Contract Intelligence (COiN) – A JP Morgan Case Study

Published by FirstAlign

When one thinks of the legal profession, one cannot but miss the massive paperwork involved. Document review is one of its pain points. It is mundane, time-consuming, and prone to error. 

But many industries and businesses spend a considerable amount of their resources, money, and time on document review until one bank decided to set the standard. JP Morgan builds a solution – COiN, short for Contract Intelligence.

The Problem

JP Morgan Chase & Co is an American multinational investment bank and financial services headquartered in New York City. It is the largest bank in the United States and the seventh-largest in the world, with a total asset of US$3.213 trillion.

For banks, document review is an essential part, and they hire lawyers to do the job. JP Morgan spent about 360,000 hours for document review under manual process. It is costly as lawyers charge by the hour, and it takes time. Can technology help improve the process?

An academic study reports that machine learning can reduce a lawyer’s billable hours by 13 percent. JP Morgan emerged as a pioneer in showing firms how it is done. 

Building Solution

JP Morgan wanted to save the hours spent on document review to seconds using technology. They came up with a solution – Contract Intelligence (COiN).  COiN automates document review to extract meaningful information.

COiN uses unsupervised machine learning. Unsupervised learning is a kind of machine learning that looks for undetected patterns in the data set with minimum human intervention. It looks for patterns with no pre-existing labels.

COiN identifies and categorizes repeated clauses based on location and wordings in the contract. Each clause is classified into the 150 different attributes of credit contracts. The first stage of testing included the review of the bank’s credit contracts. The bank’s private cloud network powers the proprietary technology.

The machine learning technology can review 12,000 credit agreements a year.  What took 360,000 hrs a year is now reduced to seconds.  Apart from the visible benefits of cost and time, COiN’s document review has greater accuracy than the manual process.

Conclusion

COiN’s success has led JP Morgan to explore other areas to implement the technology. JP Morgan intends to use COiN for more complicated filings. The idea is to move from data classification to data interpretation.

The successful implementation of machine learning to automate legal tasks has also opened up a new outlook on ways AI can be used in other traditional areas, such as legal systems.

As a global leader, JP Morgan has always embraced the latest technology. Their commitment to automate mundane tasks using AI is part of the firm’s $9.6 billion technology budget. Their innovation in improving processes has already set business standards in the industry.

The future of AI in banking is expanding, and with banks like JP Morgan relentlessly pursuing it, it will only make the drive exciting. 

References

[Featured image by Ag Ku on Pixabay]

Click here to connect with us

Spread the word

Related posts