{"id":32830,"date":"2018-11-13T15:48:11","date_gmt":"2018-11-13T20:48:11","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/i-banker\/"},"modified":"2018-11-13T15:48:11","modified_gmt":"2018-11-13T20:48:11","slug":"i-banker","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/i-banker\/","title":{"rendered":"I, Banker"},"content":{"rendered":"

Introduction<\/em><\/strong><\/p>\n

Isaac Asimov\u2019s 1950 novel I, Robot<\/span> posits a future shared, uneasily, by humans and sentient robots. As machine learning becomes more ubiquitous, it seems that world may no longer be one of science-fiction. Machine learning programs\u2019 abilities to analyze, collect, and aggregate data have had profound impacts on our world, extending to the banking and financial services industries.[1] Many firms have sought to bolster the security of their online banking platforms by using machine learning to identify data patterns indicative of fraudulent activities.[2] Programs built on machine learning are also much more efficient than human analysts at pulling relevant information from digital financial statements.[3] Machine learning programs have also been employed to automate short-term trading functions, drastically reducing the number of human traders and time required to execute trades.[4] Goldman Sachs has even begun using machine learning to automate certain parts of its IPO process such as compliance assessment, staffing, and organizational book creation that previously took thousands of analyst- and associate-hours to accomplish.[5,6] While many of these applications are yet inchoate, it is already apparent that firms must embrace and adapt to the implications of machine learning to stay competitive.<\/p>\n

JPMorgan and Machine Learning <\/em><\/strong><\/p>\n

JPMorgan has devoted more time and treasure than any other bulge bracket bank to incorporating machine learning into its banking, asset management, and investment banking operations \u2013 over $10 billion a year spent on tech and over 50,000 employed technologists.[7] Notable developments include a \u201ccontract intelligence platform (CoIN)\u201d that uses \u201cmachine learning to analyze legal documents and extract important data points and clauses\u201d.[8]<\/p>\n

Looking at the longer-term, JPMorgan has also instituted mandatory coding training for all the analysts in its asset management division to enable them to better use machine learning programs in their analysis.[9] JPMorgan also established a \u201ccenter of excellence\u201d in 2016 to \u201cexplore and implement a growing number of use cases for machine learning applications across the firm\u201d.[10] Along with an imposing study entitled \u201cBig Data and AI Strategies: Machine Learning and Alternative Data Approach to Investing\u201d in 2017, this group produced one of the first predictive machine learning applications for investment banking, the \u201cEmerging Opportunities Engine\u201d that predicts if a company is positioned to issue additional equity.[11]<\/p>\n

Despite this success, it is unclear if JPMorgan will extend coding training to the corporate and investment banking division next year.[12] Investment banking predictive applications for machine learning remain scarce, largely because experts believe utility is limited since current methods are unable to compete with the intuition and judgment of savvy humans over the mid- to long-term.[13]<\/p>\n

Recommendations<\/em><\/strong><\/p>\n

While it is true that algorithms can approximate but never replicate human intuition and judgment, opportunities still exist to leverage machine learning in investment banking. In terms of process improvement, JPMorgan can emulate Goldman Sachs in automating many of the initial stages of its IPO and merger and acquisition (M&A) processes \u2013 utilizing processes already present in their CoIN program. Like CoIN, such a program could save many thousands of hours of human labor and materially reduce process time and costs.<\/p>\n

JPMorgan can also continue pursuing applications for \u2018Deep Learning\u2019 posited by their \u201cBig Data\u201d study. The study identified that a \u2018Deep Learning\u2019 method known as Convolutional Neural Nets (CNNs) could be trained to effectively identify and classify complex technical patterns.[14] When coupled with another \u2018Deep Learning\u2019 method known as Reinforcement Learning, already used in the Emerging Opportunities Engine, these CNNs could be of particular use in predicting market conditions in the mid-term to identify the optimal timing for an IPO.[15] The study classifies these methods as \u201cespecially promising\u201d because they continually refine and improve themselves against existing data, auguring increased efficacy given expanding and closely-curated data sets.[16]<\/p>\n

A final recommendation returns to training. The \u201cBig Data\u201d study identified \u201cemploying data scientists who lack specific financial experience or financial intuition\u201d as a significant source of risk in adopting machine learning in finance.[17] JPMorgan has shown a willingness to train its analysts and associates in coding – it may be more beneficial to incorporate instruction on financial principles from the analyst on-boarding program into that used for data scientists. Because of the esoteric nature of machine learning, especially \u2018Deep Learning\u2019, the discovery of breakthrough applications will depend not on analysts with an understanding of coding, but on data scientists who understand finance and the data on which those applications must be founded.<\/p>\n

Conclusion<\/em><\/strong><\/p>\n

Despite their great potential in all areas of finance including investment banking, machine learning programs as we understand them cannot mimic human judgment and expert intuition. Therefore, it remains true that investment bankers need not fear the machines, as in Asimov\u2019s dystopian future. However, do the bankers now have cause to fear financially-savvy data scientists?<\/p>\n

(799 words)<\/p>\n

Endnotes<\/em><\/strong><\/p>\n

[1] H.J. Wilson, A. Alter, and S. Sachdev, \u201cHow Companies are Using Machine Learning to Get Faster and
\nMore Efficient,\u201d 性视界 Business Review, May 3, 2016, https:\/\/hbr.org\/2016\/05\/how-companies-are-using-machine-learning-to-get-faster-and-more-efficient, accessed November 2018.<\/p>\n

[2] Tom Groenfeldt, \u201cCiti Ventures Deploys Machine Learning and Artificial Intelligence with People,\u201d
\nForbes, October 31, 2016, https:\/\/www.forbes.com\/sites\/tomgroenfeldt\/2016\/10\/31\/citi-ventures-deploys-machine-learning-and-artificial-intelligence-with-people\/#6df26ce65268, accessed November 2018.<\/p>\n

[3] Laura Noonan, \u201cJPMorgan\u2019s requirement for new staff: coding lessons,\u201d Financial Times, October 7,
\n2018, https:\/\/www.ft.com\/content\/4c17d6ce-c8b2-11e8-ba8f-ee390057b8c9, accessed November 2018.<\/p>\n

[4] Nanette Byrnes, \u201cAs Goldman Embraces Automation, Even the Masters of the Universe are
\nThreatened,\u201d MIT Technology Review, February 7, 2017, https:\/\/www.technologyreview.com\/s\/603431\/as-goldman-embraces-automation-even-the-masters-of-the-universe-are-threatened\/, accessed November 2018.<\/p>\n

[5] Dakin Campbell, \u201cGoldman Set Out to Automate IPOs and It Has Come Far, Really Fast,\u201d Bloomberg
\nDigital Article, June 13, 2017, https:\/\/www.bloomberg.com\/news\/articles\/2017-06-13\/goldman-set-out-to-automate-ipos-and-it-s-come-far-really-fast, accessed November 2018.<\/p>\n

[6] Raul Hernandez, \u201cGoldman Sachs found a way to automate dealmaking tasks usually managed by
\ninvestment bankers,\u201d Business Insider Digital Articles, June 13, 2017, http:\/\/www.businessinsider.com\/goldman-sachs-found-a-way-to-automate-dealmaking-tasks-usually-managed-by-investment-bankers-2017-6, accessed November 2018.<\/p>\n

[7] Noonan, \u201cJPMorgan\u2019s requirement for new staff: coding lessons\u201d.<\/p>\n

[8] JPMorgan Chase & Co, 2016 Annual Report, https:\/\/www.jpmorganchase.com\/corporate\/annual-report\/2016\/ar-ceo-letter-matt-zames.htm, accessed November 2018.<\/p>\n

[9] Noonan, \u201cJPMorgan\u2019s requirement for new staff: coding lessons\u201d.<\/p>\n

[10] JPMorgan Chase & Co, 2016 Annual Report<\/p>\n

[11] Ibid.<\/p>\n

[12] Noonan, \u201cJPMorgan\u2019s requirement for new staff: coding lessons\u201d.<\/p>\n

[13] Marko Kolanovich and Rajesh T. Krishnamachari, \u201cBig Data and AI Strategies: Machine Learning and
\nAlternative Data Approach to Investing,\u201d JPMorgan & Chase Co., May 18, 2017, p. 8.<\/p>\n

[14] Ibid., p. 112.<\/p>\n

[15] Ibid., p. 114.<\/p>\n

[16] Ibid.<\/p>\n

[17] Ibid.<\/p>\n","protected":false},"excerpt":{"rendered":"

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