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

Historically, credit risk modelling relied on and Linear Discriminant Analysis (LDA) because of their interpretability and alignment with Basel regulatory rules.

: Techniques like Deep Belief Networks (DBN) and Neural Networks are increasingly used for large, heterogeneous datasets (e.g., transaction records and macroeconomic variables).

: Studies show that ensemble models can reduce misclassification rates by over 25% compared to single-model deployments. 3. The Shift to Alternative Data

Advances In Credit Risk Modelling And Corporate... Apr 2026

Historically, credit risk modelling relied on and Linear Discriminant Analysis (LDA) because of their interpretability and alignment with Basel regulatory rules. 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

: Studies show that ensemble models can reduce misclassification rates by over 25% compared to single-model deployments. 3. The Shift to Alternative Data : Studies show that ensemble models can reduce