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As a general rule, for pecuniary and regulatory reasons, commercial banks assiduously manage the credit risk of their loan portfolios. Algorithmic decision-making (ADM) systems may enable lenders to arrive at credit decisions that previously would not have been possible. However, the point at which utilizing ADM is optimized is still open for debate. To help illuminate the issues, a systematic literature review is conducted to investigate the following questions: How does algorithmic decision-making (ADM) contribute to the effectiveness of credit risk assessment (CRA)? And, what, if anything, can be done to improve the contribution of ADM? The review indicates that ADM’s contributions have largely been through enhanced human decision-making under uncertainty. In addition, the review underscores the importance of organizational arrangements to the successful deployment of ADM systems. Furthermore, the technology’s contribution to CRA can be improved by addressing algorithmic bias and transparency issues.



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