How Can Credit Scoring Benefit from Machine Learning? SWOT Analysis

This paper pioneers a comprehensive exploration through the application of a SWOT analysis to investigate the role of machine learning (ML) in credit scoring. Notably, ML demonstrates strengths in enhancing accuracy over time, processing large datasets, and identifying subtle patterns between data points. However, a key drawback lies in the lack of transparency. Additionally, addressing data imbalances proves challenging for certain models, while nonlinear approaches may incur computational expenses and risk overfitting. Despite these limitations, opportunities for ML in credit scoring are abundant, encompassing hybrid models, deep learning techniques, and the creation of interpretable models. Threats to ML adoption include the persistent preference for traditional scoring models, driven by legal and regulatory considerations like the Basel accords. Data scarcity and the influence of external factors, such as macroeconomic aggregates, also pose challenges. This study enhances our understanding and guides the discourse toward a more nuanced and effective future for credit assessment through machine learning.