Contract intelligence for commercial lending
The problem
Interpreting commercial-loan agreements consumed enormous, expensive lawyer and loan-officer time and was prone to human error.
The AI approach
COiN (“Contract Intelligence”) uses machine learning and natural-language processing (with image recognition) to extract ~150 standardised attributes from credit agreements in seconds, on the bank’s private cloud, feeding outputs into existing workflows.
Evidence it works
Bloomberg reported COiN eliminated ~360,000 hours of annual manual review, processing ~12,000 commercial credit agreements in seconds, and reduced loan-servicing errors attributable to human mistakes.
What “good” looks like
Faster review, consistent rule application, fewer servicing errors, and lawyers redeployed to higher-value judgement work.
Feasibility & cost shape
Significant up-front modelling and integration; pays off where document volume is high and contract structure is repeatable.
The canonical document-intelligence case. The transferable principle is to automate the standardised 80% and route the ambiguous tail to people — not to aim for full automation.
Based on publicly reported information about the JPMorgan Chase (COiN) work.
This is an industry example included for illustration. It is not a Leia Intelligence project, and no client of ours is implied. Figures are as publicly reported by the original parties.
Sources: Bloomberg ("JPMorgan Software Does in Seconds What Took Lawyers 360,000 Hours") · The Independent · JPMorgan 2016 Annual Report