KBRA Releases Research – Private Credit: Deep Dive on AI and Software

Why It Matters

The findings signal that AI will not destabilize private‑credit markets but will require targeted risk monitoring for vulnerable, sponsor‑backed loans, guiding lenders and investors in portfolio allocation.

Key Takeaways

• AI risk to software firms deemed diffuse, manageable
• Direct lenders face limited exposure to AI-driven defaults
• Sponsor-backed borrowers with near-term maturities under pressure
• Overall private‑credit default rate expected modest increase
• Most software‑adjacent borrowers retain strong credit fundamentals

Pulse Analysis

Artificial intelligence is reshaping the technology landscape, prompting credit rating agencies to reassess risk models across sectors. In private credit, AI’s rapid adoption creates both opportunities and uncertainties for lenders. Direct lenders, who often hold senior positions in software company capital structures, are now evaluating how AI‑driven product cycles, talent shortages, and competitive pressures could affect cash flows. KBRA’s analysis suggests that while AI introduces new variables, the aggregate credit exposure remains diffuse, allowing traditional underwriting frameworks to adapt without major overhauls.

KBRA’s report highlights a nuanced risk profile: sponsor‑backed borrowers with short‑term maturities and direct reliance on AI technologies face the steepest headwinds. These entities may experience revenue volatility as AI integration costs rise and market adoption timelines shift. Consequently, KBRA anticipates a modest uptick in default rates within the private‑credit segment, though the increase is contained. The agency stresses that most software‑adjacent borrowers retain solid balance sheets and diversified revenue streams, mitigating systemic risk. This differentiation underscores the importance of granular credit analysis rather than blanket assumptions about AI’s impact.

For investors and portfolio managers, the research offers actionable insight. Monitoring covenant structures, maturity schedules, and AI exposure levels can help isolate higher‑risk positions. Lenders may consider tightening covenants or requiring additional equity cushions for at‑risk borrowers, while still capitalizing on AI‑driven growth prospects in the broader market. As AI continues to mature, credit strategies that blend rigorous risk assessment with strategic exposure are likely to outperform, reinforcing the sector’s resilience amid technological disruption.