Evaluating Credit Score Explanations in Lending Decisions and

Risk Management

Serkan Yilmaz Kandir1,* Murat Ismet Haseki1

1 Faculty of Economics and Administrative Sciences, Cukurova University, Adana, Turkey

Received: August 11, 2026 Revised: September 10, 2026 Accepted: October 06, 2026 ⋆ Corresponding author

ABSTRACT

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.

Keywords: Business intelligence Credit scoring Explainable machine learning Underwriting Counterfactual recourse

Algorithmic fairness

1. INTRODUCTION

Credit underwriting links a forecast of repayment to an allocation

of capital. A lender chooses an approval threshold,

loan terms, and an exposure limit before learning whether

an accepted borrower will repay. A default score matters

because it changes those choices. A reason supplied for the

score can matter too: it may support a review, reveal a data

error, guide an applicant’s next step, or expose an unjustified

pattern across groups. These purposes place different

demands on the same explanation. A feature ranking that

describes a fitted model is not automatically a reason that a

loan officer can act on, still less proof that changing a feature

will cause a safer loan.

The value of a credit decision depends on more than the ordering

of predicted defaults. Interest income, funding, servicing,

expected loss, and the use of scarce review capacity all enter

a lender’s operating result. An explanation could help a

reviewer identify a mistaken input before a high-value application

is declined, or it could consume review time without

changing a justified choice. These possibilities have different

financial implications. They also suggest a business test: the