

Top quartile
Bottom quartile

Spread 9.4
percentage points

2013 20152014 2016 2017 2018 2013 20152014 2016 2017 2018
-5

0
5
10
15
20

-50

0
50

100

150

200

!Based on a sample of ~100 largest banks in terms of assets, with consistent reporting over timeline. Sample of ~100 largest banks used to account for scale in the FCF
generation in dollar terms to avoid skew from small banks. Results on a FCF to tangible equity hold if we use the full bank data set.
Source: SNL; McKinsey Panorama

Spread
$133 billion

Exhibit 8

15 The last pit stop? Time for bold late-cycle moves

Unfortunately, not—at least as compared to their financial
condition at the end of the previous cycle. Not only has growth
over the past decade been slower than the decade preceding
the global financial crisis, but most banks have not regained
their pre-crisis level of profitability (

Exhibit 9

).

By several other measures, as well, banks are not nearly as
fit as in 2007. In fact, more than 60 percent of banks still do
not generate their cost of equity ten years after the crisis
(Exhibit 10).

As banks in the 60 percent contemplate how to cross the
divide—and those already there seek to secure their footing—
they must first reckon with two foundational characteristics
that go a long way to determining both their current position
and the extent of potential improvements: geography and scale.

First, location matters more now
Banks’ opportunities for growth depend on their markets, of
course. As an illustration, the current cycle has been marked
by considerable disparity in the fortunes of North American
and European markets. If North American banks have been a
beacon of optimism, with average ROTE hitting 16 percent in
2018, banks in Europe have not achieved even half this rate.
What is more, the divergence in returns between these two
geographies, which together hold the bulk of the balance
sheet for developed markets, widened by more than 450 bps in

2018. Geography can also be seen at work in the consolidation
of US strength in global investment banks, at the expense of
European peers bogged down by sluggish home markets. More
broadly, it is evident that the influence of geography ebbs and
flows, and lately seems to have become much more powerful
(Exhibit 11).

A wide divergence in monetary, fiscal, and trade policies across
the developed and emerging world seems to have been behind
the increasing importance of geography as a factor.

Second, scale matters increasingly for most banks
Scale is not simply a matter of size, but the relationship
between operating capacity and processing volume. When
an organization is operating “at scale,” the revenue generated
by processing volume covers a) operating costs, b) capital
costs, and c) the minimum income or return (as defined by the
organization’s business strategy). The “scale advantage” is the
ability of an organization to increase volume (and revenue) while
incurring very low or zero incremental cost, with new revenues
going largely (or entirely) to the bottom line.

Our analysis of more than 1,000 banks across developed and
emerging markets shows that banks with leading in-country
market share enjoy a ROTE premium compared to peers. Also
those with the highest shares are the only ones that have
shown growth in positive returns in recent years. While the rest

This cycle has been di!erent from previous expansions, with most metrics looking much worse

Banking metrics compared

!Revenues before risk cost, compound annual growth rate 2001–10, 2010–18 (client–driven revenues). “Customer loans and deposits.
Source: Thomson Reuters, SNL, McKinsey Panorama – Global Banking Pools

2002–07
Unsustainable expansion

Robust growth, high ROTE,
high multiples

Summary Slow growth, lower ROTE,
lower multiples

2010–18
New reality

Average returns on tangible equity (ROTE)

Revenue growth!

Emerging markets’ share of revenue growth

Tier 1 Ratio (average)

Loan/deposit”

Price/book value

Percent of banks trading below book value

16.9% 10.5%

16.8% 3.

6%

Developed 146% 126%

Developed 2.2# 1.00#

Emerging 2.2# 1.22#

26.9% 77.4%

8.4% 12.5%

Emerging 84% 86%

Developed 28.4% 61.2%

Emerging 19.2% 37.6%

Exhibit 9

16The last pit stop? Time for bold late-cycle moves

Geography has sprung back as a performance driver, with spread in performance within
Europe and North America being most pronounced

!Largest 1,000 global banks allocated to country/regions by location of headquarters. “As of August 30.
Source: SNL; Capital IQ; McKinsey Panorama

Price–to–book (P/B) ratios of large quoted banks!, 2011 and 2019″

0.0

0.5

1.0

1.5

2.0

2.5
0.0
0.5
1.0
1.5
2.0
2.5

CanadaBrazil United
Kingdom

European
Union

IndiaChina JapanAustralia United
States

90th percentile 10th percentile

Median

2011 2019 2011 2019 2011 2019 2011 2019 2011 2019 2011 2019 2011 2019 2011 2019 2011 2019

Exhibit 11

!All deposit–taking institutions with available data for 2009–18 (n = 595). ROE based on average net income and average equity including goodwill for 2009–18.
Source: McKinsey Strategy Practice and Corporate Performance Analytics

44% of banks

Global banks return on equity (ROE) – cost of equity (COE) spread 2009–18, number of banks (n=595!)

Bank ROE < COE Bank ROE > COE

A majority of banks globally may not be economically viable

Exhibit 10

0% to 1% 1% to 2% 2% to 3% 3% to 4% >4%

56% of banks
105

<-4% –4% to –3% –3% to –2% –2% to –1% –1% to 0%

46

56

65
59

45
50

41

31

97

17 The last pit stop? Time for bold late-cycle moves

Exhibit 12

Within speci!c markets, scale is important but not deterministic – if you can su”ciently
di#erentiate

Bank’s return on tangible equity! (ROTE),”
2017–18, %

Dispersion of ROTE,
2017–18, %

On average, scale helps outperformance but…

…small banks can also outperform by focusing on niche markets

!Based on a sample of ~100 largest banks in terms of assets, with consistent reporting over timeline. Sample of ~100 largest banks used to account for scale in the free cash
“ow (FCF) generation in dollar terms to avoid skew from small banks. Results on FCF to tangible equity hold if we use the full bank data set.
Source: SNL Market Intelligence, ~1000 top banks by asset size

>10%
(n = 80)

1–10%
(n = 159)

0.1–1%
(n = 207)

<0.1% (n = 554)

High

Low 0 10 20 30

13.1

10.4

8.2

8.6

Single bank Average

of the industry’s returns have fallen by approximately 150 bps
in the past five years, those with more than a ten percent share
of the national or regional market have managed to improve
returns by ten bps during the same period (See sidebar, “It’s
‘in-market’ scale that matters”). The scale effect is most
pronounced in Asia and Latin America, where market leaders
enjoy approximately 400 bps and 430 bps of ROTE premium to
average banking returns, respectively.

There are exceptions to the rule. In Europe, for example, there
is a C-curve—banks with an in-country market share of either
more than ten percent or less than one percent have ROTEs
double that of their peers caught in the middle. Across markets,
banks serving a niche segment with a specialized product suite
and superior customer service have managed to sustain high
return premiums through the cycle irrespective of size. More
precisely, 17 percent of the smallest banks outperform the
average ROTE of the largest banks. Small outperformers fall
into one of three categories: (i) community banks with strong
ties to the region and tailored localized services, (ii) specialty
banks focusing on niche products and services, and (iii) private
banks. Specialization and focus have been the basis of their
superior performance, as they have been able to keep pre-risk
margins flat over the previous five years, even as margins have

shrunk by 240 bps for banks globally.

Rigorous specialization and focus pay off. The weakest
performers are those banks that are stuck in the middle with
returns that are below cost of capital and dwindling. For these
banks, a late cycle presents structural warning signals to
either gain scale or fundamentally reshape their portfolios and
operating models to deliver distinctive value within a carefully
defined market segment (Exhibit 12). And finally, there are
those with scale deriving relatively higher returns than peers
but still struggling to make healthy absolute returns. For these
companies, for example, securities companies and broker
dealers, it’s a call for a fundamental reinvention of the business
model. Scale alone won’t help.

Going forward, scale will likely matter even more as banks head
into an arms race on technology, especially given that most
new IT investments, be it for a new technology (for example,
blockchain) or a digital build, tend to be absolute in nature
and, therefore, much cheaper over a higher asset or revenue
base. The effect of scale on ROE has been reflected primarily
in a cost advantage—the ability to bring marginal costs down
as an organization gains operating leverage with consistent
increase in size.

18The last pit stop? Time for bold late-cycle moves

It’s “in-market” scale that matters

Size in absolute terms s not a reliable metric in banking. Our analysis shows that it is not total size across the diverse markets
a bank serves that enables superior performance but optimal scale within a given geography, that is, a local, regional, or
national market. We looked at the 15 largest banks that have a global footprint and found that the relationship between scale
and returns does not hold for this group (

Exhibit 13

). While the average ROTE for the 15 largest banks with global operations
is 30 bps higher than average banking returns, banks that have more than twice the assets of others do not necessarily
have higher ROTEs. Interestingly, our analysis of the local market shares of a large global bank operating across several
countries shows that the relationship between domestic market share and outperformance resonates strongly even when
the relationship is weak at a global level (Exhibit 13).

The relationship is also strong at sub-regional or niche levels: A market need not necessarily be an entire population in
a country; it can be a homogeneous subset of customers in a region. For instance, in the US, First Hawaiian Bank has
approximately 0.1 percent market share in the US but, as Hawaii’s largest financial institution, generated a ROTE of
14.6 percent in 2017/18. This compares to the average ROTE of 13.7 percent for US banking over the same period. Similarly,
in China, Bank of Gansu has approximately 0.1 percent market share in China, but as the second-largest bank in Gansu
generated a ROTE of 16.8 percent in 2017/18, compared to an average of 13.7 percent for all banks in China.1

1 The scale effect also holds within China, where most banks are state backed. Hence Gansu’s outperformance can be partly attributed to its regional scale rather
than it being state backed.

In-country scale, rather than global scale, helps lift returns in retail and
commercial banking
There is no statistically signi!cant relationship between size
and ROTE for the top 15 global banks by assets, but …

… broken down by scale in individual markets,
the correlation with market share stands out

SOURCE: SNL Market Intelligence, 15 top global banks by asset size; WBI tool & McKinsey analysis

ROTE for the top 15 global banks and assets Correlation of bank returns and market share

High

ROTE,
%

Low

Total assets
$ trillion

Low HighGlobal bank’s retail
banking market share

Low
High

Global
bank’s
ROTE

premium

Negative

Positive

Country A

Country B

Country C

Country D

Country F

Country E

Circle size
indicates
total assets

Exhibit

13

19 The last pit stop? Time for bold late-cycle moves

The varying degrees of scale impact by geography

While we found that in-country scale is a significant factor in generating higher ROTEs, the impact of scale varies in
magnitude across geographies. To understand this variance, we analyzed the relationship between C/A ratio and in-country
market share for banks worldwide.1 As noted above, we found larger banks to be generally more cost-efficient. For example,
tripling a bank’s market share typically reduces its C/A ratio by 25 bps. However, the impact of scale varies widely by country
(

Exhibit 14

). We highlight below a few examples:

Digital advanced markets. This includes markets like Australia, Sweden, and Denmark, where banking is rapidly moving
online and where the scale impact is pronounced. In Sweden, for example, the top three banks by market share have a C/A
ratio of approximately 77 basis points, while the C/A ratio of the bottom quintile exceeds 340 bps. This gap points to the
increasingly transformative impact of technology on banking.

Highly fragmented markets. These include markets like Russia, Germany, and the US, where despite highly fragmented
markets, we see strikingly different impacts of scale. In Russia, despite the central bank’s efforts, its banking system is still
highly fragmented, with more than 500 banks. At 200 bps, the average C/A ratio for the top three banks is less than a half
that of the lowest quintile (430 bps). By contrast, in the US, another highly fragmented market, the gap between the bottom
quartile and top three is only 71 bps.

Emerging Asian markets. In China and India, cost efficiency is associated with scale, but to a very different extent. In
China’s banking sector, which is dominated by many corporate banks holding large balance sheets, the average C/A ratio
for the top three banks by market share is 84 bps, which is half that of the average for the lowest quintile (169 bps). In India,
by contrast, while some scale effect is visible, even the largest banks have a C/A ratio higher than 200 bps. Indian banks
typically have a higher cost base, in part because many maintain large physical net-works to serve rural customers.

1 We have chosen costs relative to assets as the primary measure of productivity given its pronounced impact on returns; other metrics, such as risk cost, revenue
margin, and leverage, did not yield such a strong statistical relationship.

The impact of scale on cost synergies is clear across geographies, though the degree of e!ect
varies by market; highest in markets with high level of digitization
Average cost-to-assets ratio by country,by market share quintile–2016–18,
basis points

Source: S&P Global Market Intelligence, McKinsey Panorama

Lowest quintile Top 3 banks

307 269
71

85

166

205

1

86

230

DenmarkAustralia

Digitally advanced Highly fragmented

Emerging Asia

Sweden United
States

Germany Russia IndiaChina

356

49

273

107

346

77

275

204

339

134

430

200

404

218

169

84

Exhibit 14

20The last pit stop? Time for bold late-cycle moves

However, we expect to see even greater benefits of scale from
a margin advantage as digital scale really kicks in, including
the network effects of mass platforms offering peer-to-peer
payments and lending, as well as other scale-led propositions.
In fact, ecosystem plays to deliver scale beyond conventional
banking market share is one option that we strongly recommend
for a set of banks in Chapter 3.

While global banking has enjoyed a prolonged period of growth,
the sector still finds itself in a tenuous position, with growth
slowing, productivity gains fading, and digital pressures on the
rise. Nearly 60 percent of banks still generate returns below their
cost of equity. The glass-half-empty view suggests that most
banks have missed out on the opportunity to restructure and
reprioritize in this cycle. Optimists, however, think there’s still time
for banks to find new ways to strengthen profitability and boost
returns. In either view, the call to reinvent or scale is imminent.
Chapter 2 focuses on critical moves banks should consider to
increase revenues and reduce costs while ensuring that risk and
capital are managed efficiently, as well as inorganic options that
could help strengthen their hand.

21 The last pit stop? Time for bold late-cycle moves

“If you look at the American economy,
the consumer is in good shape, balance
sheets are in good shape, people are
going back to the workforce, companies
have plenty of capital, it could go on for
years, there’s no law that says it has to
stop … We do make lists and look at all
the other things: geopolitical issues, lower
liquidity. There may be a con!uence of
events that somehow causes a recession,
but it may not be in 2019, 2020, 2021.”
Jamie Dimon, CEO of JPMorgan Chase, analyst call, April 2019

22The last pit stop? Time for bold late-cycle moves

Time for bold
moves: Levers
to improve
performance in
the late cycle

Introduction
Sticking with the optimist view, one could argue that there is
still time for banks to act decisively in a late cycle before we
hit a recession. After all, idiosyncratic performance continues
to explain a large part of a bank’s economic returns—be it
through levers to achieve scale or to truly differentiate itself in
the markets it serves. As growth slows and average banking
valuations decline, pulling these levers becomes ever more
crucial. But how can executive leadership take advantage of
the late cycle to get a jump on the beginning of the next cycle?
The key is to prioritize and deliver on bold and critical moves.
Building on the insights and opportunities outlined in Chapter 1,
this chapter focuses on a few material interventions that banks
should consider—those than can be executed within the short
span of two to three years that a late cycle typically offers. In
Chapter 3, we discuss additional levers that banks may exercise
depending on the overall condition of both the bank and
its market.

There are three organic levers that we suggest banks explore:

(1) risk management based on powerful analytical tools to
prepare for a downturn; (2) productivity, using modular utilities
to materially change cost structures; and (3) revenue growth
through an improved customer experience (CX), bringing

23 The last pit stop? Time for bold late-cycle moves

a larger customer base and/or share of wallet.1 Essential
toexploiting these profitability levers are the critical enablers of
advanced data analytics and talent. It is important to note that
while these levers are interdependent, our discussion of each
lever focuses on the dimension where it will typically have the
greatest impact. If banks develop the capabilities required to
exercise these levers successfully, they will not only be able to
weather the downturn but also to build for the future.

Banks should also consider their options for building scale or
competence through inorganic levers, including both mergers
and acquisitions (M&A) as well as partnerships. Of course, the
choice of inorganic levers depends on a bank’s competitive
position and the conditions of the markets served. The wide
spread in valuations within each market provides healthy
ground for building scale through M&A. While banks generally
tend to be reactive in their inorganic pursuits (most of which
peak in a downturn), carefully chosen proactive investments
have the potential to strengthen a bank’s performance into the
next cycle (

Exhibit 15

).

Risk management: Building resilience for new-
age risks
With most indicators showing that we are in the late cycle, it is
time for banks to ramp up planning for resilience, determining
how to manage risk and protect returns in a downturn.

1 We focus on profitability levers, as the levers on capital optimization are highly dependent on the country’s regulatory system.
2 For instance, limitations on liquidity coverage ratio or net stable funding ratio.

Compared to previous waves, banks have relatively lower
levels of profitability and have experienced much more muted
performance even in the expansionary phase. While banks
remain exposed to the same traditional types of risk as in the
past—especially in pockets of corporate lending in certain
emerging markets and North America—certain risks have
changed because of evolving operating models and diverse
market circumstances.

Consider funding and liquidity risks. While banks have
materially increased their capital and liquidity levels because of
new regulations2 following the global financial crisis, there are
less well-identified liquidity risks that deserve scrutiny. These
new liquidity risks derive from the changes that have occurred
to the banking and payments systems and include, for instance,
the risk of faster withdrawals using online applications. These
risks are further amplified by the potential of social media
to spread negative reports, true or otherwise. For example,
a European challenger bank recently suffered from a viral
message that incorrectly cast doubt on its liquidity. This
contributed to a fall of approximately ten percent in share price
before recovery. There is also market risk across certain traded
asset classes where a mix of central bank purchasing and the
end of proprietary trading by banks have resulted in limited
liquidity that could test asset prices in a downturn.

What should banks do to build resilience? Resilient

35%
Machine-learning models can improve predictive
accuracy in identifying the riskiest potential
customers by

24The last pit stop? Time for bold late-cycle moves 24The last pit stop? Time for bold late-cycle moves

Banks can use advanced analytics and machine-learning models to preempt risk; new models
potentially identify 66 percent of charge-o!s within the riskiest 10 percent of accounts
Cumulative goods-bads curve for machine–learning model

Gini value, %

Comparison of current and new model

!Gini value (a measure of predictive power, a perfect crystal ball would be 100%). “Area Under Curve. #Kolmogorov-Smirnov statistics.

Low

Total
charge-
o!s, %

Low
High

Account risk, %High

0
0

100
100

Gini! 0.86
AUC” 0.93
KS# 0.70

Machine-
learning-
based scoring
identi$es 66%
of charge-o%s
within riskiest
10% of
accounts

Existing credit
risk models

Machine-
learning-
based scoring
identi$es 15%
of charge-o%s
within riskiest
1% of
accounts

50

Machine–
learning model

86

Exhibit 16

• Transfer non-di!erentiating activities
to 3rd–party utilities

• Radically restructure cost base using
zero-based budgeting

• Materially improve customer
experience

• Develop or tap into ecosystems
• Invest materially in innovation

• Manage risk with a ‘leader’s mind–set,’ advanced analytics, and scenario planning

Bold moves for the late cycle

ORGANIC

INORGANIC

Note: Universal levers addressed in Chapter 2. Archetype-speci”c levers addressed in Chapter 3.
Source: S&P Global Market Intelligence, McKinsey Panorama

Reduce cost Increase revenue

Build resilience

Enablers
Rapidly scale data

and advanced
analytics.

Hire and manage
talent for digital

enterprise

• M&A to develop scale or acquire competency
in any of the axes above

• Partnerships to leverage the same thesis but
in form of joint venture

Exhibit 15

25 The last pit stop? Time for bold late-cycle moves

organizations have invested in tools and governance
mechanisms that allow them to discuss possible deteriorations
in the market and implications for the bank, while welcoming
alternative views from the mainstream ones. To do this, a good
first step is to create “nerve centers” that comprise cross-
functional teams to ensure collaboration across business
lines, risk management, and operations, thereby increasing
transparency and speeding up decision-making during a
downturn. Nerve centers not only need a strong leadership
mindset but also require superior tools to make effective
unbiased decisions that can be executed swiftly, especially
as fast response will be of the essence in an increasingly
connected and transparent system.

The second lesson is that banks must invest in advanced
analytics and artificial intelligence capabilities to support
early alerts, enabling banks to monitor emerging trends and
distress. A potential case is shown in Exhibit 16, where a
machine-learning model can identify the riskiest potential
customers with an increase of 35 percentage points in the
Gini value (measure of predictive accuracy) compared to
traditional models. Banks can also improve their debt collection
programs by using AA to help identify accounts with high
value-at-risk, segmenting outcomes, and developing digital
tools for collections (that is, an orchestrated provision and use
of multiple digital channels to both alert customers of their

delinquency and provide channels through which they can
manage and resolve their debt).

The third key to resilience is to prepare for risks with scenario
planning and reinforced infrastructure in security and
communications (especially, for example, social media tools).
Regulators have increased stress testing for tail risks in
recent years and banks should use lessons from those tests
to increase resilience. Banks should consider their action
sets with respect to: (i) balance-sheet preservation, including
liquidity and capital buffers, (ii) preservation of through-the-
cycle profit generation, and (iii) strategic positioning. This will
allow them to mitigate losses, protect liquidity, secure long-
term funding sources through strengthened relationships, and
even consider inorganic opportunities (both portfolio sales
as well as acquisitions) that tend to present themselves in
downturns.

Finally, banks have learned from the previous cycle that socio-
political scrutiny peaks during a downturn, particularly as
scrutiny of organizations according to environmental, social,
and governance (ESG) criteria continues to grow. Now is the
time for banks to diagnose any potential ESG risks so they
can deal with them before a downturn, and to effectively
communicate their ESG strategy, including through social
media, with stakeholders.

Most banks have failed to deliver signi!cant bottom-line impacts in recent years, despite major
cost programs
Banks with given change in cost-to-income ratio (C/I),! 2010-15

!Based on a sample of 417 banks whose operational expenditure in 2010 or 2015 was >$100 million and assets >$10 billion (n = 417).
Source: SNL Financial

Reduced ratio Increased ratioStagnant ratio

11

<-20 -15 to -20 -10 to -15 -5 to -10 -5 to 0 0 to 5 5 to 10 10 to 15 15 to 20 >20

6 21

63

112

102

58

20
10 14

Exhibit 17

26The last pit stop? Time for bold late-cycle moves

Productivity: Shifting non-differentiating
activities to industry utilities
The impact from traditional cost-cutting is rapidly diminishing.
Even with years of cost-cutting following the financial crisis,
most banks have not made the material improvements in
productivity needed to compete effectively with fintechs,
neo banks, and digital giants, which operate at nearly half
the marginal costs of traditional banks (Exhibits 7 and 17).
What is more, the gains generated by most banks’ ongoing
cost-efficiency efforts are shrinking (Exhibit 2), and all banks,
regardless of their performance and the conditions of the
markets served, must re-evaluate the impact of productivity on
the ability to compete and win in a late-cycle world.

Among the near-term moves available to all banks, the use
of third-party “utilities” to handle non-competitive and non-
differentiating functions has the potential to produce the
swiftest and most radical reduction in costs. We estimate that
less than half of bank costs (vested mainly in IT and support
functions as well as some operations) is directed towards
activities that do not differentiate an organization from
competitors (Exhibit 18) and could potentially be outsourced
to multi-tenant utilities.3 The remaining expenses keep the
machine running in compliance with regulatory demands but do
not add value. For an industry centered on lending, the reliance
on external credit bureaus for credit scoring suggests that here
are more banks can do to industrialize the cost base.

The experience of German automotive manufacturers in the
1990s holds a powerful lesson in outsourcing modules of non-
differentiating activities to common utilities with the volume
to reap scale advantages (see sidebar “The auto industry in
the 1990s: Lessons for banks in 2019”). We have already seen
examples of banks choosing to build their platforms with third-
party vendors such as Mambu, Thought Machine, and 10X,
and now is the time to push on to other areas of the cost base.

3 This can be through more traditional managed service outsourcing or carve outs
and partnerships. We refer to these three options when discussing outsourcing in
the utilities section.

27 The last pit stop? Time for bold late-cycle moves

Outsourcing a large portion of these activities could create
benefits of 200 to 400 bps in an average bank’s cost-to-
income (C/I) ratio. For example, by moving trading processing
volumes for capital markets players to multi-tenant at-scale
utilities, the industry can bring down operations spending
by approximately 20 percent. Another potential area for
industrialization is regulatory and compliance functions such as
know your customer and anti-money laundering, which typically
represent between 7 and 12 percent of costs. These processes
are critical for banks yet entirely non-differentiating.

200 to
400 bps

Outsourcing non-differentiated activities could
improve banks’ C/I ratios by

By transferring non-di!erentiating activities to modular industry utilities, banks could
potentially improve return on equity by 60 to 100 basis points
Share of total operating cost, typical universal incumbent bank example, %

Typical potential
for utilities

Front o!ce/
distribution

Middle o!ce/
operations

IT Support
functions

“Includes product management, advertising/promotions, market research, CRM activities, etc. #Includes share of collections. $Other costs, includes depreciation and
amortization.
Source: McKinsey Cost Tool Box Benchmarking and Finalta Retail Cost Benchmark

Retail branches
20–30

Retail

operations#

6-9

CIB
operations#

8-10

Products and
marketing”

2-7

Risk
3-5

Finance
2-4

HR

1-3

Legal

0.5-1

0.5-1

Application
development/

maintenance
9-12

Infrastructure
6-8

General
management

and other
costs
4-6

Administration/
overhead

1-3

CIB Front O!ce
10–12

Contact

centers 2-3

ATM
0.5-1

Other$
0.5-2 Internal audit

Low Moderate High

Online/digital
0.5-2

Criteria for assessing
outsourcing cost
opportunity:

• Is it a clearly de%ned
utility?

• Is it a competitive
activity?

• Is the potential cost
saving material?

• Can it be separated
from the bank?
(e.g., technology,
regulatory
constraints)

• Are there su!cient
suppliers that can
pick up the module?

Exhibit 18

As an example, banks can strip out non-di&erentiated regulatory and compliance cost modules to more e!cient regulatory
backed utility; savings in here could yield 60 to 100 bps improvement in return on equity across the average bank.

28The last pit stop? Time for bold late-cycle moves

Further, in countries or functions where third-party providers
do not exist, a common, regulatory-backed utility could be
created with funding from either private or public capital.
Alternatively, specific banks with scale could also provide
services within ring-fenced entities. Industry regulators and
governments may support such a plan, as it ultimately aims
for better customer service and harmonization of customer
security across the banking system. In markets struggling with
growth and returns, we believe that the creation of industry
utilities delivering best-practice services at scale for diverse
bank departments, from the front office (for example, the Dutch
yellow ATM network, the P27 Nordic payments platform) to the
middle and back offices (for example, reconciliation, regulatory
compliance), may be the one of the most powerful keys for
unlocking value. Shifting the costs of non-differentiating
activities to third-party “utilities” could potentially improve
ROTEs by more than 100 bps.

29 The last pit stop? Time for bold late-cycle moves

The auto industry in the 1990s: Lessons for banks in 2019
During the downturn of the 1990s, the German automotive industry faced stagnation in global demand, a high “historical”
cost structure, and margin pressure from competitors (in this case, Japanese car makers) with low-cost operating models.
Sound familiar? These are precisely the late-cycle trends that we have described for a large swathe of global banking. How
did the German automotive industry respond? It replaced incremental innovation with fundamental transformation through
a combination of zero-based budgeting (ZBB) and modularization. A winning theme was seamlessly integrating suppliers in
the value chain to pivot from a one-off approach to vendor usage, that is, modularization.

Manufacturers achieved modularization by identifying siloed activities that did not add competitive value and could thus
be outsourced to an industry-standard utility. As German automobile manufacturers deconstructed their value chains,
they discovered several non-differentiated modules of activity, such as car brakes and electronics, and outsourced these
to experts with economies of scale. To this day, these components are provided by two or three major suppliers. The
change from a platform strategy to a modular one was a huge part of the industry’s successful transformation, reducing the
operating cost base by 20 percent and improving productivity via a 30-percent decrease in throughput time
(

Exhibit 19

).

From common chassis strategy in 1990…

Impact of transformation

… to modular strategy in 2010

Source: McKinsey

1990 Unclear, broad, cost based 2010 Clearly de!ned cost base

A00 A0 A B C D E A00 A0 A B C D E

Body
type

Body
type

Length segmentLength segment

Body Common chassis 5050

reduction in non-full-time
equivalent (FTE) costs20% reduction inFTE costs15% reduction inthroughput time30%

Automotive manufacturs moved from a platform strategy to a modular tool–kit strategy,
signi!cantly improving productivity in face of economic challenges

Exhibit 19

30The last pit stop? Time for bold late-cycle moves

Revenue growth: Improve customer experience
to deepen relationships
Simply put, when customers are more satisfied, they are
more engaged and generate more value for banks. And as
customers face increasing economic challenges—as typically
happens late in the cycle—they tend to shop around even
more, which brings customer experience front and center to
a bank’s strategic priorities. Cross-functional design teams
use recent innovations in data analytics to transform customer
relationship management from a high-level concept into
a tactical tool to make precise improvements to customer
journeys, focusing on decision points that provide the basis
for distinction. The redesign of select journeys can produce
results (including increased revenue and lower rates of churn)
within 12 to 18 months, making CX an attractive lever as banks
head into the late cycle. Although the benefits of CX are far-
reaching, including growth, productivity, and risk, we focus
on its potential for revenue growth, as this is where it has the
most material impact. Further, improving CX for growth is not

necessarily about being best-in-class, but rather closing the
gap from “very unhappy” (the point where customers tend to
switch banks) to “good enough.” For instance, customers will
switch if they’ve had a very poor experience, but they will not
necessarily buy more even if they are highly satisfied. Tactical
approaches to enhancing CX, therefore, can benefit all banks—
market leaders can use it to widen their competitive advantage,
while challenged banks can use it to stop customer outflows.

For banks to improve their products and create a deeper
customer relationship, they must be tactical and do it journey
by journey, product by product, and customer by customer.
Simply trying to move customers as a block from “unhappy” to
“okay” to “great” usually fails to lift revenues, due to diminishing
returns. With finite CX resources, banks need to make sure they
are prioritizing the right ones. For instance, in our research,
roughly ten percent of customers who rank their overall
mortgage experience a four out of ten or less are approximately
seven times as likely to refinance elsewhere as customers who
give a five or higher (

Exhibit 20

).

The least satis!ed customers are far more likely to re!nance their mortgage with another
provider
Customers who intend to re!nance mortgage with another bank, by satisfaction rate, %

Source: 2018 McKinsey Journey Pulse Benchmarks

101 2 3 4 5 6 7 8 9
0

10
20
30
40
50
60

Average
re!nancing
rate of
customers with
satisfaction of
5 or more

Customers who intend
to re!nance mortgage

with another bank

HighCustomer satisfaction rateLow

Average
re!nancing
rate of
customers with
satisfaction of
4 or less

40%

6%
Exhibit 20

31 The last pit stop? Time for bold late-cycle moves

Banks should use a value-oriented and analytically informed
approach to choose the right CX levers:

— Integrate the “customer voice” with both operational
and financial data to generate greater insight. A bank
can use data from interactions across multiple channels
(for example, digital footprints, other channel interactions,
product usage, cost-to-serve, revenue data) and use
advanced analytical models to connect the dots and build a
detailed story of actual customer journeys.

— Analyze data for two kinds of insight. First, identify
where changes in experience result in changes in customer
behavior that increase value. Second, identify the “break
points” where a particular element of the experience has a
disproportionate effect on the overall outcome.

— Set up a strong design capability to ensure the insights
are translated into action and to develop new customer
journeys as efficiently as possible.

— Set up strong frontline execution capabilities to make
those designs a reality. For instance, in our research we
found that customers wish to be taught how to make the
most out of digital channels, and one of the best ways to do
that is by investing in branch and contact center capabilities
and repurposing tellers for universal roles to match demand
efficiently.

Enablers: Advanced data analytics and talent
Advanced data analytics: The case for prioritizing value
realization
AA tools are enabling superior performance in organizations
willing to make the proper commitment. Across all industries,
companies that are more analytically driven grow three times
faster than their less analytical competitors.4 Banking, with
access to a broad set of valuable data, starts from a strong
position, but has yet to realize the full potential from embedding
analytics deep into its culture, decision processes, and
business operations.

Unsurprisingly, many of the levers we described earlier depend
on a strong set of AA capabilities. What then can banks do to
strengthen their analytics capabilities, accelerate performance,
and support chosen priorities in the next few years that a late
cycle offers?

The answer is two-fold: (i) fully leverage AA end-to-end by
balancing an enterprise-wide and business-unit-led approach,
and (ii) ensure that advanced diagnostic capabilities are applied
in a consistent way and value is extracted quickly from high-

4 Carlos Fernandez Naveira et al., “Smarter analytics for banks,” McKinsey.com, Sept 2018, https://www.mckinsey.com/industries/financial-services/our-insights/
smarter-analytics-for-banks

5 Ignacio Crespo et al., “Using data to unlock the potential of an SME and mid-corporate franchise,” McKinsey.com, October 2018, https://www.mckinsey.com/industries/
financial-services/our-insights/using-data-to-unlock-the-potential-of-an-sme-and-midcorporate-franchise

priority use cases.

Set up to fully leverage advanced analytics. To build a
successful AA program that delivers a return within one to
three years, top executives should define the guiding vision
for the bank’s data transformation and work with business unit
executives to develop a detailed roadmap for turning the vision
into reality. To develop the roadmap, these executives should
identify and prioritize customer journeys and internal use cases
that have the greatest impact, either through cost reduction
or increased revenue (Exhibit 21). The proper use of data and
analytics can potentially increase a bank’s cost advantage by
ten percent and improve C/I ratios by up to fifteen percent
(even in a recession). On the revenue side, for example, the
implementation of recommendation engines identifying
emerging needs among SMEs has boosted revenue for some
banks by between 20 and 30 percent.5

Scale capability building to quickly realize value. How can
banks rapidly scale AA? First, capability building should be tied
to value creation, and banks must prioritize the most valuable
levers and the roles most critical for driving those levers.
Banks should develop a community where colleagues—across
all parts and levels (including the CEO)—learn, engage, and
share best practices. Learning content must be customized to
reflect day-to-day use cases, with emphasis on high-impact
business problems to make learnings stick through iterative
cycles of classroom learning and real-world application. Finally,
leaders should rigorously track results from capability building
initiatives using “hard metrics” as well as qualitative dimensions,
linked to business impact across the entire organization.

Talent: The need to win the digital battle
To successfully build their data and AA capabilities, banks need
to close the talent gap between their current capabilities and
those needed for a predominantly digital enterprise. This poses
two fundamental challenges.

Finding talent. Many banks currently find themselves with
a significant skills gap widened by two factors. First, digital
innovation entails a new balance of skills relative to those banks
have traditionally hired for, with diminished reliance on basic
cognitive skills and higher demand for socioemotional and
technological skills. Second, banks’ perceived employee value
proposition lags those of technology and other leading sectors,
which are also competing for that same talent (

Exhibit 22

).

Should the late cycle also present a softening in the tech cycle,
banks should prioritize hiring as a way to position themselves
for success in the next cycle. Further, banks should develop

32The last pit stop? Time for bold late-cycle moves 32The last pit stop? Time for bold late-cycle moves

In the short term, it is important to outpace competitors in revenue-boosting advanced analytics

Example use cases for using advanced analytics and potential impact

Source: McKinsey analysis of publicly available data

High–feasibility use cases

Prioritize rapid implementation of microsegmentation cases to avoid
competitors gaining market share.

Implement pricing and call–center optimization use cases in parallel
because of the strong track record of impact in other organizations.

In the next three years, advanced microsegmentation
will give fast-movers a disproportionate advantage.

Advanced analytics in pricing and call centers is less
urgent but has been proven to have low risk, high
feasibility, and high impact.

What banks should do What success looks like

Urgent use cases

economic
value
added

+30%

Micro-
segmentation

bad–debt
provisions

–20%

Collections

churn
rate

–20%

Churn

fraud
detection

+15%

Fraud

gross
nonperforming–

loan in!ow

–25%

Advanced early-
warning systems

call-center
productivity

+40%

Smart
allocation

basis points
to deposit

margin

+20

1-to-1
pricing

Exhibit 21

Banks need to work hard to close the digital–skills gap; technology has overtaken banking in
perceived attractiveness of compensation and bene!ts
Employee sentiment about compensation and bene!ts

!Nine largest US banks by assets, 2017. “Ten largest US tech #rms by revenue, 2017.
Source: 2018 McKinsey Journey Pulse Benchmarks

Employee
sentiment

Lower

Higher

2014 2018
3.5

3.6

3.7

3.8

3.9

4.0

Banking!

Technology”

–1.8%

+3.6%

Exhibit 22

33 The last pit stop? Time for bold late-cycle moves

reskilling programs to close the gaps in digital talent through
their existing work force. We estimate that one-third of existing
talent gaps can be addressed by reskilling current employees.
Reskilling has economic and social considerations. Eighty
percent of CEOs at a recent World Economic Forum meeting
pledged that as they adopted AI they would retain and retrain
existing staff in 2016.

Managing talent. Banks will need to become faster and
flatter to retain the best talent. With the introduction of agile
teams, banks must become faster to achieve weekly (versus
monthly or annual) digital releases. Not only does this mean
that banks will be able to move at the same speed or faster
than the competition, but they can also take greater risks with
certain projects, as the consequences of failure on smallscale
releases are manageable. To enable these faster digital
releases, organizations will need to become flatter. This means
that they must give more power over decisions to digital
talent to pursue these projects at speed without current time-
consuming burdens of bureaucracy and build non-hierarchical
multidisciplinary teams that are co-located. Banks need to shift
from the traditional front- and back-office segmentations to
multidisciplinary teams working together on well-defined tasks.

Beyond organic options: Mergers, acquisitions,
and alliances
Given the competitive advantages that come with scale,
many banks are finding that mergers and acquisitions—along
with strategic partnerships—are an efficient way to achieve
their scale ambitions or a means to completely reinvent their
business models. Indeed, the ground for mergers, acquisitions,
and partnerships is fertile, as the current environment provides
a favorable combination of capital, regulation, and senior-level
interest. First, there is a large dispersion in valuations and
capital levels across the banking system, creating an ideal
environment for inorganic moves (

Exhibit 23

).

Second, with systemic risk in banking largely mitigated through
capital and liquidity build-ups since the global financial crisis,

and fragmented banking sectors in many markets struggling
to produce returns (

Exhibit 24

), regulators are more likely
to be supportive of consolidation. Third, the need for large-
scale investments in technological transformation, combined
with weak organic growth, is pushing M&A up on the board
agenda. Banks, however, should be careful as they assess
these options, as very few deals have historically created value.
Building capabilities in partnerships, joint ventures, and M&A is
also essential to maximizing their full potential.

Options to consider. Banks have two options in terms of
overall objective and inorganic strategy. The first option is to
choose partners/targets that afford them scale. As an example,
two challenged mid-market banks serving the same client
base in the same geography in a challenged market could
consider merging to gain scale. Our analysis shows in-market
mergers with overlapping footprints could result in savings
of up to 20 percent of the combined cost base in addition to
revenue synergies. The ability of a combined entity to then fund
differentiating innovation rises significantly as well. A recent
announcement from the SABB!Alawwal merger in Saudi Arabia
guided to cost synergies of 15 to 20 percent of the combined
cost base and revenue synergies of two to three percent. It’s
hard for banks to realize value to the same quantum organically
in a short period of time.

The second option for banks is to merge across capability
vectors to complement existing assets and help reinvent their
business model. For example, a bank with a strong customer
franchise could merge with a digital bank with the primary
objective of enhancing operational efficiency (and perhaps
realizing a secondary goal of increasing access to customer
segments beyond its traditional footprint). This playbook
equally applies to large banks that are flush with capital from
their superior returns from the last cycle; inorganic options
should be considered to acquire and supplement technology,
skill sets, and customer groups. A large fintech ecosystem that
has been created in this cycle offers plenty of targets in a late
cycle; now’s the time for banks to sharpen their shopping lists

79%
of leading banks have partnered with a fintech

34The last pit stop? Time for bold late-cycle moves

as late-cycle valuations become attractive. Lastly, acquisitions
can be a critical tool for reinvention where core business
models are getting disrupted. For players looking at extending
their service proposition beyond their traditional capabilities
(which are being disrupted), the fastest way to get there is with
tactical acquisitions. The securities value chain, from asset
managers to broker dealers to trust banks, is a classic example.
With core margins coming down across each business model
primarily driven off lower asset management margins, players
in each part of the value chain are being forced to ask the
fundamental question, What drives value for their customer?
Which in turn leads to deliberate choices of where to give away
value and where to extract it. And in those new choices of
offering value, acquisitions can be a tool to build capabilities
faster. We’ve already seen such plays in the securities
industry—for example, State Street acquiring Charles River to
boost front-office capabilities to service asset managers even
as back- and middle-office margins get competed away. Late-
cycle pressures are likely to extend such moves across the
securities value chain as players are forced to reinvent.

Further, banks must determine the optimal collaboration
model—partner or merge/acquire? This choice should be

6 Based on a research covering publicly announced partnerships of the top 100 banks and other digitally advanced banks, from McKinsey Panorama Fintech.
7 Cristina Ferrer et al., “M&A as competitive advantage,” McKinsey.com, August 2013, https://www.mckinsey.com/business-functions/strategy-and-corporate-finance/

our-insights/m-and-a-as-competitive-advantage

informed by the scope and depth with which a bank can pursue
growth opportunities, as well as its existing capabilities,
customer segments served, and the capital available for
investment.

— Partnerships: For banks that choose to form partnerships,
success will depend on establishing clear terms for
combining resources and sharing returns equitably. The
process for managing partnerships extends to collaboration
in the formation of platform-based marketing ecosystems.
Our research shows that 79 percent of leading banks have
partnered with a fintech to foster innovation in payments,
lending, investment, or other areas.6

— M&A: For M&A banks must establish a rigorous process for
screening potential acquisition targets and prioritizing them
according to their relevance to clearly articulated strategic
objectives. It is critical to adopt a portfolio approach,
coupled with scrutiny, giving a single team responsibility
for M&A.7 Regardless of the structure chosen, significant
research and planning is needed as the compatibility of
operating models, cultures, technology, and customer
relationships are critical to success and value capture.

The current diversion in capital levels and valuations creates fertile ground for M&A within
markets

!Financials of the largest global banks ( n = ~1000). “Publicly listed largest banks ( n = 504).
Source: SNL

High

Price
to book

(P/B)
ratio

Low
High

Capital level, equity/assetsLow

Capital levels (tangible equity/total assets),! 2013–18, % Correlation between valuation and capital level!

Bottom 25%

Top 25%

Median

2013 20152014 2016 2017 2018 0

2
4
6

10 20 30
4

5
6
7
8
Exhibit 23

35 The last pit stop? Time for bold late-cycle moves

Fragmentation in market sets the stage for the potential of bank M&A

Market share of top 5 banks in each geography, by assets, %

Source: PFIC- World Banking Intelligence

Largest 2nd 3rd 4th 5th Others

Top 3
market
share, %

Developed markets

Top 5
market
share, %

United States 10 9 9 5 3 64 29 36

27 14 6 4 4 44Germany 47 56

23 23 16 13 10 15Canada 62 85

20 19 17 15 9 20United Kingdom 56 80

19 16 13 12 4 35Japan 48 65

18 14 13 12 9 34France 44 66

18 13 11 8 4 46Switzerland 42 54

16 9 3 3 2 68Italy 28 32

Largest 2nd 3rd 4th 5th OthersEmerging markets

22 15 13 12 8 31Mexico 50 69

19 6 5 5 5 59India 31 41

16 16 10 10 4 45Indonesia 42 55

10 9 8 8 4 61China 27 39

Exhibit 24

36The last pit stop? Time for bold late-cycle moves

37 The last pit stop? Time for bold late-cycle moves

Native cross-border platforms have a harmonized cross-border
customer proposition delivered through a single IT stack and organization
Countries that have adopted or are adopting open banking

Source: McKinsey analysis

Single IT stack

Single organization

Harmonized customer proposition
(Product o!ering, customer segments, channels, and processes)

Cross-border bank platform

Market 1 Market 2 Market 3 … Market n

Legal entity 1 Legal entity 2 Legal entity 3 … Legal entity n

Balance sheet 1 Balance sheet 2 Balance sheet 3 … Balance sheet n

Ledger 1 Ledger 2 Ledger 3 … Ledger n

Exhibit

25

Local market–speci!c
position

38The last pit stop? Time for bold late-cycle moves

Faced with increased capital and liquidity constraints following
the global financial crisis, banks globally have tried a long list
of interventions across operative levers to increase returns.
Though successful to varying degrees, the marginal return of
these initiatives is stalling, and most banks still fail to produce
the returns that investors demand. Hence, the urgency of
the bold moves highlighted in this chapter. Some are pure
defense—for example, managing “new” risks. Some will help
banks to go on the attack with material economic impacts—
for example, transferring non-differentiating activities to

third-party “utilities” and improving CX by redesigning critical
customer journeys and decision points. Others, such as
advanced data analytics and talent, cut across both. And
across all these levers, inorganic options, which most banks
have not considered actively for some time, should return to
the management table. Are there other levers a bank should
consider in light of its starting position and the condition
of the markets it serves? We address this question in the
concluding chapter.

Can technology help cross-border M&A make a comeback?
Since the global financial crisis, the appetite for cross-border M&A has dropped significantly due to regulators’ efforts to
ring-fence certain banking businesses. However, market leaders constrained by share growth in their domestic markets are
starting to ask if technology provides a means to scale banking franchises globally. Several European banks are looking to
emulate leading technology companies that build once and then scale globally as big tech companies like Uber, Facebook,
Tencent, Amazon, and Google have done. These banks have built “one” organization by harmonizing cross-border customer
propositions on the basis of a single IT stack. These stacks support separate, country-specific legal entities, balance sheets,
and ledgers to satisfy local laws and regulations (Exhibit 25). We estimate that these cross-border platforms can reduce C/I
ratios for banks by five to ten percent.1

The advantages include the ability to enter new markets much faster—either organically or through an acquisition—with a
full product and service line-up; the ability to attract world-class business, product, and engineering talent; and the ability
(like big tech companies) to leverage this talent across a global platform. Another advantage is that having one IT stack
spreads IT expenditures over a larger revenue and cost base, and the bank can also avoid enormous IT modernization
lifecycle costs that would normally have to occur every few years across many local markets. Beyond these benefits, there is
an opportunity to change the investor narrative towards valuing the bank as a technology platform company instead of “just”
a bank. However, there are two important caveats. First, there has to some level of customer and regulatory homogeneity
between the markets that a bank wants to access. It’s easier for a Nordic bank to leverage such an architecture between
Sweden and Denmark than for a global bank to use the same to ply similar banking offerings across, for example, the US,
India, and China. Secondly, the tech stack offers scale only in technology; to achieve superior returns banks will still need
significant in-market banking product scale in each market in which it operates.

On balance, we think there is an opportunity for banks to take a page out of the business models of fintechs and neo
banks backed by private equity and venture capital. Most digital lending startups tend to base their business model on the
assumption of credit risk in a single market using a new digital stack and completely digitized lending process. Once they
scale that model, they monetize it by white-labeling the solution to banks in other geographies without necessarily taking on
credit risk. Banks with leading operating models can follow the same game plan and offer solutions to non-competing banks
in other markets. A dollar of fee income earned from leveraging their home market operating model is worth four in capital-
intensive lending models. This could be a model to scale globally without necessarily assuming credit, market, or liquidity
risk across different geographies.

1 Leorizio D’Aversa, Andrea Del Miglio, and Niels Van der Wildt, “The case for cross-border banking platforms,” McKinsey.com, July 2019.

39 The last pit stop? Time for bold late-cycle moves

“. . . The best time to plant a
tree was 20 years ago. The
second-best time is now.”
Chinese Proverb

40The last pit stop? Time for bold late-cycle moves

The right
moves for the
right bank

Introduction
The reality is that each bank is unique. The degrees of strategic
freedom it enjoys depend on its business model, assets, and
capabilities relative to peers as well as on the stability of the
market in which it operates. Considering these factors, we
narrow the set of levers that bank leaders should consider to
boldly yet practically achievable moves to materially improve—
or protect—returns within the short period of time afforded
by a late cycle. To that end, in this chapter we classify each
bank into one of four archetypes, each with a set of levers that
management should consider. In combination with the universal
levers discussed in Chapter 2, these archetypal levers form a
full picture of the degrees of freedom available to a bank.

The four archetypes are defined by two dimensions: (i) the
bank’s strength relative to peers and (ii) the market stability of
the domain within which the bank operates (Exhibit 26; and see
sidebar, “Identifying the four bank archetypes,” for more details
on how the archetypes are derived):

1. Market leaders are top-performing financial institutions in
attractive markets. They have had the best run economically in
this cycle, growing returns faster than the market and earning
well above their cost of equity. Their critical challenge is to
sustain performance and maintain their leadership position into
the next cycle.

2. Resilients tend to be top-performing operators that
generate economic profit despite challenging market and
business conditions. Their strategic priority is to sustain
returns in a low-growth, low-interest rate, and highly disruptive
environment. For resilient leaders in challenged business
models such as broker dealers, reinvention of the traditional
operating model itself is the imperative.

3. Followers tend to be mid-tier organizations that continue
to generate acceptable returns, due largely to the favorable
conditions of the markets in which they operate, but whose

The reality is that
each bank is unique.

41 The last pit stop? Time for bold late-cycle moves

overall enterprise strength relative to peers is weak. The
key priority for followers is to rapidly improve operating
performance to offset market deterioration as the cycle turns
by scaling, differentiating or radically cutting costs.

4. Challenged banks generate low returns in unattractive
markets and, if public, trade at significant discounts to book
value. Their strategic priority is to find scale through inorganic
options if full reinvention of their business model is not feasible.

To identify the degrees of freedom relevant for each bank
archetype, we assessed: (i) who they are: a description of how
banks in each archetype have performed economically in
recent years (

Exhibit 27

) and (ii) where they live: the underlying
health of the markets in which they operate (

Exhibit 28

). These
factors point to what they should prioritize, that is, the critical
moves banks in each archetype should prioritize during the
late cycle.

There are four bank archetypes, based on enterprise strength and market stability

!Market is defined as a homogenous customer base (asset class), i.e. tend to be geography (Retail and commercial) or global asset class market (CMIB, Wealth and Asset
management)

Enterprise strength

Market stability

Low
High

HighLow

The bank’s individual state relative to its respective core market!:

Enterprise health
Assesses the sustainability
of the performance premium
through qualitative dimensions
including (i) culture, and (ii)
ability to manage balance sheet
risks, operational (including
digital) and reputational risk.

Performance premium
Return on tangible equity
(ROTE) of bank – ROTE of the
underlying market in which it
operates.

Level of stability and performance based on underlying key factors:

Disruption potential:
Assesses the sustainability
of the market’s pro”tability
through qualitative dimensions
including (i) customer behavior
shifts and technology trends (ii)
supply-side forces including
regulation and private capital
investment, and (iii) market
concentration.

Market pro!tability
Average ROTE of the relevant
market – cost of equity.

Followers

Market leaders

Challenged

Resilients

Exhibit 26Exhibit 26

4
archetypes, based on enterprise strengh
and market stability

42The last pit stop? Time for bold late-cycle moves

Revenue yield is the key di!erentiator across the four archetypes

Average performance metrics by archetype, 2016–18, %

!Operating income over assets. “Operating expenses over assets. #Impairments cost over assets. Metrics are simple averages per bank.
Source: S&L & CapIQ (n = size of 984 banks from different banking segments)

10.7
17.1

9.6

1.6

Enterprise
strength

Low
High

HighMarket stabilityLow

Resilients Market leaders

Challenged Followers

Resilients Market leaders
Challenged Followers

Average return on tangible equity (ROTE) Average revenue over assets!

2.9 4.2

3.2
1.8

Resilients Market leaders
Challenged Followers
Resilients Market leaders
Challenged Followers

Average cost over assets” Average risk#

1.7 2.2

2.01.3

0.24
0.48

0.400.30

!Market is defined as a homogenous customer base (asset class), i.e. tend to be geography (Retail and commercial) or global asset class market (CMIB, Wealth and Asset
management)
Exhibit 27

43 The last pit stop? Time for bold late-cycle moves

!Operating income over assets. “Operating expenses over assets. #Impairments cost over assets. Metrics are simple averages per bank.
Source: S&L & CapIQ (n = size of 984 banks from different banking segments)

Favorable markets are primarily in Asia and North America, while unfavorable markets are
found in Europe and developed Asia
Geographical split of archetypes, % of total

Source: S&L data & CapIQ data (n = 984 banks)

Resilients (n =230)

Market leaders (n =210)

Eastern Europe

Eastern Europe

Africa

Western Europe
44

Developed Asia

28

Emerging
Asia
17 Emerging Asia

46

North America
25

Scandinavia

7

2

Latin
America

9

Africa
5

4
1

Developed
Asia

11
Emerging Asia
Scandinavia
Eastern Europe

Africa
Challenged (n =354)

Developed Asia
43

12
5

1
0

Western Europe

37

Developed Asia
Eastern Europe

Followers (n =190)

8

North America
36

Emerging Asia
38

2
Latin
America
9

Africa
6

Exhibit 28

44The last pit stop? Time for bold late-cycle moves

Archetypal levers comprise three critical moves—ecosystems,
innovation, zero-based budgeting—in two of the three
dimensions discussed in Chapter 2—that is, productivity and
revenue growth. Combining the universal and archetypal
levers results in the degrees of freedom available to each
bank archetype (Exhibit 29). Unsurprisingly, market leaders
and resilients should focus primarily on levers that will allow
them to gain further scale and grow revenues through
ecosystems and innovation, with productivity improvements
limited to outsourcing non-differentiated costs to third-party
“utilities.” By contrast, followers and challenged banks both
need to achieve productivity improvements through ZBB, and
additional scale within their niche segments with inorganic
options as the most credible choice.

A brief look at the archetypal levers
The ecosystem opportunity for driving growth
In previous editions of the Global Banking Annual Review,

we have examined in detail ecosystems that reach across and
beyond banking. What has become particularly important
over the past 12 months is the increased significance of scale,
the pressure to find new revenue streams, and the threat
from innovative big tech challengers (for example, Facebook
and Amazon). These trends increase the attractiveness of
ecosystems, which offer a unique way to supercharge scale
geographically or within a customer segment by leveraging the
bank’s customer relationships and data. As such, given rapid
innovation and the high number of customer touchpoints that
allow an institution to “own” the client relationship, payments
services have become a key ecosystem battlefield (

Exhibit 30

).
It is no surprise that several large technology enterprises have
entered banking through payments (for example, Apple Pay,
Alipay, Google Pay). Furthermore, ecosystems are no longer
only an “Asian retail consumer” success story, as we now see
successful examples across North America, as well as in global
wholesale banking.

Identifying the four bank archetypes
The degrees of freedom available to a bank in setting its strategy depend on both (i) enterprise strength relative to peers and
(ii) the market stability of the domain within which the bank operates. Each dimension is characterized by both quantitative
and qualitative methods that capture the current state as well as the likelihood of change. Based on this framework, each
bank falls into one of four archetypes.

Enterprise strength is reflected in a bank’s performance premium and health. While the bank’s performance premium
relative to its peers reflects its strength in its relevant markets, this alone fails to capture the enterprise’s overall health, that
is, its ability to sustain its performance premium.

Performance premium: Answers the question, “Is the bank a high performer?” This is determined by calculating the
bank’s three-year average ROTE less the three-year average ROTE of the market(s) in which it operates. “Market” may refer
to a homogenous customer base (for example, an asset class), geography (especially for retail and commercial banking),
or global asset class market (for example, capital markets and investment banking or wealth and asset management). As
discussed in Chapter 1, performance has a high correlation with in-market scale, so this dimension captures in-market size
and a bank’s true ability to generate scale benefits.

Enterprise health: Answers the question, “How susceptible to risk (relative to peers) is the bank’s business model?” We
determine this through a combination of quantitative factors (for example, direct risk exposures to market and credit risk
in specific sectors or asset classes) and measurable qualitative elements such as non-balance sheet risk (for example,
operational risk, including digital and reputational risk) and the enterprise’s culture. Market stability combines the market’s
profitability and its potential for disruption, with market profitability reflecting the current attractiveness of the market and
disruption potential indicating the sustainability of that performance.

Market profitability: Answers the question, “Does the underlying market return its cost of capital?” We determine this by
calculating the three-year average ROTE of the market minus the cost of equity. Like the performance premium in enterprise
strength, returns are considered specific to the relevant customer base (asset class), typically by geography (for retail and
commercial banking) or global asset class market (for CMIB, wealth and asset management, and payments).

Disruption potential: Answers the question, “How much disruptive change is the bank’s core market experiencing or
likely to experience?” This dimension seeks to capture the underlying structural economic health of the domain within
which the bank operates. We determine this by assessing: (i) changes in customer behavior (for example, switching inertia)
and technology trends (for example, shift from branch to online channels), (ii) supply-side forces, including regulation
(for example, open banking) and private capital investments, and (iii) market concentration (degree of fragmentation or
consolidation).

45 The last pit stop? Time for bold late-cycle moves

Late-cycle degrees of freedom vary across archetypes–consisting of archetypal
levers and universal levers

!Zero-based budgeting. “Customer experience. #Data and advanced analytics, and talent.

Archetypal
levers

Key late–cycle priorities by archetype

Universal
levers

EcosystemsInnovation PartnershipsZBB! M&A Utilities CX” Risk Data#

Market leaders
(21%)

Resilients
(23%)

Followers
(19%)

Challenged
(36%)

Exhibit 29

Payments is a key battleground for customer innovation by incumbents and new entrants

!1,700+ cases registered in the database as of August 2017, might not be fully representative. “Includes small, and medium-size enterprises. #Includes large corporates, public
entities, and non-bank $nancial institutions. %Includes retail current account (CA) deposit revenue and corporate CA and non-CA deposits. &Includes investment banking, sales
and trading, securities services, retail investment non-CA deposits, and asset management factory.
Source: McKinsey Panorama FinTech database, Panorama Global Banking Pools

Innovating players by customer segment and product focus, 2018, % of total!

<5% 5%–7.5% 7.5%–10% >10%

Banking segment’s
share of total
banking revenues

Products and capabilities

Customer
segments

Retail

Commercial”

Large
corporate#

Account
management%

Lending
and $nancing

Financial assets
and capital markets&

Payments

8 10 14 16

4 8 12 7

3 3 9 7

Exhibit 30

46The last pit stop? Time for bold late-cycle moves

How can banks use the ecosystem opportunity to their
advantage in the next two to three years? Given the short
span of time that a late cycle typically offers, we focus on two
ecosystem models: Orchestrators of existing platforms and
participants that access other platforms to quickly extend
revenue footprint without significant investments (see previous
GBARs for detailed definition of ecosystem archetypes).

Ecosystem orchestrators can monetize their platforms by:
(i) bundling low- and high-frequency products and services,
(ii) cross-selling through partners’ channels, and (iii) using
multidimensional data for precision marketing. State Bank
of India (SBI) is an example of the power of increasing the
frequency of client touchpoints. In 2017, it launched YONO, an
integrated digital banking platform combining SBI’s low- to
mid-frequency traditional banking ecosystem with its high-
frequency online (non-banking) marketplace, enabling users to
bank and shop in a single visit. The platform handled 2.5 million
transactions in the first quarter of 2019, up 224 percent from
the previous quarter—with over 27,000 banking accounts
opened every day.

Ecosystem participants can both acquire new customers
and build value by manufacturing key products and services
such as payments and credit services for distribution through
existing platforms either under their own brand or through a
white-labeling opportunity. In either case, the product through
which a bank participates in the ecosystem should be chosen
by identifying an unmet need or an underserved segment that
the bank can address through its best-in-class capabilities
or low-cost banking. Participant banks will, of course, need
to establish customized and tested APIs that ensure the
orchestrator’s systems can communicate without glitches.
Extending loans to SMEs via Amazon’s platform is one example.

What is more, banks need not choose just one ecosystem. In
our view, companies that join multiple ecosystems can expect
to create more value, as they can achieve economies of scale by
sharing customer acquisition costs and improving their cross-
selling capabilities. Finally, given the right machine learning and
AA capabilities, the new customer data to be captured from
value chains spanning multiple ecosystems can yield highly
valuable insights, enabling banks to lower risk and other costs
while also deepening customer relationships.

Unleashing innovation to fortify and build competitive
advantage
While exploring specific innovations that a bank might
pursue lies beyond the scope of this report, we discuss the
importance of adopting a portfolio approach for managing the
organization’s innovation initiatives in a systematic and holistic
way to maximize the success of its innovation investments. To
develop innovation into a strategic core competency, banks
must answer three key questions: (1) What ROI and contribution
to revenue and profits do we need from innovation, and
how quickly do we need these results? (2) What portfolio of
innovation initiatives can attain this ROI and fulfill our strategy?
(3) How will we adjust the level of risk in the innovation portfolio
as we move through the cycle?

The first step is for top leaders to set goals for the return on
innovation (the “green box” in

Exhibit 31

), define metrics to
measure progress, and set time frames for achieving these
goals. All these aspirations must be “wired” into annual plans
to help leaders measure ROI, understand which initiatives
to continue and discontinue, and establish accountability. In
our work across industries, we have found that 65 percent
of leading innovators set their aspirations in this manner,
compared to only 20 percent of all other companies.

43%
of current market leaders will cease to be at the top
come the next cycle

47 The last pit stop? Time for bold late-cycle moves

The second step is to manage the organization’s innovation
efforts and investments as an integrated “innovation portfolio.”
Banks should manage these initiatives as a portfolio,
prioritizing them according to their relevance to strategic
priorities, risk, and time to impact. Through this portfolio view,
leaders can manage the balance between short-term initiatives
that will generate revenue or cost savings relatively quickly, with
longer-term investments. Leaders should look for opportunities
to reallocate resources, doubling down on initiatives that
are succeeding and quickly killing those that are struggling
by using metered funding, agile governance, and other
mechanisms. Our research shows that 47 percent of leading
innovators have strong innovation portfolio management and
resource allocation systems, compared to 12 percent of other
companies.

Finally, banks should integrate the first two steps within a
robust operating model that will allow them to periodically
adjust the aspiration (the “green box”), deciding which initiatives
to drop and which ones to accelerate as macroeconomic
and industry conditions change. The transparency and fast
decision-making mechanisms of this model free banks from
the need to impose a “universal haircut” on all innovations in
a slowdown, only to restart them once conditions improve.
Ultimately, innovation is not an “on/off” switch but rather a
range of options that can be exercised to optimize returns
through the cycle.

Zero-based budgeting: Rethinking the operating
model bottom-up

The operating platforms and organizational structures in
banks—especially those in developed markets—were built
for growth and interest-rate scenarios that are unlikely to
materialize in the medium term. This, in addition to increased
digitization, calls for an entirely new operating model that
is hard to build incrementally. However, lessons from other
industries—including automotive manufacturers and telcos—
prove that true cost transformation for established players
is possible if the operating model is zero-based (

Exhibit 32

).
Further, ZBB embeds a model and culture of continuous
improvement, setting banks up to weather late-cycle
challenges, especially in constrained markets. In fact, the zero-
based model has already been tested in the banking sector,
with a leading European bank achieving a reduction of 280 bps
in the C/I ratio soon after implementation.

To execute a ZBB program, banks will have to fundamentally
rethink the way they look at cost. ZBB is a repeatable process
to rigorously review every dollar in the annual budget, manage
monthly financial performance, and build a culture of cost
management. What makes ZBB unique is not the budgeting
methodology but the shift in mindset that upends managers’
default assumptions. As one executive who made the transition
to ZBB told us, “It was more effective to talk about every dollar

An organization without clear aspirations and quanti!ed, committed goals for innovation will
fail to innovate at scale
Revenues of hypothetical organization, $ billion

Source: McKinsey Innovation Practice

Innovation by

2025

This box represents what
innovation needs to deliver, and
can be broken down into the
business as targets

Target
position

2025

Current
position

2019

Market
growth
(ie, fair
share)

Share
gain in
current

business

Incremental
innovation

through
process

e!ciency

Inorganic
growth
(M&A)

Innovation

Protect the core

Diversi”cation
and adjacencies

Ecosystem
innovation

New business
models6.5

2.0
2.0

0.8

0.3

0.2

0.21.5

0.5

0.3 0.2

Exhibit 31

48The last pit stop? Time for bold late-cycle moves

spent in terms of efficiency, and ask if it was really necessary,
rather than to compare it to last year. It resets the discussion.”1
While ZBB is a powerful lever for material cost reduction,
it is a challenge to implement throughout an organization
due to the depth and breadth of change it requires. In short,
this archetypal lever is best pulled by banks that need
critical change.

Who you are you and what are your late-cycle
priorities? Game board for the archetypes

Market leaders: Priorities to retain leadership into the
next cycle
Who they are. Market leaders have benefited from favorable
market dynamics as well as their (generally) large scale,
both of which have allowed them to achieve the highest
ROTEs of all bank archetypes—approximately 17.0 percent
average ROTE over the previous three years. And they have
achieved this leadership without having to focus too much
on improving productivity, as reflected in their average C/A
ratio of approximately 220 bps. Unsurprisingly, most of the
market leaders in developed markets are North American
banks; however, it is also interesting to note that a significant

1 Matt Fitzpatrick and Kyle Hawke, “The return of zero-base budgeting” McKinsey.com, April 2015.

proportion (approximately 46 percent) of market leaders
consists of banks in emerging markets in Asia—mainly China—
and the Middle East. These banks, even with declining ROTEs
in the previous cycle, still have returns above the cost of capital.

Priorities for the late cycle. For this group, the need for
action is clear as we head into the late cycle: These banks
must understand their key differentiating assets and invest
in innovation using their superior economics, especially
when peers cut spending as the late cycle bites. As noted
earlier, history shows us that approximately 43 percent of
current leaders will cease to be at the top come the next cycle
(

Exhibit 33

). The investments made now—whether organic
or inorganic—will decide their place at the top table in the
next cycle.

Given the scale advantages that leaders enjoy, banks in this
group will be challenged to sustain revenue growth, especially
as credit uptake typically slows in the late cycle. The focus
now needs to shift toward increasing their share of wallet
among current customers by extending their proposition
beyond traditional banking products. This should be done
through a classic ecosystem move, where they can generate
capital-light fees by introducing other products into their
platforms. This approach should allow them to expand

Other industries have shown that true cost transformation is possible if the operating model
is zero based

!Top 6 EU markets: UK, Germany, France, Spain, Italy, and Netherlands.
Source: SNL Market Intelligence, 15 top global banks by asset size; WBI tool & McKinsey analysis

Automotive manufacturers Telcos

Operating margin, % EU 6!, Structural costs, ” billion

Approach

Reducing head count
continuously
Shifting large portion of
sales to less expensive
online channel and facilitat-
ing self-service solutions
Mix of out-o#shoring of both
non-core and core activities
Consolidation through
in-country and cross-
country M&A
Setting up of shared service
center
E$ciency initiatives
engaging sta# to identify
possible cost%savings

–
–
–
–
–
–
–
–
–
Approach

Streamlined product
portfolio based on a
modular approach
Transferred lean manage-
ment principles from blue-
collar to white–collar
working environments
Used continuous improve-
ment assumptions for
budgeting: managers have
to constantly look for
e$ciency improvement
opportunities
Continuous–improvement
assumption also
transferred to supplier
negotiations

9.7

2010 2015 2010 2015

11.4 79.9

67.3

+18% –16%

Exhibit 32

49 The last pit stop? Time for bold late-cycle moves

revenues in a short period of time without spending significant
amounts in development or acquisition costs. Meanwhile,
improvements to the bank’s innovation capabilities as well
as capital commitments to innovation should remain in the
forefront. Market leaders are also in prime position to explore
opportunities to acquire smaller banks that have a customer
base that is like their own, or a struggling fintech that has
digital capabilities that can supplement the bank, and to
pursue a programmatic M&A strategy across a select set
of key technologies. In most cycles, a downturn creates the
best opportunities, and now is the time to create the wish
list. Fundamental to all these is the need to retain a strong
capital and management buffer beyond regulatory capital
requirements to capitalize on a broad range of opportunities
that will likely arise.

Resilients: The challenge of managing returns in
sluggish markets
Who they are. Resilients have been strong operators and
risk managers that have made the most of their scale in what
have been challenging markets due to either macroeconomic

conditions and/or disruption. This has allowed them to
generate returns just above cost of equity, with average ROTE
of 10.7 percent over the previous three years, without taking
on undue risk, as reflected in the lowest impairment rates of
all archetypes (24 bps). Banks in this archetype have worked
hard at costs even as they have struggled to maintain revenues,
beating the C/A ratios of market leaders (their peers in buoyant
markets) by nearly fifty basis points. However, at 170 bps, there
is still significant opportunity for productivity improvements
when compared to best-in-class peers. Unsurprisingly,
resilients are almost all in Western Europe and developed Asian
markets such as Japan, which have been the toughest banking
markets over the past three years. Leading broker dealers also
feature in this group.

Priorities for the late cycle. Like market leaders, resilients
must seek constantly a deeper understanding of which assets
set them apart from the competition and take advantage
of their superior economics relative to peers to invest in
innovation, especially when peers cut spending as the late cycle
takes hold. However, unlike market leaders, given that they

Odds of remaining in same archetype, % of banks What typically shapes a bank’s odds?

Leaders must be careful and laggards can be hopeful; archetypes move across cycles

!Defining average return on tangible equity (ROTE) of markets as 15%, 2005–07.
Source: S&L data (n = 432 banks)

En
din

g p
os

itio
n

20
16

–1
8

Endowment (who you are)

• Size
• Regulatory capital levels
• Past investments in product development

and technology

Trends (where you are)

• Banking pro”tability in core markets
• GDP growth in core markets

Moves (what you do)

• Reinvestment to promote organic growth
• Resource reallocation toward high (ROE),

businesses with growth potential
• Improved margins from lower costs,

reduced credit losses, and lower deposit
rates

• Changing product or pricing mix that
supports higher gross interest income
and/or greater levels of fee income

• Inorganic moves–acquisitions, disposals,
and partnerships

Ch
all

en
ge

d
Fo

llo
we

rs
Re

sil
ien

ts

Ma
rke

t

lea
de

rs
Challenged
Followers
Resilients

Market
leaders

Starting position,!

2005–07

28
33

29 35

29 16

37

17

13

11171018

25
23
60

Market change contributed
to archetype change

Archetype changed

Archetype remained unchanged

Exhibit 33

50The last pit stop? Time for bold late-cycle moves

already operate in an unattractive market and barely earn their
cost of capital, they have a higher sense of urgency in making
their late-cycle moves.

The first item on their agenda, just like market leaders, should
be to focus on increasing their share of wallet among their
current customers through enhanced CX and by building a
value proposition that extends beyond the traditional set of
banking products. The most practical path is to expand their
ecosystem activities and improve their ability to innovate.
Second, those with a large infrastructure asset (for example,
securities companies), should innovate by white-labeling their
platforms across non-competing peers and other industry
participants to find new ways of monetizing their assets.
Further on the cost front, resilients need to pay closer attention
to opportunities for improving productivity by exploring bank-
wide appetite for ZBB. Where the resilients differ from market
leaders is in inorganic levers. Due to their lower excess capital
reserves, they should explore strategic partnerships to acquire
scale or capabilities rather than material acquisitions. However,
they should remain alert to the possibility of a compelling
distressed asset becoming available.

Within resilients are banks that are less challenged by the

macro conditions and more by the declining economics of
their own underlying business models. For these, the playbook
listed above definitely holds but they need to go beyond. As
mentioned earlier in this report, there is an urgent need to find
areas where they can actually add value and get rewarded as
their core business economics fall. Identifying those areas and
ramping up on those capabilities organically or inorganically will
be the late cycle priority.

Followers: Preparing for tailwinds turning to headwinds
Who they are. Followers are primarily mid-sized banks
that have been able to earn acceptable returns, due largely
to favorable market dynamics. However, their returns (on
average 9.6 percent ROTE) have been little more than half
of those of market leaders, who have also operated with the
same favorable market dynamics. The principal driver of their
underperformance relative to market leaders is in revenue
yields, where they are 100 bps lower. Finally, given their
underperformance relative to other banks in similar markets,
they have invested in productivity improvements and have C/A
ratios 20 bps lower than market leaders but 70 bps higher than

similarly underperforming peers in more challenged markets.
Approximately 76 percent of followers are North American and
Chinese banks.

Priorities for the late cycle. There is a clear need for action
with bold moves to ensure that returns do not deteriorate
materially during a downturn. Furthermore, if they are to be
among the 37 percent of follower banks that become leaders
regardless of market environment (Exhibit 31), now is the time
to build the foundation as they still have time to benefit from the
excess capital that operating in a favorable market gives them.

Given their sub-scale operations and the fact that they are still
in a favorable market, they should look for ways to grow scale
and revenues within the core markets and customer sets that
they serve. This includes both organic and inorganic options.
On the latter, followers, which have underperformed their peers
in buoyant markets, should also reevaluate their portfolios and
dispose of non-strategic assets before the market turns.

Organically, growth priorities for this group are best realized
by achieving a high standard of CX and improving the bank’s
innovation capabilities, with an emphasis on understanding
ways to better serve the specific needs of their niche market
rather than developing revolutionary new products. They

should also explore strategic partnerships that allow them to
offer new banking and non-banking products to their core
customers as a platform, thereby extending much needed
capital-light, income-boosting returns.

Cost is also a significant lever for this group. With an average
C/A ratio that is 70 bps higher than peers in more challenged
markets (where challenged banks as a group have pulled the
cost lever harder than other archetypes), followers have the
potential to improve productivity significantly. For the portion
of the cost base that cannot be outsourced to third parties,
implementing ZBB is a highly effective way to transform the
bank’s approach to costs.

The Challenged: Final call for action

Who they are. Some 35 percent of banks globally have earned
a mere average of 1.6 percent ROTE over the past three years.
This is the lowest average return of all archetypes and well
below the cost of equity of these banks, which we classify
as “challenged banks.” With an average C/A ratio of 130 bps,

Followers need to make bold moves to
ensure that returns do not deteriorate
materially during a downturn.

51 The last pit stop? Time for bold late-cycle moves

they have the best cost performance. The problem,
however, is in revenues, where they have the lowest
revenue yields, at just 180 bps, as compared to an
average revenue yield of 420 bps among market
leaders. Further analysis of this category also points
to the fact that most operate below scale and are
“caught in the middle,” with neither high single-
digit market share nor any niche propositions.
Unsurprisingly, most of these banks are in Western
Europe, where they contend with weak macro
conditions (for exapmle, slow loan growth and low
interest rates).

Priorities for the late cycle. For challenged banks,
the sense of urgency is particularly acute given
their weak earnings and capital position; banks in
this group need to radically rethink their business
models. If they are to survive they will need to gain
scale quickly within the markets they currently serve.
To that end, exploring opportunities to merge with
banks in a similar position would be the shortest
path to achieve that goal. Potentially high-value
mergers within this segment are of two kinds: first are
mergers of organizations with completely overlapping

franchises where more than 20 to 30 percent of
combined costs can be taken out, and second are
those where the parties combine complementary
assets, for example, a superior customer franchise
and a brand on one side and a strong technology
platform on the other.

The only other lever at hand is costs, in which this
group already leads other banks. However, there
should still be further opportunities, including the
outsourcing of non-differentiated activities and the
adoption of ZBB, both discussed earlier. With an
average C/A ratio of 130 bps, challenged banks as a
group still have a good 50 bps to cover before they
produce the best-in-class cost bases we’ve seen
from Nordic banks. In addition, costs (especially
complexity costs) could creep up as the group chases
higher revenue yields through product introductions.
It is better to launch products off a leaner base, and,
should a bank seek an acquirer, a lower cost base
would also help strengthen valuations.

180 bps
average revenue yields for challenged banks,
compared to 420 bps for market leaders

52The last pit stop? Time for bold late-cycle moves

53 The last pit stop? Time for bold late-cycle moves

54The last pit stop? Time for bold late-cycle moves

While the jury is still out on whether the current
market uncertainty will result in an imminent
recession or a prolonged period of slow growth,
the fact is that growth has slowed. As growth
is unlikely to quicken in the medium term, we
have, without question, entered the late cycle.
Compounding this situation is the continued
threat posed by fintechs and big technology
companies, as they take stakes in banking
businesses. The call to action is urgent: Whether
a bank is a leader and seeks to “protect” returns
or is one of the underperformers looking to turn
the business around and push returns above
the cost of equity, the time for bold and critical
moves is now.

To this end, banks should urgently consider
a suite of radical organic or inorganic moves
before we hit a downturn. These universal
levers span both defensive moves (for example,
improving risk management with advanced
analytics and artificial intelligence) and
offensive moves (such as dramatically lowering
costs by outsourcing non-differentiated
cost drivers to industry utilities). And at the
same time, making tactical improvements to
customer experience to find elusive revenue
growth and building advanced data analytics
capabilities to support the effort. If executing
any of these moves organically turns out to
be a challenge, the environment is fertile for
mergers, acquisitions, and partnerships. Boards
and management teams need to execute fast to
create the scale that they need to succeed.

However, all banks are not made equal—be
it in their starting economic position or the
attractiveness of the markets and business
models in which they operate. To reflect these
nuances, we divide banks into four archetypes

with varying degrees of urgency and action.
Leaders in both buoyant and stagnant markets
have ridden their scale and differentiated
proposition through this economic cycle earning
superior returns and generating the luxury of
excess capital. What these banks do now with
their capital will decide their fate in the next
cycle. Investing heavily in innovation where they
have the highest competitive conviction will
be key; investments should include attempts
to reinvent business models to face potential
disruption down the line. Leaders should stretch
their operating model to look at monetization
options beyond banking. Inorganic options
should also be considered to add scale or to
reinvent business models; especially as certain
attractive assets will most certainly come into
play. History tells us that 40 percent of those
at the top drop to the bottom half of peers in
the next cycle—moves made today will have
a defining role in hedging the probability of
that slide.

Then there are the followers and challenged
banks. For many, the urgency now is about
finding niches in their business model where
they are truly champions and doubling down
organically and inorganically to build scale
around these customer and product sets. It is
clearly an opportunity they have missed in this
cycle. However, for the nearly 35 percent of
banks classified as challenged, the barely one
to two percent ROTE that they generate doesn’t
leave them with marginal organic options. These
banks face a last call for radical inorganic moves
to build scale or restructure business models
before it’s too late.

Reinvent, scale, or perish: These are the

Conclusion

55 The last pit stop? Time for bold late-cycle moves

Reinvent, scale, differentiate, or perish: These are the stark
choices banks face today. With late-cycle clouds gathering,
the call for action is loud and clear.

And lest we forget in our shareholder value-oriented pursuit of
scale and returns, we note that banks remain the fundamental
pillars of money transmission and the custodians of the wealth
of nations. Because of the special role they play in society, they,
perhaps more than other industries, benefit from society in
areas such as deposit protection and regulation as a means of
constraining supply. In return, they are particularly accountable
in an era of rising inequality and falling faith in historically
trusted institutions; beyond shareholders to society and
the sustainability of the environment in which they and their
clients operate.

Late cycles spur talks of “bad banks.” Society—including
investors and regulators—increasingly wants “good banks,” i.e.,
banks that have a firm sense of their “why.” As the proverb goes,
“If you know your ‘why,’ it is a lot easier to figure out ‘how.’ And
as the Business Roundtable noted in its recent statement on a
broader stakeholder capitalism, banks especially need to figure
out their purpose in society beyond the basic economic role
they play. Those banks that look beyond the regulator’s license
and central bank protection in grappling with their reason for
existing will thrive in the late cycle and into the next.

Postscript

56The last pit stop? Time for bold late-cycle moves

57 The last pit stop? Time for bold late-cycle moves

This annual review of the global banking and securities
(“banking”) industry is based on data and insights from
McKinsey Panorama, McKinsey’s proprietary banking
research arm, as well as the experience of clients and
practitioners from all over the world.

58The last pit stop? Time for bold late-cycle moves

Chira Barua
Partner, London
chira_barua@mckinsey.com

Miklos Dietz
Senior Partner, Vancouver
miklos_dietz@mckinsey.com

Somesh Khanna
Senior Partner, New York
somesh_khanna@mckinsey.com

Matthieu Lemerle
Senior Partner, London
matthieu_lemerle@mckinsey.com

Asheet Mehta
Senior Partner, New York
asheet_mehta@mckinsey.com

Kausik Rajgopal
Senior Partner, Silicon Valley
kausik_rajgopal@mckinsey.com

Joydeep Sengupta
Senior Partner, Singapore
joydeep_sengupta@mckinsey.com

Marcus Sieberer
Senior Partner, Zurich
marcus_sieberer@mckinsey.com

The authors would like to acknowledge the
contributions of colleagues Jorge Adib,
Leorizio D’Aversa, Gergely Bacso, Debopriyo
Bhattacharyya, Dr. Stephanie Eckermann,
Arvind Govindarajan, Philipp Härle, Nils Jean-
Mairet, Attila Kincses, Tara Lajumoke, Jared
Moon, Roger Rudisuli, Tamim Saleh, Robert
Schiff, Outi Simula, Zubin Taraporevala and
Zane Williams to this report.

This report was edited by John Crofoot.

We also thank our external relations, editorial,
and design colleagues Matt Cooke, Chris
Depin, Paul Feldman, Richard Johnson, Monica
Runggatscher, and Mark Staples for their
valuable contributions and support.

We are grateful for all the input we have
received, but the final report is ours, and all
errors are our own. We welcome comments
on this research at fs_external_relations@
mckinsey.com.

For queries regarding citing data from this report or for any media inquiries, please contact
Matt Cooke, Director of Communications, at matt_cooke@mckinsey.com.

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