Artificial Intelligence and Machine Learning in Credit Risk Assessment: Enhancing Accuracy and Ensuring Fairness

Credit risk assessment has become one of the major concerns in modern finance regarding informed lending decisions. Although several studies have used traditional logistic regression and linear discriminant analysis techniques, these have increasingly become inadequate tools in today’s complex and data-rich environment. Such models often struggle with large datasets and nonlinear relationships, thus reducing their predictive power and adaptability. Artificial Intelligence (AI) and Machine Learning (ML) provide two of the most innovative approaches to credit risk modeling. This paper reviews a few ML models applied to improve the accuracy and efficiency of credit risk assessment, from Random Forests and Support Vector Machines to Neural Networks. Compared to the more traditional models, AI models can enhance predictive accuracy by using a wealth of structured and unstructured information, including alternative information sources such as social media activities and transaction history. However, despite noticeable advantages, there are some challenges concerning the use of AI in credit risk assessment, including model opaqueness, bias, and regulatory compliance. The nature of such a “black box”, especially for deep learning algorithms, can limit their interpretability and complicate regulatory compliance and decision rationalization. To solve problems induced by this “black box” nature, explainable AI techniques, namely Shapley values and LIME, have been implemented to enhance the transparency of models and raise stakeholder trust in support systems for decision-making. This review aims to evaluate the current applications of AI and ML in credit risk assessment, weigh the strengths and limitations of various models, and discuss the ethical considerations and regulatory challenges linked to their adoption by credit institutions.