Given high transaction volume, accurate historical records, and the quantitative nature of the financial ecosystem, few industries are better poised to reap the process improvement rewards associated with machine learning (\u201cML\u201d) than the financial services industry. JP Morgan (\u201cJPM\u201d), arguably the largest financial institution in the United States, has made a strategic decision to incorporate ML into their operations in order to sustain competitive advantages in today\u2019s historically competitive financial services market.<\/p>\n
Representing half of the Firm\u2019s 2017 net revenue,[1]<\/a> JPM\u2019s lending business can benefit tremendously from the process improvement associated with ML. Fiercely competitive dynamics in the lending market, one in which consumers have come to expect the immediacy they find in the online retail sector and in which regulators have established \u201czero-tolerance\u201d policies for compliance mistakes, have led to industry-wide adoption of ML software used to streamline loan application processes, reducing the lengthy time commitment and paperwork gathering of the past.[2]<\/a> ML software can also be utilized to meet lofty compliance standards set forth by lending regulators, primarily in helping to produce accurate financial reports and expand the scope of stress testing and risk monitoring.[3]<\/a><\/p>\n JPM also has the potential to recognize meaningful benefits from ML implementation across the rest of its day-to-day operations. By 2030, the financial services industry is expected to recognize a 22% reduction in operating expenses (approximately $1-1.3 trillion) resulting directly from ML adoption.[4]<\/a> Use-case examples include incorporating ML into compliance and anti-money laundering functions (expected to save nearly $220 billion).[5]<\/a> Lastly, and perhaps of most long-term importance, ML processes data to suggest the optimal financial solution for an individual, thus providing firms with the ability to better understand customer needs and forecast the demand of financial services, both without human intervention.[6]<\/a><\/p>\n In 2017, JPM\u2019s Quantitative Investing and Derivates Strategy Team issued a 280-page report highlighting how the Firm\u2019s forward-thinking management team has made cautious adoption of ML a short and medium-term priority.[7]<\/a> Highlighted in the report was JPM\u2019s recent incorporation of ML software which trains algorithms on millions of samples of consumer data (age, job, habits, etc.) and financial results (credit history, insurance status, etc.) to make better lending decisions.[8]<\/a> JPM has also allowed ML software to trade securities on their investment management platform: \u201cMachines have the ability to quickly analyze news feeds and tweets, process earnings statements, scrape websites, and trade on these instantaneously,” says Marko Kolanovic, JPM\u2019s Head of Quantitative and Derivatives Research. \u201cThis will help erode demand for fundamental analysts, equity long-short managers and macro investors.\u201d[9]\u00a0<\/a><\/p>\n JPM recently launched a \u201cContract Intelligence\u201d platform that leverages Natural Language Processing, a common ML technique.[10]<\/a> The solution helps to improve back office functionality and reduce administrative expenses by processing legal documents and extracting essential data.[11]<\/a> Manual review of 12,000 commercial credit agreements would typically occupy approximately 360,000 labor hours; ML allows for the same review in a just a few hours.[12]<\/a> Aside from applying ML to its current business processes, JPM has improved its chances of long-term success in ML adoption by creating a corporate culture and building a workforce that is supremely focused on maximizing the ML opportunity. The report highlights JPM\u2019s hiring of some of the world\u2019s top data scientists and further willingness to \u201chire an army of people to acquire, clean, and assess data.\u201d[13]<\/a><\/p>\n While JPM has established itself as an ML thought leader, hurdles remain in ensuring that the sizable opportunity is maximized. First, as most consumers and regulators remain wary of ML applications, particularly in financial services, JPM must build and incorporate its ML capabilities with the upmost transparency to secure market trust.[14]<\/a> Much of this trust will be established with continuous operational improvement and enhanced customer experience, but JPM must be able to quickly explain the \u201cblack box\u201d to optimize the impact of its major strategic initiative, a daunting challenge given that the technological capabilities of ML are limited in the sense that ML software cannot easily explain the reasoning behind its decisions. JPM must continue to invest significant time and resources to combat both this existing reputation of ML in the market and the other inherent limitation of adopting the technology: Although data is being created at an accelerated pace and the robust computing power needed to efficiently process the data is available, most massive data sets are not simple or financially feasible to create.[15]<\/a> Investment of today\u2019s resources must continue to make the adoption of ML easier and more financially appealing over the medium and long-term.<\/p>\n The most significant looming questions that merit comment from the HBS student body revolve around wide-spread adoption of ML in the financial services industry. How will adoption of ML impact the labor force currently in-place? Will adoption of the technology displace millions of employees or will it grant employees increased availability to work on more meaningful tasks? (800 words)<\/p>\n [1]<\/a> JP Morgan, \u201c2017 Annual Report,\u201d JPMorganchase.com, April 5, 2018. https:\/\/www.jpmorganchase.com\/corporate\/investor-relations\/document\/annualreport-2017.pdf<\/a>, Accessed November 2018.<\/p>\n [2]<\/a> Price Waterhouse Coopers, \u201cLeveraging Robotic Process Automation in Mortgage Lending,\u201d PWC.com, March 1, 2017. https:\/\/www.pwc.com\/us\/en\/consumer-finance\/publications\/assets\/pwc-rpa-robotic-digital-labor-mortgage-lending.pdf<\/a>, Accessed November 2018.<\/p>\n [3]<\/a> Ty Kiisel, \u201c7 Ways Automation Improves the Bank Lending Process,\u201d Lendio.com, May 13, 2013. https:\/\/www.lendio.com\/blog\/small-business-tools\/automation-improves-lending\/<\/a>, Accessed November 2018.<\/p>\n [4]<\/a> Lisa Joyce, \u201cArtificial Intelligence and The Banking Industry\u2019s $1 Trillion Opportunity,\u201d thefinancialbrand.com, May 29, 2018. https:\/\/thefinancialbrand.com\/72653\/artificial-intelligence-trends-banking-industry\/<\/a>, Accessed November 2018.<\/p>\n [5]<\/a> Lisa Joyce, \u201cArtificial Intelligence and The Banking Industry\u2019s $1 Trillion Opportunity,\u201d thefinancialbrand.com, May 29, 2018. https:\/\/thefinancialbrand.com\/72653\/artificial-intelligence-trends-banking-industry\/<\/a>, Accessed November 2018.<\/p>\n [6]<\/a> Lisa Joyce, \u201cArtificial Intelligence and The Banking Industry\u2019s $1 Trillion Opportunity,\u201d thefinancialbrand.com, May 29, 2018. https:\/\/thefinancialbrand.com\/72653\/artificial-intelligence-trends-banking-industry\/<\/a>, Accessed November 2018.<\/p>\n [7]<\/a> Vincent Granville, \u201cJ.P. Morgan’s Comprehensive Guide on Machine Learning,\u201d uci.edu, November 21, 2017. http:\/\/faculty.sites.uci.edu\/pjorion\/files\/2018\/05\/JPM-2017-Summary.pdf<\/a>, Accessed November 2018.<\/p>\n [8]<\/a> Vincent Granville, \u201cJ.P. Morgan’s Comprehensive Guide on Machine Learning,\u201d uci.edu, November 21, 2017. http:\/\/faculty.sites.uci.edu\/pjorion\/files\/2018\/05\/JPM-2017-Summary.pdf<\/a>, Accessed November 2018.<\/p>\n [9]<\/a> Vincent Granville, \u201cJ.P. Morgan’s Comprehensive Guide on Machine Learning,\u201d uci.edu, November 21, 2017. http:\/\/faculty.sites.uci.edu\/pjorion\/files\/2018\/05\/JPM-2017-Summary.pdf<\/a>, Accessed November 2018.<\/p>\n [10]<\/a> Konstantin Didur, \u201cMachine learning in finance: Why, what & how,\u201d towardsdatascience.com, July 11, 2018. https:\/\/towardsdatascience.com\/machine-learning-in-finance-why-what-how-d524a2357b56<\/a>, Accessed November 2018.<\/p>\n [11]<\/a> Konstantin Didur, \u201cMachine learning in finance: Why, what & how,\u201d towardsdatascience.com, July 11, 2018. https:\/\/towardsdatascience.com\/machine-learning-in-finance-why-what-how-d524a2357b56<\/a>, Accessed November 2018.<\/p>\n [12]<\/a> Konstantin Didur, \u201cMachine learning in finance: Why, what & how,\u201d towardsdatascience.com, July 11, 2018. https:\/\/towardsdatascience.com\/machine-learning-in-finance-why-what-how-d524a2357b56<\/a>, Accessed November 2018.<\/p>\n [13]<\/a> Vincent Granville, \u201cJ.P. Morgan’s Comprehensive Guide on Machine Learning,\u201d uci.edu, November 21, 2017. http:\/\/faculty.sites.uci.edu\/pjorion\/files\/2018\/05\/JPM-2017-Summary.pdf<\/a>, Accessed November 2018.<\/p>\n [14]<\/a> Lisa Joyce, \u201cArtificial Intelligence and The Banking Industry\u2019s $1 Trillion Opportunity,\u201d thefinancialbrand.com, May 29, 2018. https:\/\/thefinancialbrand.com\/72653\/artificial-intelligence-trends-banking-industry\/<\/a>, Accessed November 2018.<\/p>\n [15]<\/a> Konstantin Didur, \u201cMachine learning in finance: Why, what & how,\u201d towardsdatascience.com, July 11, 2018. https:\/\/towardsdatascience.com\/machine-learning-in-finance-why-what-how-d524a2357b56<\/a>, Accessed November 2018.<\/p>\n","protected":false},"excerpt":{"rendered":" Brief overview of JP Morgan's adoption of machine learning into its day-to-day operations<\/p>\n","protected":false},"author":11343,"featured_media":36252,"comment_status":"open","ping_status":"closed","template":"","categories":[385,2205,264,1215,2510,346],"class_list":["post-35381","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-banking","category-digital-banking","category-finance","category-financial-technlogy","category-jp-morgan","category-machine-learning","hck-taxonomy-organization-jp-morgan","hck-taxonomy-industry-financial-services","hck-taxonomy-country-united-states"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-rctom\/assignment\/rc-tom-challenge-2018\/","yoast_head":"\n