3000 words research paper. There are some sources there already please add more!!

Hang Nguyen

ENG 3304

Topic: Machine Learning in Finance

INTRODUCTION

Ever since its development in the 1970s, machine learning has become an irreplaceable tool for increasing efficiency and accuracy in all industries. The finance industry has been one of the leading industries to implement machine learning in many functionalities across the industry. According to the Investment Banking Council (2019), “AI will generate more than $2.9 trillion in business value and recover 6.2 billion hours of worker productivity by 2021.” As more and more financial institutions are taking advantages of machine learning, many people raise the question of whether machine learning will take jobs away from human, which will make an already competitive jobs market become even more cutthroat. Moreover, machine learning has also brings new challenges facing not only financial institutes but anyone who wishes to use it. Any further used of machine learning without addressing these issues can bring up a catastrophe. While machine learning can give predictions based on the data its processes, the accuracy of those predictions rely entirely on the quality of the data. Many firms are struggling to ensure quality of the data that they feed into their machine learning system. Thus, there are many regulations and failures in internal processes that also can endanger the results of the machine learning process. In short, machine learning can bring many opportunities but great challenges are still facing companies.

This report will discuss the current trends in machine learning implementation in the finance industry. Next, the report introduces the benefits and examines the challenges facing financial institutes when using machine learning. After that, the report will analyze the impact of machine learning on the jobs market. Finally, the report discuss the future of machine learning in finance.

FINDINGS

· The implementation of machine learning

· Brief introduction of machine learning in Finance.

· M. Romao, J. Costa and C. J. Costa, “Robotic Process Automation: A Case Study in the Banking Industry,” 2019 14th Iberian Conference on Information Systems and Technologies (CISTI), Coimbra, Portugal, 2019, pp. 1-6, doi: 10.23919/CISTI.2019.8760733.

· Discuss the current implementation of machine learning in Finance.

· M. Romao, J. Costa and C. J. Costa, “Robotic Process Automation: A Case Study in the Banking Industry,” 2019 14th Iberian Conference on Information Systems and Technologies (CISTI), Coimbra, Portugal, 2019, pp. 1-6, doi: 10.23919/CISTI.2019.8760733.

· Didur, K. (2018, July 11). Machine learning in Finance: Why, what & how. Retrieved February 15, 2021, from

https://towardsdatascience.com/machine-learning-in-finance-why-what-how-d524a2357b56

· The benefits and challenges facing machine learning

· Benefit of machine learning.

· Didur, K. (2018, July 11). Machine learning in Finance: Why, what & how. Retrieved February 15, 2021, from https://towardsdatascience.com/machine-learning-in-finance-why-what-how-d524a2357b56
· M. Romao, J. Costa and C. J. Costa, “Robotic Process Automation: A Case Study in the Banking Industry,” 2019 14th Iberian Conference on Information Systems and Technologies (CISTI), Coimbra, Portugal, 2019, pp. 1-6, doi: 10.23919/CISTI.2019.8760733.

· Ai in investment banking – the new frontier. (2019, September 11). Retrieved February 06, 2021, from

https://www.investmentbankingcouncil.org/blog/ai-in-investment-banking-the-new-frontier

· Challenges facing machine learning

· M. Romao, J. Costa and C. J. Costa, “Robotic Process Automation: A Case Study in the Banking Industry,” 2019 14th Iberian Conference on Information Systems and Technologies (CISTI), Coimbra, Portugal, 2019, pp. 1-6, doi: 10.23919/CISTI.2019.8760733.
· Didur, K. (2018, July 11). Machine learning in Finance: Why, what & how. Retrieved February 15, 2021, from https://towardsdatascience.com/machine-learning-in-finance-why-what-how-d524a2357b56

· The impact of machine learning in jobs market.

· Acknowledges the potential jobs loss.

· Press, G. (2019, July 17). Is ai going to be a jobs killer? New reports about the future of work. Retrieved February 19, 2021, from

https://www.forbes.com/sites/gilpress/2019/07/15/is-ai-going-to-be-a-jobs-killer-new-reports-about-the-future-of-work/?sh=38faf628afb2

· Rinehart, W., & Edwards, A. (2019, July 11). Understanding job loss predictions from artificial intelligence. Retrieved February 19, 2021, from

https://www.americanactionforum.org/insight/understanding-job-loss-predictions-from-artificial-intelligence/

· Opening the door to others jobs field and the skills required.

· Rinehart, W., & Edwards, A. (2019, July 11). Understanding job loss predictions from artificial intelligence. Retrieved February 19, 2021, from https://www.americanactionforum.org/insight/understanding-job-loss-predictions-from-artificial-intelligence/
· Press, G. (2019, July 17). Is ai going to be a jobs killer? New reports about the future of work. Retrieved February 19, 2021, from https://www.forbes.com/sites/gilpress/2019/07/15/is-ai-going-to-be-a-jobs-killer-new-reports-about-the-future-of-work/?sh=38faf628afb2

· The future of machine learning.

· Future implementation trends of machine learning.

· Didur, K. (2018, July 11). Machine learning in Finance: Why, what & how. Retrieved February 15, 2021, from https://towardsdatascience.com/machine-learning-in-finance-why-what-how-d524a2357b56

· Current technology that companies are investing in to address the current limitations of machine learning

· Bouland, A., Van Dam, W., Joorati, H., Kerenidis, I., & Prakash, A. (2020, November 12). Prospects and challenges of quantum finance. Retrieved February 19, 2021, from

https://arxiv.org/abs/2011.06492

.

CONCLUSION

· Summary of main points

· Brief discussion of future of machine learning

REFERERNCES

(to be included)

Project 2 Prompt

Length: around 2,500-3,000 words

Audience: Instructor and Peers (Academic)

In business, reports are often based on significant research and are commonly packed with critical details and data. These documents deliver substantive information and analysis that organizations need to plan, produce, evaluate, and advance their work, including major projects. The task for these reports is to study the topic, analyze it, and present the readers the details of your analysis. They are written in objective, impersonal (yet readable) style. This project asks you to write a report for your fellow peers in which you discuss and analyze the current debate or conflict in the business field you researched for Project 1 and provide conclusions about its history, current state, and future.

Project 2 serves many purposes. First, it is intended to give you practice in writing toward a particular audience and working in a particular genre. Second, it aims to build upon the awareness and knowledge of the current state of your discipline. Third, it gives you practice with researching, analyzing sources (critical reading and thinking), and citing in APA format. Fourth, it aims to give you practice digesting large amounts of information into smaller, more succinct sections (something commonly associated with writing in the business professions).

Your research report will need to include the following elements:

· It must be in report format, with a targeted audience (maybe a company itself, maybe other students who share your major whom you want to inform, etc)

· It should be cited using APA documentation style.

· It must be around 2,500-3,000 words.

· It should include a variety of sources from scholarly journals (at least 2), trade/profession publications, and popular press (newspapers, magazines, blogs, etc.). It must contain a minimum of 10 sources.

· It should be written in a tone that is conversational and objective, as well as positive.

· It must use correct grammar, punctuation, usage, spelling and mechanics.

· It should use sources from 2016-present. Limit pre-2016 sources to background issues.

Hang Nguyen

ENG3304

Feb 19th 2021

Source 1

1. Citation

M. Romao, J. Costa and C. J. Costa, “Robotic Process Automation: A Case Study in the Banking Industry,” 2019 14th Iberian Conference on Information Systems and Technologies (CISTI), Coimbra, Portugal, 2019, pp. 1-6, doi: 10.23919/CISTI.2019.8760733.

2. Type

This is a scholar article

3. Source Credibility

:

This article was published in the 2019 Iberian Conference on Information Systems and Technology. This is a conference held by an association of European and American most prestige colleges including Columbia University and University of South California. There are more than 100 scientists leading in their respective fields in the committee of the conference. The authors of the articles are all professors at the University of Lisbon.

4. Summary and Analysis of the main ideas:

· “Robotic process automation (RPA) is the use of software with artificial intelligence (AI) and machine learning capabilities to handle high-volume, repeatable tasks that previously required only humans to perform.”

· The article introduces the concept of how Machine Learning can replace traditional Business Process Management.

· “in the banking industry, it becomes easier to detect credit card fraud, or target customers for the purpose of marketing campaigns. Another benefit of machine learning over business processes lies in their greater flexibility, for example, it becomes easier to fit the assessment of a certain case to varying external inputs according to new context changes.”

· The articles highlighted the benefits of using such technology in banking: optimization of tasks, people and timing.

· “Taking into account the current regulatory context [10], there is a huge focus on operational risk, as it can generate significant losses resulting from failures in internal processes and unpredictable people’s behavior”

· The articles also highlighted the risks of using such technology: regulators, bad data, bad algorithm.

5. Usefulness

· I will use this article to introduce about machine learning in Finance in the introduction the paper.

· This article will also be used to talks about the benefits and risks of using machine learning in Finance.

6. Connection

s

:

· This article answer the question of what examples of machine learning being used in banking

· The articles also answer the question of the benefits and risks of machine learning

· The articles also answer the question of can machine learning be truly trusted?

Source 2

1. Citation

Ai in investment banking – the new frontier. (2019, September 11). Retrieved February 06, 2021, from

https://www.investmentbankingcouncil.org/blog/ai-in-investment-banking-the-new-frontier

2. Type

This is a professional source

3. Source credibility

This is an article publish by the Investment banking council of America. This is a highly trusted organization in the investment banking industry. They are responsible for the CIBP certification, which is a highly desired certification for investment banking professionals.

4. Summary and Analysis of Main Idea

s

· “According to Gartner, AI will generate more than $2.9 trillion in business value and recover 6.2 billion hours of worker productivity by 2021. Forrester Research also predicts that AI-driven companies will take away a staggering $1.2 trillion away from their non-AI capable counterparts by 2020.”

· This articles gives statistics numbers on how much money AI will generate in business value and how much hours of manual working hours it will reduce.

· “According to the professional network LinkedIn, AI jobs saw an increase of 190% between 2015 and 2017. Even higher is the demand of investment banking professionals who are technologically savvy to incorporate AI in their daily operations across departments, with several initiatives with academia already underway by the likes of Barclays and JP Morgan Chase to enhance their AI adoption at even faster rates.”

· This article also gives statistic numbers on how much jobs it will create.

5. Usefulness

· I will this article to write about whether AI is creating jobs or taking away jobs

· I will use this article to highlight that why is this worth it to invest billions of dollars into developing new AI technology

· The numbers in the article will help my argument of why machine learning is worth it more concreate

6. Connections

· This article will connect with benefits of AI in my papers

· I will use this article to argue my case of why AI will only create more jobs and not take jobs away.

· Connect directly on the machine learning topic in my project

Source 3

1. Citation

Rinehart, W., & Edwards, A. (2019, July 11). Understanding job loss predictions from artificial intelligence. Retrieved February 19, 2021, from

https://www.americanactionforum.org/insight/understanding-job-loss-predictions-from-artificial-intelligence/

2. Type

This is a popular source.

3. Source credibility

America Action Forum is a nonprofit organization dedicated to provide data-driven insight into the US politics. The author, Will Rinehart, is a long-time writer for The Wall Street Journal and Bloomberg. Previously, he was a Research Assistant in Technology for the Institute for Policy and Civic Engagement.

4. Summary and Analysis of Main Idea

· “At the mid-point scenario, 400 million jobs worldwide will face automation by 2030, while 800 million jobs worldwide will face automation in the fastest rate. McKinsey suggested that United States could lose between 16 million and 54 million jobs between 2016 and 2030”

· This article gives specific number of how many jobs will be replaced by AI.

· “In the decade between 2006 and 2016, for example, over 51 million jobs were destroyed, while 179 million jobs were created.”

· According to the authors, more jobs were created than jobs were actually lost due to machine learning/ AI.

· According to the authors, many industries will be reluctant to switch or invest in machine learning due to the capital that it requires.

· In the articles, it includes a table ranking which industries are more likely to be replaced with automation.

5. Usefulness

· I will use the specific numbers in the article to argue for why AI or machine learning will create more jobs than its destroy.

· Using the ranking table, I will make my point that those industries that are being automated are industries that are outdated and will likely go away in the future.

· I will the author statistics research on why industries are reluctant to change to AI or machine learning for my cases that there are jobs that are most likely will not go away for quite some times.

6. Connections:

· More specific numbers on jobs lost and gain than Source 2.

· Connects directly with the question “Will machine learning take jobs away from human”

Source 4

1. Citation

Press, G. (2019, July 17). Is ai going to be a jobs killer? New reports about the future of work. Retrieved February 19, 2021, from

https://www.forbes.com/sites/gilpress/2019/07/15/is-ai-going-to-be-a-jobs-killer-new-reports-about-the-future-of-work/?sh=38faf628afb2

2. Type
This is a popular source.
3. Source Credibility

Forbes is one of the biggest and long-standing business magazine. The magazine has been the source for all business-related news for many people. The author, Gil Press, is a senior editor at Forbes who specializes in technology, entrepreneurs and innovation.

4. Summary and Analysis of Main Idea

· “The fastest growing jobs AI has created from 2017 to 2018 include Senior Data Scientist with an annual growth of 340% (resulting in average salaries of $257,000 according to Burtch Works), Mobile Application Developer (186%) and SEO Specialist (180%). AI is creating “a surge in new career opportunities,” says the ZipRecruiter report.”

· This article points out specific jobs that machine learning will create.

· “Communication, creativity, and critical thinking are most important in the new era of automation.”

· The author highlights which skills are necessary in the future. Plus, the author also highlights that most jobs that machine learning create will most likely to require technical skills such as math, science, coding and especially working with data.

5. Usefulness

· I will use this article to write about what specific jobs that machine learning will create. This can argue a point that machine learning will take jobs away but it will also create jobs. It is just that the jobs will be different. Because we, as human beings, always evolve.

· This article will also be used to recognize the different skills that people need to learn in order to find a new job in the market.

6. Connection

· This article are an extension or next-step of the previous source.

· Relate to the question what kind of jobs will machine learning create.

Source 5

1. Citation

Bouland, A., Van Dam, W., Joorati, H., Kerenidis, I., & Prakash, A. (2020, November 12). Prospects and challenges of quantum finance. Retrieved February 19, 2021, from https://arxiv.org/abs/2011.06492

2. Type

This is a scholar source.

3. Source Credibility

This is an article published by Cornell University. Cornell University is one of the most prestigious university in the world with numerous research conducted every year. The authors of this articles are all professors at well-known universities such as UC Berkeley, University of Paris Diderot.

4. Summary and Analysis of Main Idea:

· “Let us start with quantum Monte Carlo methods, one of the most prominent applications of quantum algorithms in finance. It has been shown that full-scale fault-tolerant quantum computers offer a significant speedup, by drastically reducing the number of samples needed compared to classical Monte Carlo methods.”

· This article shows how can quantum computers change the current method of analyzing data in finance.

· “for example for long-only portfolios; and the more general case where the optimization can also include integer constraints, for example when a limit on the number of assets to invest is set. For the first case, we start by describing quantum algorithms with provable speedups for certain cases, based on quantum linear system solvers.”

· This articles explains in details how can quantum computer can be used in finance for portfolio optimization.

· “We emphasize that to date this algorithm has only been performed on systems with a handful of qubits, and has not yet demonstrated an advantage over classical methods due to the small system sizes considered.”

· The article also analyze the limitations of quantum computing and the predictions on when exactly can we use it.

5. Usefulness

· This article can be used to introduce a new concept in machine learning within Finance that companies are actively investing in.

· I will use this article to talk about the future of machine learning in Finance: how it will change, what else are being developed at this moment to balance risks and return.

· I will also use this article to recognize the limitation of quantum computing in Finance and the time will be long before we can use it commercially.

6. Connection:

· This articles relates to the topic of machine learning. When talking about any topic of technology, we need to talk about the future of that technology.

· Quantum Computing in Machine Learning in Finance

Source 6

1. Citation

Didur, K. (2018, July 11). Machine learning in Finance: Why, what & how. Retrieved February 15, 2021, from

https://towardsdatascience.com/machine-learning-in-finance-why-what-how-d524a2357b56

2. Type
This is a professional source
3. Source Credibility

This a trusted medium publication for all things technology-related topic. The author of this article is a professor at Wharton Business School and faculty lead in Artificial Intelligent at Wharton.

4. Summary and Analysis of Main Idea

· “Most financial services companies are still not ready to extract the real value from this technology for the following reasons:

· Businesses often have completely unrealistic expectations towards machine learning and its value for their organizations.

· R&D in machine learning is costly.

· The shortage of DS/ML engineers is another major concern. The figure below illustrates an explosive growth of demand for AI and machine learning skills.

· Financial incumbents are not agile enough when it comes to updating data infrastructure.”

· This article explains why even though machine learning are popular, it is not being used enough in finance.

· This articles also touches on the topic of jobs market demands for a new field: Data Science/ Machine Learning engineers.

· “As a result, machine learning enables companies to optimize costs, improve customer experiences, and scale up services. Here are automation use cases of machine learning in finance:

· Chatbots

· Call-center automation.

· Paperwork automation.

· Gamification of employee training, and more.”

· Detailed examples of what specific activities of finance is machine learning being used at.

5. Usefulness:

· This article will be used in order to highlight the drawbacks or limitations of machine learning.

· This articles will also be used, especially the above chart, to describe the trends of implementing AI in Finance

· This article will also be used to give examples of machine learning in Finance.

6. Connection:

· This article relates directly to machine learning in Finance topic.

· This article will answer the question of trends and examples of machine learning in Finance.