Source: The National Law Review

The private credit market is experiencing growing integration of artificial intelligence across sourcing, underwriting, documentation, and portfolio management activities. This trend coincides with continued industry growth and shifts in deal flow dynamics, including a recalibration of documentation terms more favorable to lenders and increased attention to disciplined underwriting due to challenging economic conditions for highly leveraged issuers. AI-driven analytics are used to evaluate borrower performance, identify covenant risks, and analyze industry trends from extensive datasets, offering improvements in due diligence speed.

The use of AI raises legal, regulatory, and risk-management considerations for market participants. Key concerns include dependence on the quality, completeness, and reliability of underlying data, with risks posed by inaccurate or biased inputs. Accordingly, AI-generated credit assessments should undergo meaningful human review, and policies need to specify the role of automated analysis in credit approval. Additionally, accelerated underwriting timelines enabled by AI may increase risks of inadequate due diligence or documentation gaps, necessitating compliance with regulatory expectations and proper recordkeeping of AI-assisted outputs. Considerations also extend to challenges from borrower use of AI, intellectual property dependencies, and the need to address data confidentiality and privacy protections, including robust contractual provisions with AI vendors.