JP Morgan COIN (Contract Intelligence)

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A JP Morgan COIN (Contract Intelligence) is a 1st-party legal contract review application that is machine learning-powered document review system developed by JP Morgan to automate the interpretation of commercial loan agreements.

References

2024

  • (ChatGPT-OpenAI, 2024) ⇒ Prompt: Summarize the concept JP Morgan COIN (Contract Intelligence) in 1- 2 paragraphs. ChatGPT (model:gpt-4-turbo-preview). Retrieved: 2024-05-25.
    • QUOTE: JP Morgan COIN (Contract Intelligence) is an innovative technology platform developed by JPMorgan Chase, one of the world's leading multinational banks. Launched to streamline and enhance the processing and analysis of legal documents, COIN leverages the power of artificial intelligence (AI) and machine learning (ML) to automate the labor-intensive task of reviewing and interpreting commercial loan agreements, a process that traditionally involved a significant amount of manual labor and was prone to human error. The platform has the capability to rapidly sift through vast volumes of data, extracting relevant information and clauses, thereby reducing the time required to complete such tasks from thousands of man-hours to a matter of seconds.

      The introduction of JP Morgan COIN not only signifies a major leap forward in the financial industry's application of AI but also sets an example for how large corporations can leverage technology to optimize operational efficiencies and reduce costs. By automating routine tasks, COIN enables lawyers and financial professionals at JP Morgan to focus on more strategic aspects of their work, enhancing customer service and decision-making processes. Additionally, its success illustrates the potential of AI in transforming the landscape of the industry, offering insights into future technological advancements and their practical applications in business processes.

2023

  • (Dignan, 2023) ⇒ Larry Dignan (2023). "JPMorgan Chase: Digital Transformation, AI, and Data Strategy Sets Up Generative AI". In: Constellation Research.
    • NOTE: It describes JPMorgan Chase's $15.3 billion technology investment in 2023, illustrating the bank's commitment to technological advancement.
    • NOTE: It highlights the use of multiple data platforms, including Databricks, Snowflake, and MongoDB, as well as internal platforms like JADE and Infinite AI.
    • NOTE: It details the bank's efforts to modernize its infrastructure, decommissioning 2,500 legacy applications and moving 60% of in-scope applications to new data centers.
    • NOTE: It mentions the employment of over 900 data scientists, 600 machine learning engineers, and a 200-person AI research team, emphasizing the scale of the bank's AI initiatives.
    • NOTE: It emphasizes JPMorgan Chase's responsible approach to AI, including ethics, compliance, and building trust with customers.

2019

  • (Sullivan, 2019) ⇒ Casey C. Sullivan (2019). "Machine Learning Saves JPMorgan Chase 360,000 Hours of Legal Work". In: FindLaw.
    • QUOTE: ... The lawyer-eliminating program, called COIN for "Contract Intelligence" automates the interpretation of commercial loan agreements, a "mind-numbing job" that would normally be handled by attorneys and loan officers, according to Bloomberg News.

      "The software reviews documents in seconds, is less error-prone and never asks for vacation," Bloomberg writes.

      The program relies on machine learning algorithms, learning "by ingesting data to identify patterns and relationships." It's part of what Bloomberg describes as "a new era of automation," one that's "now in overdrive as cheap computing power converges with fears of losing customers to startups." ...

2018

  • (Legal ML, 2018) ⇒ Legal ML (2018). "JP Morgan COIN: A Bank’s Side Project Spells Disruption for the Legal Industry". In: Assignment: RC TOM Challenge 2018, Harvard Business School.
    • QUOTE: Ask a young corporate lawyer the most painful part of his job and you’ll probably hear “doc review.” 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’s job can be automated. [1] As a result, many law firms are looking to automate the document review process (top firms already outsource it).

      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’ 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.

      Last year, JPMorgan announced it had developed and deployed new software called COIN—shorthand for Contract Intelligence—that 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’s 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 “attributes” of credit contracts. [5] For example, it may note certain patterns based on clause wording or location in the agreement.

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

      While automated “technology-assisted legal review” 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] ...

2017a

  • (Generak Counsel News, 2017) ⇒ https://generalcounselnews.com/jpmorgan-software-does-in-seconds-what-took-lawyers-360000-hours/ 2017-March-1
    • QUOTE: ... A new JPMorgan Chase & Co. a learning machine called COIN, for Contract Intelligence, is parsing financial deals that once kept legal teams busy for thousands of hours, according to a Bloomberg report.

      The company uses the technology to interpret commercial-loan agreements that formerly consumed 360,000 hours of work each year by lawyers and loan officers. The software reviews documents in seconds, is less error-prone and never asks for vacation, writes Hugh Son. ...

2017b

  • (Son, 2017) ⇒ Hugh Son (2017). "JPMorgan software does in seconds what took lawyers 360,000 hours". In: The Independent.
    • QUOTE: At JPMorgan, a learning machine is parsing financial deals that once kept legal teams busy for thousands of hours.

      The program, called COIN, for Contract Intelligence, does the mind-numbing job of interpreting commercial-loan agreements that, until the project went online in June, consumed 360,000 hours of lawyers’ time annually. The software reviews documents in seconds, is less error-prone and never asks for vacation.

      While the financial industry has long touted its technological innovations, a new era of automation is now in overdrive as cheap computing power converges with fears of losing customers to startups. Made possible by investments in machine learning and a new private cloud network, COIN is just the start for the biggest U.S. bank. The firm recently set up technology hubs for teams specializing in big data, robotics and cloud infrastructure to find new sources of revenue, while reducing expenses and risks.

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