Enhancing Credit Scoring Models with Artificial Intelligence: A Comparative Study of Traditional Methods and AI-Powered Techniques

Abstract

This study compares traditional credit scoring techniques with artificial intelligence (AI) methodologies to investigate how credit scoring models have evolved. Logistic regression and linear discriminant analysis are two statistical models that have been widely used in traditional credit scoring. While these models are reliable, they frequently have difficulty capturing complex, non-linear data patterns. Artificial intelligence (AI)-based approaches, which include machine learning algorithms like ensemble methods, decision trees, and neural networks, offer a sophisticated substitute by efficiently handling big information and identifying intricate patterns.
This research uses data from a large financial organization to compare several ways based on how effective, predictable, and able they are to manage different types of data. To assess the efficacy of the models, critical performance metrics such as the F1-score, precision, recall, and area under the receiver operating characteristic (ROC) curve are employed. As evidenced by the data, AI-driven techniques can revolutionize credit scoring procedures as they perform better in predicted accuracy and resilience than conventional models. In order to enhance decision-making processes, financial institutions must adopt these cutting-edge techniques, as this research highlights the revolutionary impact of AI on assessing financial risk.