AI‐Based Credit Scoring and Perceived Fairness Among Digital Finance Users in Bangladesh

AI-based credit scoring may broaden the information available to lenders while creating questions about explanation, unequal treatment and control over personal data. This article develops a Bangladesh-focused framework for perceived fairness, integrating the performance-expectancy component of UTAUT2, organisational justice and privacy-calculus reasoning. It also examines whether the supplied Group 8 Law-SDG database can support the proposed empirical model. The workbook contains 385 records and five credit-scoring statements, but it does not contain the proposed multi-item measures of usefulness, explainability, procedural justice, trust, privacy concern and overall perceived fairness. An item-level audit therefore replaces the earlier illustrative regression. Endorsement of disclosure about automated credit decisions is 82.3%, regular bias testing 83.6%, correction of inaccurate information 63.9%, relevant and lawful data use 63.6%, and the proposition that fairness affects acceptance 60.0%. These percentages describe supplied records rather than a representative population. The credit-scoring block has low internal consistency (Cronbach’s alpha = 0.193), precluding its use as a validated reflective fairness scale. The five proposed hypotheses remain untested. The article contributes a distinction between endorsement of safeguards, perceived fairness and acceptance, together with a measurement plan for evaluating their relationships. Recommendations concern understandable reasons, data correction, accessible review and documented model governance. Numeric-response provenance and digital-finance eligibility require confirmation; the codebook labels identifiers and comments synthetic. The study consequently offers a conceptual framework and a descriptive measurement audit, not a completed causal or structural test among verified borrowers.