The Role of Machine Learning Techniques in Predicting Credit Risk and Default Probability in Banking

Posted: Sep 18, 2023

• Evelyn Perez
• Evelyn Scott
• Evelyn White

Abstract

The accurate prediction of credit risk and default probability represents one of the most critical challenges in modern banking operations. Traditional credit scoring models, primarily based on historical financial data and static risk indicators, have demonstrated limitations in capturing the complex, dynamic nature of borrower behavior and economic fluctuations. The emergence of sophisticated machine learning techniques offers unprecedented opportunities to revolutionize credit risk assessment through advanced pattern recognition, temporal analysis, and multi-dimensional feature engineering. This research introduces a ground-breaking approach that transcends conventional methodologies by integrating quantum-inspired optimization with ensemble learning architectures, creating a predictive framework that adapts to evolving economic conditions while preserving data privacy across institutional boundaries.