Credit Risk Assessment in the Chilean Retail Banking Sector: A Machine Learning Approach

Abstract

This article explores credit risk assessment in the Chilean retail banking sector using machine learning techniques. It examines the effectiveness of various algorithms, including logistic regression, decision trees, and neural networks, in predicting default probabilities and enhancing credit decision-making processes. The study employs a comprehensive dataset comprising customer profiles, transaction histories, and macroeconomic indicators to train and validate the models. Key findings demonstrate that machine learning approaches can significantly improve accuracy in credit risk predictions compared to traditional methods, leading to better risk management and reduced default rates. The article discusses the implications of these findings for retail banks in Chile, advocating for the adoption of advanced analytics to optimize credit assessments and enhance overall portfolio performance. Additionally, it addresses challenges such as data privacy and model interpretability, emphasizing the importance of ethical considerations in implementing machine learning solutions in banking.