{"id":35962,"date":"2018-11-13T19:34:20","date_gmt":"2018-11-14T00:34:20","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/jp-morgan-coin-a-banks-side-project-spells-disruption-for-the-legal-industry\/"},"modified":"2018-11-13T19:34:20","modified_gmt":"2018-11-14T00:34:20","slug":"jp-morgan-coin-a-banks-side-project-spells-disruption-for-the-legal-industry","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/jp-morgan-coin-a-banks-side-project-spells-disruption-for-the-legal-industry\/","title":{"rendered":"JP Morgan COIN: A Bank\u2019s Side Project Spells Disruption for the Legal Industry"},"content":{"rendered":"

Ask a young corporate lawyer the most painful part of his job and you\u2019ll probably hear \u201cdoc review.\u201d Document review is the process of lawyers poring over thousands of documents to determine which are relevant for litigation. Rote and time-consuming, this work is mind-numbing for attorneys and expensive for clients. Largely because of this process, McKinsey reports that nearly a quarter of a lawyer\u2019s job can be automated. [1] As a result, many law firms are looking to automate the document review process (top firms already outsource it).<\/p>\n

But incentives are misaligned. Law firms generally bill by the hour, and clients already squeeze them to reduce hours charged. One academic study concluded that just adopting existing machine learning could reduce lawyers\u2019 billable hours by about thirteen percent. [2] Lawyers would make less money in the immediate future as a consequence. A select few clients have instead begun to productize some of the work of their attorneys. JPMorgan has recently emerged as a leader in this trend.<\/p>\n

Last year, JPMorgan announced it had developed and deployed new software called COIN\u2014shorthand for Contract Intelligence\u2014that automates document review for a certain class of contracts. The company first dispatched the program to review thousands of its own credit contracts. The software employs image recognition to identify patterns in these agreements. [3] While JPMorgan has been tight-lipped about the details of the proprietary technology, the bank has stated that the algorithm uses unsupervised learning: by digesting data on the bank\u2019s numerous contracts, it can identify and categorize repeated clauses [4]. The bank reports that the algorithm classifies clauses into one of about one hundred and fifty different \u201cattributes\u201d of credit contracts. [5] For example, it may note certain patterns based on clause wording or location in the agreement.<\/p>\n

The software reviews in seconds the number of contracts that previously took lawyers over 360,000 man-hours. JPMorgan\u2019s economic incentive to develop the product is thus self-evident. But what\u2019s more: the algorithm is more accurate than human lawyers. [6] So the bank\u2019s investment in the technology is not just about costs, but also about quality since COIN improves the accuracy of the contract review process.<\/p>\n

While automated \u201ctechnology-assisted legal review\u201d solutions are not new, JPMorgan benefits from the large scale and low variability it has in credit contracts. The bank processes over 12,000 credit agreements per year, which are far less complex than contracts that might better suit human review, such as custom M&A agreements. [7]<\/p>\n

And the bank appears well suited to expand its use of machine learning in legal work. JPMorgan is already exploring other opportunities to tackle attorney costs. [8] In the short term, it intends to deploy COIN for more complex filings, such as credit-default swaps and custody agreements. [9] In the medium and long term, the bank also hopes to use machine learning to interpret altogether new regulations (questions of \u201cfirst impression,\u201d as lawyers often call them). [10] The idea is to move beyond data classification to data interpretation.<\/p>\n

JPMorgan is solving an important problem for its own business while encouraging a more efficient legal industry. But it should be clear about the type of legal work a machine can and cannot automate\u2014and prioritize its software development accordingly. First, although the use of machine learning for legal work has recently won favor in federal courts, judges and lawyers continue to create a human monopoly on much of their work by requiring that a licensed professional do it. [11] Until algorithms start earning JDs, much of attorneys\u2019 work will be safe. Second, new legal questions do not always fit the mold of past legal doctrine; as technology and business change, so do judicial and regulatory decisions. Third, the law is dynamic: often jurists borrow from one area of the law to confront a new challenge in another. A machine might know everything about contracts law but it takes lateral thinking and reasoning by analogy to predict that a judge is about to borrow a tort law doctrine. In short, it would be a risk to permit a machine to anticipate new rules.<\/p>\n

A better principle is to focus on the litigation and corporate work that is readily automatable\u2014pattern identification and classification\u2014and to delay software development on the more complicated tasks\u2014analysis, interpretation and argumentation. But this author\u2019s aware that a few questions might arise from this recommendation: is this proposed distinction between machine and human work collapsing as machine learning improves? And will companies look to displace law firms or their own in-house counsel with these new technologies?<\/p>\n

(759)<\/p>\n

\u00a0<\/strong><\/p>\n

 <\/p>\n

[1] McKinsey & Company. (2018).\u00a0Harnessing automation for a future that works<\/em>. [online] Available at: https:\/\/www.mckinsey.com\/featured-insights\/digital-disruption\/harnessing-automation-for-a-future-that-works [Accessed 14 Nov. 2018].<\/p>\n

[2] Remus, D. and Levy, F. (2015). Can Robots Be Lawyers? Computers, Lawyers, and the Practice of Law.\u00a0SSRN Electronic Journal<\/em>.<\/p>\n

[3] Megatrends by HP. (2018).\u00a0Bank on AI to shape the future of finance \u2013 Megatrends by HP<\/em>. [online] Available at: https:\/\/hpmegatrends.com\/bank-on-ai-to-shape-the-future-of-finance-93520267e63a [Accessed 14 Nov. 2018].<\/p>\n

[4] J. P. Morgan 2016 Annual Report. [online] Available at: https:\/\/www.jpmorganchase.com\/corporate\/investor-relations\/document\/2016-annualreport.pdf [Accessed 14 Nov. 2018].<\/p>\n

[5] Bestpractice.ai. (2018).\u00a0JPMorgan reduced lawyers’ hours by 36… – AI Case Study JPMorgan<\/em>. [online] Available at: https:\/\/www.bestpractice.ai\/studies\/jpmorgan_reduced_lawyers_hours_by_360_000_annually_by_automating_loan_agreement_analysis_with_machine_learning_software_coin [Accessed 14 Nov. 2018].<\/p>\n

[6] J. P. Morgan 2016 Annual Report. [online] Available at: https:\/\/www.jpmorganchase.com\/corporate\/investor-relations\/document\/2016-annualreport.pdf [Accessed 14 Nov. 2018].<\/p>\n

[7]J. P. Morgan (@jpmorgan) \u201cShowcasing Innovation: A quick look at how $JPM is using machine learning.\u201d Feb 28, 2017, 1:41pm. Tweet. Available at: https:\/\/twitter.com\/jpmorgan\/status\/836692784652435460<\/a><\/p>\n

[8] J. P. Morgan 2016 Annual Report. [online] Available at: https:\/\/www.jpmorganchase.com\/corporate\/investor-relations\/document\/2016-annualreport.pdf [Accessed 14 Nov. 2018].<\/p>\n

[9] Sennaar, Kumba. \u201cAI in Banking – An Analysis of America’s 7 Top Banks.\u201d\u00a0TechEmergence<\/em>, 29 Oct. 2018, www.techemergence.com\/ai-in-banking-analysis\/.<\/p>\n

[10] Son, Hugh. \u201cThis Software Does in Seconds What Took Lawyers 360,000 Hours.\u201d\u00a0The Independent<\/em>, Independent Digital News and Media, 28 Feb. 2017, www.independent.co.uk\/news\/business\/news\/jp-morgan-software-lawyers-coin-contract-intelligence-parsing-financial-deals-seconds-legal-working-a7603256.html.<\/p>\n

[11] Michael Simon, Alvin F. Lindsay, Loly Sosa & Paige Comparato. Lola v. Skadden and the Automation of the Legal Profession<\/em>. 20 Yale J.L. & Tech. 234 (2018).<\/p>\n","protected":false},"excerpt":{"rendered":"

JPMorgan has begun to automate the work of its law firms using machine learning. 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