Private credit’s machine learning moment

As private credit scales, analysts can turn to machine learning to extract, standardize, and validate data from non-standard documents faster. Explore how predictive models, alternative datasets, and human oversight are changing approaches to risk assessment.

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Highlights

Private credit analysis can involve extensive manual review, as analysts work through complex documentation, financial statements, and borrower data. Machine learning can reduce time spent on data extraction and help teams focus on credit judgment, probability of default, and earlier risk signals—but accuracy, explainability, data security, and human oversight remain central to trust.

$3.5tn

estimated value of the private credit sector in 2024.1

18%

of traders say machine learning will have the greatest influence on trading over the next three years.2

67%

of lending professionals say alternative data has increased confidence in supported decisions.3

“Credit analysis is still, at its core, a judgment exercise. Machine learning can process large volumes of information, but it does not understand context the way an experienced credit professional does.” 

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Gabriele Butti

Markets and Economy

Machine Learning in Private Credit

Sep 01, 2026

The new modeling frontier driven by cutting-edge tech.

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Gabriele Butti and Sarah Gang

17:49 - Markets and Economy

Machine learning, guided by expert human judgment

Sep 01, 2026

Watch as J.P. Morgan's Sarah Gang, Global Head of Underwriting, Credit Financing, and Gabriele Butti, Global Head of Credit Quantitative Research, explore the acceleration in use of machine learning, and why human oversight still matters.

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References

2.

J.P Morgan Markets e-Trading Survey 2026

3.

LexisNexis Risk Solutions 2025 Alternative Credit Data Impact Report - Alternative Credit Data