Machine learning models for credit default prediction

Abstract

Prediction of credit default is a vital part of financial risk management, especially in an age when data is becoming more available and borrowers are changing their behavior. This paper focuses on the use of machine learning models in credit default forecasting, including how the models can enhance predictive accuracy, robustness, and efficiency in decision making over traditional statistical models. Based on various datasets that contain demographic, financial, and behavioral variables, the research compares various algorithms: Logistic Regression, Random Forest, Gradient Boosting, and Artificial Neural Networks.
The results show that ensemble and non-linear machine learning models are much better than the traditional ones in terms of classification precision, recall, and accuracy and area under the curve (AUC), which is in line with previous research (Lai, 2020; Moscatelli et al., 2020; Alonso Robisco and Carbo Martinez, 2022). Moreover, explainable AI methods are integrated to improve the transparency of the model, which could be used to resolve the main regulatory and ethical issues related to black-box models (Zhu et al., 2023). The research also notes the increased relevance of other types of data, such as user-generated and behavioral data, in enhancing predictive performance (Kriebel and Stitz, 2022).
Comprehensively, the study proves that machine learning-based credit scoring models can be used as a more flexible and data-driven approach to evaluating default risk, with important implications to financial institutions, fintech applications, and policy regulators. It is suggested to use hybrid and interpretable models and continuously monitor the model risk in dynamic financial settings.