AI-Enhanced Credit Risk Prediction for Structured Finance Instruments: RMBS, CMBS, ABS & CLOs

Résumé fourni par la source

Structured finance instruments, such as residential mortgage-backed securities (RMBS), commercial mortgage-backed securities (CMBS), asset-backed securities (ABS), and collateralized loan obligations (CLOs), pose unique credit risk modeling challenges due to pool heterogeneity, tranching, structural features (senior/subordinated), prepayment/default linkages, and sparse loss event data. Recent advances in artificial intelligence (AI) and machine learning (ML) have promised to augment traditional risk models by leveraging high-dimensional data, nonlinear relationships, and alternative data sources. This review surveys the state-of-the-art in AI applications to credit risk within the structured finance domain, critically reviews the methodologies used, identifies the gaps relative to structured finance use-cases, and proposes a conceptual framework for integrating these models in investment-monitoring contexts. We find that while many studies address borrower/retail credit risk broadly, few directly apply to securitized pools. Moreover, issues of interpretability, data scarcity, tranche-level modelling, and regulatory model-risk remain. We highlight how integrating deal-level features (e.g., collateral vintage, securitization waterfall, servicer behavior, prepayment vectors) and newer AI methods (such as explainable AI and large language model (LLM) document ingestion) can help bridge this gap.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.