Private credit has expanded rapidly, increasing pressure on underwriting standards, portfolio monitoring, and risk oversight. AI is increasingly used to address these pressures, but its role must be clearly defined.
This article defends the thesis that AI improves private credit investing by strengthening underwriting discipline, improving portfolio monitoring, and supporting risk identification without reducing accountability.
Underwriting Discipline
Underwriting quality in private credit depends on consistency. Covenant structures, leverage assumptions, cash flow forecasts, and downside cases must be evaluated with the same rigor across deals.
AI supports this by enforcing structured analysis. It ensures that key components are reviewed systematically and that assumptions are explicitly stated. This reduces dispersion in underwriting quality caused by time pressure or team workload.
Portfolio Monitoring
Once capital is deployed, monitoring often becomes reactive. Financials are reviewed periodically, but changes in performance may go unnoticed until they become material.
AI systems can track financial data, covenant metrics, and qualitative disclosures continuously. This improves visibility without increasing manual effort. Monitoring improves not because AI predicts outcomes, but because it highlights deviations earlier.
Risk Identification
Credit risk rarely appears suddenly. It develops through small changes in cash flow, margins, working capital, or management behavior.
AI can surface these changes consistently across a portfolio, allowing human decision makers to intervene earlier. The responsibility for action remains human. AI improves signal detection, not judgment.
Conclusion
AI improves private credit investing by reinforcing discipline and visibility. It does not eliminate risk, but it reduces blind spots. Used correctly, AI strengthens the foundations of underwriting and monitoring without weakening accountability.