J.P. Morgan: Machine learning in private credit

J.P. Morgan Markets published Machine Learning in Private Credit, a report on how private credit analysts are using machine learning to get data out of non-standard documents and into their models. Our State of Private Credit Technology is quoted in it.

Our research is the survey of around 150 private credit lenders that John Markell (Armentum Partners) and I present each year at The Private Credit Technology Summit. The stat J.P. Morgan used is that adoption of third-party underwriting technology doubled year over year, from about 10% to 20%, with nearly all of that growth coming from AI-native tools. Since switching costs are low for trialing a new product in this category, adoption is moving much faster.

Quote in the report

"When you can ingest a quarterly reporting package, normalize the financials, and flag covenant issues automatically, the analyst's role fundamentally changes. The next frontier is not extraction. It is analysis."

Sarah Gang describes the problem we started Lumonic to solve. The goal, she says, is for credit analysts to get the information they need to do the actual analysis "without needing so many people opening up PDFs and spreading numbers," and her team's target for data extraction is 30 seconds per company, down from about 45 minutes today.

Read the full report on jpmorgan.com or download the PDF directly from J.P. Morgan. Our State of Private Credit Technology research is on the site in two editions, 2026 and 2025.

Thank you to the J.P. Morgan Markets team for including us!