Machine Learning Algorithms for Credit Risk Assessment in Financial Markets: A Comparative Study of Gradient Boosting and Neural Networks

Abstract

The accurate assessment of credit risk remains a cornerstone of financial stability and profitability for lending institutions globally. As the volume of transactional data expands and the complexity of financial behaviors increases, traditional statistical methods such as logistic regression often fail to capture the non-linear intricacies inherent in modern credit datasets. This paper presents a comparative analysis of two dominant machine learning paradigms: Gradient Boosting Machines, specifically the XGBoost implementation, and Artificial Neural Networks. Utilizing a comprehensive dataset of consumer loans, we evaluate these models based on predictive accuracy, computational efficiency, and interpretability. Our findings indicate that while both methodologies significantly outperform traditional baselines, they exhibit distinct advantages depending on the operational constraints. Gradient boosting demonstrates superior performance on tabular data with faster training times and greater interpretability through feature importance analysis. Conversely, neural networks show potential for capturing highly complex, high-dimensional interactions, albeit at a higher computational cost. The study concludes that the choice between these algorithms should be dictated by the specific requirements of the financial institution regarding the trade-off between predictive precision and model transparency.