COMPARATIVE ANALYSIS OF ARTIFICIAL INTELLIGENCE METHODS AND TRADITIONAL CREDIT RISK MODELS: AN EXPLORATORY QUALITATIVE REVIEW
DOI:
https://doi.org/10.35631/AIJBAF.825004Keywords:
Artificial Intelligence, Machine Learning, Hybrid Modelling, Fairness, Auditability, Black-Box, Bias, SLR, Behavioural Patterns, PICO, Ethical ImplicationsAbstract
Artificial intelligence (AI) and machine learning (ML) methods are revolutionizing the credit risk modelling. Traditional statistical methods which have been around for a long time are replaced with AI-based models. Although AI models provide superior predictive performance, their adoption in regulated financial sector is seen with caution. Financial sector regulatory requirements regarding interpretability, stability, fairness, and governance of credit risk models are one of the big challenges to AI & ML adoption. This study is based on an exploratory qualitative review to perform a comparative analysis of AI models and traditional credit risk models. This study examines relevant academic literature, publications, and industry reports. Five key points have been identified as a result of this study: (1) AI models are better than traditional models in terms of performance; (2) explainability and auditability is still the biggest challenge with AI adoption; (3) traditional models are more reliable yet they may not be efficient; (4) regulatory requirements impact model selection; and (5) hybrid modelling approaches are a promising middle ground. The article suggests a multidimensional theoretical framework for assessing credit risk models and emphasizes prospective research avenues in hybrid modelling, fairness-aware AI, and real-time risk monitoring.
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