Outlet Title

Fairness-Aware Credit Intelligence: Balancing Predictive Accuracy, Inclusion, And Regulatory Accountability

Document Type

Article

Publication Date

Spring 7-13-2026

Abstract

Machine learning has substantially improved consumer credit-risk prediction, yet its deployment in lending decisionsraises persistent concerns regarding demographic fairness, financial exclusion, explainability, and regulatory defensibility. Thisstudy develops and empirically evaluates a Fairness-Aware Credit Intelligence (FACI) framework — a four-layer architectureintegrating predictive modeling, in- and post-processing fairness intervention, explainability and human policy override, andportfolio-level simulation and governance — using loan-level data from 412,683 consumer lending applications spanning 2021–2026. The study compares a traditional credit scorecard, gradient boosting and deep neural network models, two single-constraint fairness-aware models (demographic parity and equal opportunity), and the integrated FACI framework acrosspredictive accuracy (AUC-ROC), approval rates, demographic parity and equal opportunity differences, disparate impactratios, portfolio return, and default rates. Results show that unconstrained gradient boosting and neural network models achievethe highest raw accuracy (AUC-ROC 0.781–0.789) but the largest fairness disparities (demographic parity difference 0.187–0.201, disparate impact ratios of 0.65–0.68, below the regulatory four-fifths threshold). The integrated FACI framework achievesAUC-ROC of 0.778 — within 0.003 of the unconstrained gradient boosting benchmark — while reducing the demographic paritydifference to 0.038 and improving the disparate impact ratio to 0.92, alongside a higher simulated net portfolio return (5.87%)than either single-constraint fairness model and a lower default rate (8.8%) than the unconstrained benchmark. Subgroupanalysis reveals that FACI's gains are concentrated among thin-file applicants, whose approval rate gap relative to the referencegroup narrows from 27.1 to 14.4 percentage points while their default rate under FACI (10.2%) falls below their default rateunder the unconstrained model (11.8%). Portfolio stress simulations across five macroeconomic and operational scenariosdemonstrate that FACI's fairness mechanisms function as a form of risk diversification, with smaller return degradation andsmaller fairness-metric deterioration than the unconstrained benchmark under severe recession and regional economic shockscenarios. The paper contributes the FACI framework, a five-level maturity roadmap, and a regulatory framework mapping tofintech, responsible AI, and information systems governance research, demonstrating that accuracy-fairness trade-offsdocumented in prior single-constraint studies can be substantially — though not entirely — resolved through an integrated,multi-layer organizational decision architecture rather than model-level constraints alone.

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