Risk Modelling And Corporate... | Advances In Credit

The landscape of credit risk and corporate finance has shifted from static, linear statistical models toward dynamic, AI-driven frameworks. This paper examines the integration of machine learning (ML), the role of alternative data in addressing "thin-file" borrowers, and the critical emergence of Environmental, Social, and Governance (ESG) factors in credit assessments. It highlights how these advances improve predictive accuracy by 10–25% while introducing new challenges in model interpretability and regulatory compliance. 2. Evolution of Modelling Techniques

A major advancement in corporate finance is the move beyond traditional "tradeline" data (credit scores, income, and liabilities). The Use of Alternative Data in Credit Risk Assessment Advances in Credit Risk Modelling and Corporate...

: Techniques like Deep Belief Networks (DBN) and Neural Networks are increasingly used for large, heterogeneous datasets (e.g., transaction records and macroeconomic variables). The landscape of credit risk and corporate finance