Toward an evolving framework for responsible AI for credit scoring in the banking industry

Abstract

Purpose The aim of this research is to conduct a systematic review of the literature on responsible artificial intelligence (RAI) practices within the domain of AI-based Credit Scoring (AICS) in banking. This review endeavours to map the existing landscape by identifying the work done so far, delineating the key themes and identifying the focal points of research within this field. Design/methodology/approach A database search of Scopus and Web of Science (last 20 years) resulted in 377 articles. This was further filtered for ABDC listing, and augmented with manual search. This resulted in a final list of 53 articles which was investigated further using the TCCM (Theory, Context, Characteristics and Methodology) review protocol. Findings The RAI landscape for credit scoring in the banking industry is multifaceted, encompassing ethical, operational and technological dimensions. The use of artificial intelligence (AI) in banking is widespread, aiming to enhance efficiency and improve customer experience. Based on the findings of the systematic literature review we found that past studies on AICS have revolved around four major themes: (a) Advances in AI technology; (b) Ethical considerations and fairness; (c) Operational challenges and limitations; and (d) Future directions and potential applications. The authors further propose future directions in RAI in credit scoring. Originality/value Earlier studies have focused on AI in banking, credit scoring in isolation. This review attempts to provide deeper insights, facilitating the development of this key field.

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