: Studies show that ensemble models can reduce misclassification rates by over 25% compared to single-model deployments. 3. The Shift to Alternative Data
: Modern approaches now prioritize ensemble methods like Random Forests , XGBoost , and Gradient Boosting Machines (GBM) . These models excel at capturing non-linear relationships and high-dimensional interactions that traditional models miss. Advances in Credit Risk Modelling and Corporate...
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 : Studies show that ensemble models can reduce
: Techniques like Deep Belief Networks (DBN) and Neural Networks are increasingly used for large, heterogeneous datasets (e.g., transaction records and macroeconomic variables). These models excel at capturing non-linear relationships and
The following is an overview of the core themes and advancements to include in a paper titled This structure reflects recent shifts toward machine learning, the integration of alternative data, and the rising importance of climate-related financial risks. 1. Abstract
Historically, credit risk modelling relied on and Linear Discriminant Analysis (LDA) because of their interpretability and alignment with Basel regulatory rules.
