Volume 15 • Issue 1 • PP: 38–49 • 2027
Evaluating Credit Score Explanations in Lending Decisions and Risk Management
Open Access & Copyright
© 2027 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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Credit score explanations can influence lending reviews, exception handling, and customer communication, yet an explanation may change under resampling, recommend an infeasible action, or fail to illuminate the approval decision. This structured review examines the evidence needed to use explanations in lending and risk management. Thirty-eight journal studies published from 2023 through January 2026 were coded by method family, information setting, validation design, and decision connection. The evidence spans interpretable scores and rules, post hoc attributions, counterfactuals, stability tests, fairness audits, and decision-oriented comparators. It does not support a single ranking of methods: studies examine different properties on different applicant populations, and few identify the incremental effect of an explanation on an underwriter’s action or an applicant’s outcome. Four comparisons organize the findings. Predictive discrimination does not establish explanation fidelity; plausible local reasons may not survive changes in training data; a score-changing counterfactual may be unattainable; and similar attributions do not imply equal lending outcomes. We develop a validation framework linking temporal performance, explanation perturbations, feasible action, group analysis, and policy value. Additional analyses map the evidence to decision stages, distinguish estimable quantities from missing outcomes, and specify reporting standards for management use. The review contributes an operational basis for deciding when an explanation can be treated as evidence in underwriting rather than as a model display.
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