JPMorgan's COiN (Contract Intelligence) Platform: Using AI in Mergers & Acquisitions and Commercial Lending
(09/2025)
Executive Summary
JPMorgan Chase's Contract Intelligence (COiN) platform represents a landmark achievement in AI implementation within the financial services industry. Launched in 2017, this groundbreaking system transformed the bank's approach to legal document review, especially in commercial lending and mergers & acquisitions, reducing annual processing time from 360,000 man-hours to mere seconds while improving accuracy and enabling strategic applications.
Key Impact Metrics:
Time Reduction: 360,000+ hours saved annually
Processing Capacity: 12,000+ agreements analysed per year
Accuracy Improvement: Higher precision than human lawyers
Cost Savings: Millions of dollars in operational cost reduction
Scalability: Suitable for large-scale document review
This case study aims to inform financial and legal professionals about the impact of AI in these industries by demonstrating a real-life example from a large financial services enterprise such as JPMorgan.
Table of Contents
1. Background and Context
2. The Challenge
3. Solution Overview
4. Technical Implementation
5. Results and Impact
6. Application in M&A
7. Industry Implications
8. Challenges and Limitations
9. Future Outlook
10. Lessons Learned
11. Conclusion
Introduction
In 2017, JPMorgan Chase launched COiN, short for Contract Intelligence, to attack a stubborn, costly bottleneck: the painstaking review of legal agreements. The headline numbers were striking. Work that consumed more than 360,000 human hours each year could be executed in seconds, with higher accuracy and consistency than traditional review teams typically achieved. At a stroke, legal review moved from a laborious task to a data-rich, technology-enabled capability with direct strategic value. For mergers and acquisitions (M&A), where time is compressed, volume of documents are vast and mistakes are expensive, the implications were immediate: faster due diligence, clearer risk mapping and keener pricing of deals. This case study examines why COiN was built, how it works, what has changed inside the bank and the wider lessons for financial services, law and corporate development.
1.Background and Context
JPMorgan Chase is among the world’s largest and most influential financial institutions, operating in over 60 countries with assets exceeding $3.7 trillion. In 2016, the bank formalised a company-wide artificial intelligence push, establishing a Centre of Excellence within Intelligent Solutions and committing a serious budget to move from experimentation to scaled delivery. COiN emerged from this programme as a flagship, practical application of AI to a pervasive problem: extracting structured meaning from unstructured contracts on an industrial scale.
Financial services is, by its nature, data-rich and process-heavy. Before AI, contract analysis, especially of commercial credit agreements and document sets that accompany M&A, was slow, expensive and error prone. Reviewers wrestled with inconsistent templates, variable drafting quality and sheer volume of documents. The business case for automation was not simply a reduction in costs; it was also a reduction in risk. In lending operations, for example, a large share of servicing errors could be traced to misread or inconsistently interpreted clauses. The status quo was barely sustainable with increasing deal flow, regulatory scrutiny and client expectations.
2.The Challenge
Prior to COiN, JPMorgan devoted more than 360,000 hours annually to reviewing upwards of 12,000 commercial agreements. Highly skilled professionals spent too much time on routine clause checks, creating five systemic issues: high costs, sluggish turnaround, hard limits on scalability, inconsistency across reviewers and avoidable mistakes under deadline pressure. In M&A, with numerous contracts, tight timetables and heterogeneous document types, all five problems intensify. The institution needed a way to read everything quickly, remember perfectly and apply rules consistently, while ensuring that humans retained control of judgement calls.
3.Solution Overview
COiN is an AI platform that automates the extraction and analysis of key data points from legal documents. Its objectives are pragmatic and measurable: reduce manual labour, raise accuracy and consistency, shorten cycle times, cut cost, and scale linearly with volume. In practice, that means classifying agreement types, identifying and extracting relevant clauses, surfacing anomalies and risks, and presenting structured outputs, such as summaries, red-flag lists and dashboards, that lawyers, bankers and risk teams can act upon immediately.
“In our pilot program, COiN processed 53% of a sample set of loan applications in line with decisions that we would expect of loan officers. COiN accomplished this in a fraction of the loan processing time that involves humans. Another 25%-30% of the loans in our sample set can be shunted to COiN with limited human supervision as we train ourselves in using this tool more effectively.” — Matt Zames, JPM COO, April 2017
4.Technical Implementation (How COiN works)
COiN combines natural language processing (NLP) with pattern detection, automated clause classification and image recognition to cope with messy scans and varied layouts. It can extract more than 150 attributes from commercial lending and M&A agreements, recognise recurring structures and parse the phrasing patterns that lawyers and bankers care about. Notably, it employs unsupervised machine-learning approaches to discover patterns without requiring exhaustive manual labelling, a pragmatic choice when facing unusual legacy documents at scale.
Operationally, documents are ingested, converted and pre-processed; features are extracted; clauses are classified; potential issues are scored; and the platform generates structured outputs for human review. The system runs on JPMorgan’s private cloud, Gaia, which provides the compute elasticity and security posture required for regulated workloads and integrates with existing internal systems so outputs land where case teams already work. Continuous improvement is made by performance monitoring, user feedback and updates that reflect new contract types and regulatory change.
5.Results and Impact
The quantitative gains are significant. Each year, more than 360,000 hours of manual review effort are eliminated; thousands of contracts are analysed in seconds; over 12,000 commercial credit agreements can be processed; and operational costs fall by millions of dollars. Accuracy improves, not because machines are “smarter”, but because they apply the same rules, the same way, every time. Consistency goes up significantly; human error goes down; and availability becomes 24/7 rather than bound by office hours.
“Our loan service error rate is less than a tenth of what it used to be a decade ago thanks to COiN and updated processes.” — Sri Shivananda, JPM CTO, Q3 2024 Earnings Update
Qualitatively, the work changes. Legal professionals shift from primary readers to exception handlers and strategic advisers. Client service improves because answers arrive sooner and are more complete. Risk management becomes more proactive: issues are flagged early and systematically, rather than discovered late and anecdotally. Internally, knowledge that once lived tacitly in people’s heads becomes explicit, searchable and reusable through the platform.
6.Application in M&A
M&A due diligence is the archetypal stress-test for any contract-intelligence system: enormous volumes, compressed timelines, multiple jurisdictions and the unhelpful fact that the cost of an oversight can be measured in percentage points of deal value. COiN’s contributions are clearest in three phases:
Pre-transaction scanning. Buyers can triage a target’s contract estate quickly, identify potential deal-breakers and focus scarce human attention where it counts: concentrations of risk, unusual clauses or conditions that could undermine value capture.
Execution-phase diligence. Instead of armies of reviewers trawling agreements, COiN parses them at machine speed, extracts key terms (termination rights, indemnities, transferability, renewal and pricing), flags anomalies and buckets issues for targeted follow-up. Outputs are structured and comparable across counterparties and contract types, enabling a genuinely risk-based approach to human review.
Post-merger integration. After deal close, the same system can support contract harmonisation, identify consolidation opportunities across customer and vendor lists, and monitor compliance obligations in the combined entity. What was once a one-off “data room ordeal” becomes an ongoing portfolio capability.
At document level, the system can accelerate analysis of asset purchase agreements (representations and warranties, indemnities, closing conditions), employment contracts (change-of-control, retention and restrictive covenants) and customer and vendor agreements (assignment restrictions, term and renewal mechanics). In each case, speed and completeness reduce execution risk and external legal spend, while sharpening the commercial levers available to the deal team.
Why it is faster - and often better - than manual review
Three features, in combination, explain the improved performance:
Bulk parsing and structured extraction. COiN ingests entire contract databases, extracting standard fields and clause classifications across thousands of documents in a single sweep. Deal teams get comprehensive coverage at the start of diligence, not halfway through it.
Automated risk and anomaly detection. The platform flags outliers, inconsistencies and problematic provisions, directing human expertise to where it is most valuable. Under deadline pressure, this triage substantially reduces the chance of missing material issues.
Consistency and precision under pressure. Machines do not have tired Fridays. Uniform application of rules reduces reviewer variability and makes outputs easier to compare across counterparties and templates, improving auditability and stakeholder confidence.
The net effect is a competitive edge: quicker closings, fewer surprises, lower external fees, and more senior time spent on negotiation, valuation and structuring rather than data gathering.
Change management and operating-model shifts
Deploying an AI platform rarely succeeds as a “technology drop”. COiN’s impact depended on changing roles, incentives and workflows. Lawyers and deal teams had to trust machine outputs but also understand their limits; managers had to redesign processes to put automation up-front rather than downstream; and training had to equip staff to operate as reviewers-in-chief rather than first-pass readers. Over time, this human-AI collaboration model becomes the norm: people supply judgement, context and negotiation skills, while the system provides speed, coverage and consistency.
7.Industry Implications (Law and Banking)
Platforms like COiN are reshaping legal services. Routine review work is increasingly automated, pressuring the billable-hours model and forcing firms to differentiate on strategic advice, negotiation and complex judgement. Market research widely cited in the sector suggests that a non-trivial fraction of legal tasks can be automated or significantly reshaped by AI. The upshot is not the end of lawyers, but the end of a certain kind of legal work as a reliable revenue stream. For banks, first movers build moats: clients get faster service and more transparent risk analysis; internal teams get cleaner workflows; regulators get better audit trails. The talent profile shifts towards AI-literate professionals who can partner with machines - designing checks, interpreting outputs and exercising judgement.
Economically, benefits compound. Lower transaction costs and shorter cycles increase deal throughput; better risk capture reduces remediation and litigation; institutional knowledge, once fragmented, becomes an asset in its own right. Beyond finance, law schools and business programmes are adapting curricula to reflect data-driven practice and human-AI collaboration as baseline skills.
8.Challenges and Limitations
No AI system abolishes nuance. COiN performs best on standardised instruments with regular patterns. Bespoke M&A agreements, drafted at speed by imaginative counsel, can stretch any model, particularly where meaning depends on context spread across multiple documents. Also, because AI learns from historical data, there is always the risk of amplifying biases or over-fitting to familiar patterns.
Governance is therefore central. In a regulated industry, risk management, explainability and robust validation frameworks are not optional extras. Institutions must be able to explain how the system reached its conclusions, protect client data and define professional accountability for AI-assisted work. There are ethical questions, too: the displacement of routine roles; potential concentration of capabilities in large institutions; the need to be transparent with clients about how their documents are analysed. Internally, resistance to change, training requirements and integration complexity all need active management.
9.Future Outlook
The technology frontier is moving quickly. Expect richer multi-modal analysis (text, images, tables and even handwritten mark-ups), predictive analytics (estimating the likelihood a clause triggers), and real-time assistance during live negotiations. Contract generation and negotiation support tools are maturing alongside analysis engines, closing the loop from drafting to diligence to ongoing portfolio management. Cross-jurisdictional intelligence is likely to improve, helping global deal teams navigate divergent legal regimes without spawning parallel processes.
“COiN depended a lot on competent prompts and set up in its early days. Now the tool is more robust, assumes and fills in the blanks and corrects inputs more effectively, and needs less training of its users.” — ex-FIG Deal Generation Team Associate, Current JPM Banking Analyst, September 2025
On the market side, adoption is broadening. AI-assisted document analysis is fast becoming standard across financial services, with sector-specific variants and tighter integration into everyday legal and banking software. As capabilities consolidate, platforms will embed multiple AI functions; open-source components will accelerate innovation; and dedicated “reg-tech” for audit and supervision will grow. Regulators are already strengthening expectations for model oversight, auditability and professional standards appropriate to AI-powered processes. Industry bodies will respond with best-practice frameworks, interoperability standards and ethical guidelines.
10.Lessons Learned
“Over the years, LLMSuite, SNAP, QCI, COiN, ACOE and other proprietary AI tools we develop have benefited substantially from the lessons we learned in the less successful programs that we shut down.” — Lori Beer, JPM Chief Information Officer, 2024 Company presentation
A few principles stand out from COiN’s trajectory and are instructive for any organisation contemplating similar transformations:
Start with a sharp problem. COiN targeted a measurable, high-friction process with clear success metrics. Vague ambitions to “use AI” are rarely successful.
Invest in data and integration. Model performance depends on data quality. Just as crucial is integration: AI must sit inside existing workflows, not beside them.
Treat change as a first-class workstream. Communication, training and staged roll-outs matter as much as model performance. People need to know what the machine will do, what they remain accountable for and how their roles will evolve.
Build robust model-risk management. Validation, bias monitoring, scenario testing and fallbacks are essential in production. One must be able to defend the system to auditors and to clients.
Sustain leadership commitment. AI transformation is a multi-year journey requiring executive sponsorship and patient capital; pilots are easy, production requires culture-change.
11.Conclusion
COiN is a concrete proof-point that AI, thoughtfully implemented, can deliver speed and substance simultaneously. By replacing hundreds of thousands of hours of manual review with seconds of machine analysis, while improving accuracy, JPMorgan has speeded up how it conducts due diligence and manages contractual risk. In M&A, in particular, where deal speed, depth of insight and error avoidance determine outcomes, that shift is transformative. The lesson is not that machines replace lawyers or deal teams, but that they change what those professionals spend their time on. The routine gets automated; the judgement gets elevated. Institutions that embrace this division of labour and back it with robust governance, will move faster, negotiate better and carry fewer surprises into closing of transactions. COiN offers a credible blueprint for that future.
References and Further Reading
Primary Sources
JPMorgan Chase Annual Reports (2016-2024)
JPMorgan Technology and AI Research Publications
Federal Reserve and OCC Banking Technology Guidelines
Industry Analysis
McKinsey & Company: "AI in Financial Services" Reports
Forrester Research: "AI Transformation in Banking"
Goldman Sachs: "AI Impact on Financial Services Employment"
Academic Research
Harvard Business School: "AI Implementation in Financial Services"
MIT Sloan: "Machine Learning in Banking Operations"
Stanford AI Lab: "Natural Language Processing in Legal Documents"
Regulatory Guidance
Federal Reserve: "Model Risk Management Guidance"
Office of the Comptroller of Currency: "Banking Technology Guidelines"
European Central Bank: "AI in Financial Services Supervision"
Sources
The Independent – “COiN cut down 360,000 lawyer-hours a year” https://www.independent.co.uk/news/business/news/jp-morgan-software-lawyers-coin-contract-intelligence-parsing-financial-deals-seconds-legal-working-hours-360000-a7603256.html
JPMorgan Chase – 2016 Annual Report (COiN processed 12,000 contracts, extracted 150 attributes) https://reports.jpmorganchase.com/investor-relations/2016/ar-ceo-letters.htm
Harvard RC-TOM – “JPMorgan COiN: A Bank’s Side Project Spells Disruption for the Legal Industry” https://d3.harvard.edu/platform-rctom/submission/jp-morgan-coin-a-banks-side-project-spells-disruption-for-the-legal-industry
GoBeyond.ai – “JPMorgan COiN: AI Contract Analysis with NLP, Speed, and Accuracy” https://www.gobeyond.ai/ai-resources/case-studies/jpmorgan-coin-ai-contract-analysis-legal-docs
BestPractice.ai – “JPMorgan reduced lawyers’ hours by 360,000 annually by automating loan agreement analysis with COiN” https://www.bestpractice.ai/ai-case-study-best-practice/jpmorgan_reduced_lawyers%27_hours_by_360%2C000_annually_by_automating_loan_agreement_analysis_with_machine_learning_software_coin
Futurism – “An AI completed 360,000 hours of finance work in just seconds” https://futurism.com/an-ai-completed-360000-hours-of-finance-work-in-just-seconds
DigitalDefynd – “JPMorgan Using AI: Deployment Efficiency and Clause Detection” https://digitaldefynd.com/IQ/jp-morgan-using-ai-case-study
Product Monk – “Meet COiN: JPMorgan’s Efficiency Wizard” https://www.productmonk.io/p/meet-coin-jpmorgan-s-efficiency-wizard
Superior Data Science – “JPMorgan COiN: A Case Study of AI in Finance” https://superiordatascience.com/jp-morgan-coin-a-case-study-of-ai-in-finance
LinkedIn (Jorge Chirinos) – “Case Study: JPMorgan Chase’s Contract Intelligence (COiN)” https://www.linkedin.com/pulse/case-study-jpmorgan-chases-contract-intelligence-coin-jorge-chirinos-qcyje
Welflab – “Revolutionizing Financial Legal Services: JPMorgan Chase’s AI-powered Contract Analysis” https://welflab.com/revolutionizing-financial-legal-services-jp-morgan-chases-ai-powered-contract-analysis
ICMA – “Tracker of New Fintech Applications in Bond Markets” https://www.icmagroup.org/fintech-and-digitalisation/fintech-resources/tracker-of-new-fintech-applications-in-bond-markets
M&A Community – “AI in M&A Due Diligence” https://mnacommunity.com/insights/ai-in-ma-due-diligence
Greenwich Capital – “The Ultimate Guide to Due Diligence” https://greenwichcapital.com.au/insights/ultimate-guide-to-due-diligence
Admincontrol – “Technology Powering the M&A Process of the Future” https://admincontrol.com/insights/technology-powering-the-ma-process-of-the-future
Deloitte WSJ – “Improving M&A Value Through Due Diligence and Finance Transformation” https://deloitte.wsj.com/cfo/improving-m-a-value-through-due-diligence-and-finance-transformation-01671429833
Drooms – “AI in Merger and Acquisition Transactions” https://drooms.com/fr/blog/intelligence-artificielle/ai-in-merger-and-acquisition-transactions
STP.one – “M&A Due Diligence in Days, Not Months: AI-powered Contract Intelligence” https://www.stp.one/en/blog/ma-due-diligence-in-days-not-months-ai-powered-contract-intelligence
Sirion.ai – “Contracts in Mergers & Acquisitions” https://www.sirion.ai/library/contracts/merger-acquisition-contracts
Execo – “The Hidden Cost of Manual Contract Review in M&A” https://www.execo.com/blog/the-hidden-cost-of-manual-contract-review-in-ma