ICRA Analytics

Credit risk frameworks vary significantly across corporate wholesale lending and project finance. Defaults are rarer, exposures are long‑dated, and structure often matters more than historical outcomes. The question, then, is whether model risk management should adapt while remaining rigorous and regulatory‑defensible. In low‑default portfolios, traditional outcome‑heavy validation often struggles to generate meaningful insight. The issue is not lack of testing, but reliance on metrics that were never designed for such environments. A more effective approach shifts the focus from default prediction to conceptual soundness, structural logic, stability, and embedded conservatism under plausible stress scenarios. Proxy‑based analysis, benchmarking, and strong governance play a much larger role here. When actual default data is sparse, how judgment is applied, challenged, and documented becomes central to credibility. How is your institution adapting model validation for low‑default portfolios? Share your perspective or save this if model risk is on your agenda.