AI in Private Credit: Key Considerations for Market Participants

The private credit market is navigating a complex environment, one in which artificial intelligence (AI) is emerging as a potentially transformative force in how private credit transactions are sourced, evaluated, documented and managed throughout the investment lifecycle. The increased focus on AI coincides with continued growth across the private credit industry, driven by demand for non-bank financing solutions and the deepening sophistication of private debt platforms. Geopolitical and economic dynamics have reinforced the importance of disciplined underwriting, particularly with respect to highly leveraged issuers facing margin compression and refinancing pressure. Meanwhile, deal flow has been uneven, documentation terms are showing early signs of recalibration in favor of lenders, and market participants are balancing substantial available capital against heightened concerns regarding credit quality and enterprise valuations. As these market dynamics intensify, AI adoption is accelerating across the private credit landscape, raising important legal, regulatory, and risk-management considerations for lenders, sponsors and borrowers alike.

Adoption of AI in Private Credit

Over the past year, private credit funds, direct lenders and alternative asset managers have increasingly adopted AI-driven analytics to evaluate borrower performance, identify covenant risks, and assess industry trends across large datasets that historically required substantial manual review. In practice, these technologies have the ability to improve diligence speed and enhance credit decision-making, and lenders that effectively integrate AI into their transaction workflows may gain a meaningful advantage in sourcing and closing opportunities. Notably, sponsors and borrowers are becoming accustomed to accelerated underwriting timelines, and we expect that execution speed will only continue to function as a competitive differentiator among private credit providers.

Legal and Risk-Management Considerations

While the efficiency gains are real, the growing reliance on AI presents a number of legal, regulatory and risk-management considerations that market participants should evaluate carefully.

Data quality and model reliability. AI models remain dependent on the quality, completeness and reliability of underlying data. Overreliance on automated outputs may create exposure where assumptions or datasets prove inaccurate, incomplete or biased. In private credit, this concern is particularly relevant in sectors experiencing volatility or limited transparency, where historical data may not accurately predict future borrower performance. Lenders should ensure that AI-generated credit assessments are subject to meaningful human review and that internal policies clearly delineate the role of automated analysis in credit approval processes.

Documentation and diligence implications. As AI accelerates underwriting timelines, market participants should be attentive to whether compressed execution periods create risks of inadequate diligence or documentation gaps. Lenders relying on AI-driven analysis should confirm that their processes satisfy applicable regulatory expectations regarding credit risk assessment and that AI-assisted outputs are appropriately documented for audit and compliance purposes.

Borrower use of AI. The growing use of AI by borrowers themselves may create challenges for credit providers, particularly in evaluating business models whose revenue generation or operational infrastructure depends substantially on emerging technologies. Lenders should consider whether existing diligence frameworks adequately address AI-related operational risks, intellectual property dependencies and regulatory uncertainties within borrower businesses.

Competitive dynamics and market standards. Sophisticated lenders may increasingly differentiate themselves through proprietary technology platforms capable of delivering enhanced diligence, faster execution and more dynamic risk monitoring across portfolios. As AI adoption becomes more widespread, market participants should monitor whether evolving market standards create new expectations around lender responsiveness, reporting capabilities, or portfolio surveillance that may require investment in technology infrastructure.

Data privacy and confidentiality. Market participants that deploy AI tools to evaluate borrower financials and other confidential information should carefully assess the risk of data leakage, particularly where third-party platforms process or retain inputs in ways that may breach contractual confidentiality obligations or conflict with applicable data privacy regulations. Sensitive borrower or investor information may be inadvertently incorporated into model training datasets or made accessible to other platform users. Lenders should ensure that AI vendor agreements include robust data isolation, non-retention and confidentiality provisions and that internal governance frameworks address compliance with relevant privacy laws.

Looking Ahead

Private credit remains a dynamic and competitive market, with fundraising activity continuing alongside heightened investor scrutiny of track records, portfolio resilience and downside protection. Successful private credit platforms will be those that balance technological innovation with disciplined credit judgment, rigorous legal documentation and careful human oversight. AI is unlikely to replace the core relationship-driven nature of private credit transactions, but it will increasingly shape how transactions are evaluated, documented and managed. Market participants should proactively assess their own AI capabilities and governance frameworks, and should carefully evaluate AI-related risks in both their internal operations and the businesses to which they extend credit. Katten's market-leading Private Credit practice is available to assist market participants across the complex and growing world of private credit, including navigating the legal and regulatory considerations arising from AI adoption.