Algorithm Bias in Credit Markets and Socio-Economic Inequality – A Survey of The Six Geopolitical Zones in Nigeria

John Okey Onoh PhD; Chidi Slessor; Mba PhD

doi:10.56201/ijssmr.vol.12no3.2026.pg630.653

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Algorithm Bias in Credit Markets and Socio-Economic Inequality – A Survey of The Six Geopolitical Zones in Nigeria

John Okey Onoh PhD, Chidi Slessor, Mba PhD

Abstract

The rapid diffusion of machine-learning credit-scoring models promises faster loan decisions and lower transaction costs, yet mounting evidence suggests that such algorithms can reproduce and even exacerbate existing socio-economic disparities (Hand, 2021). In Nigeria, where formal financial inclusion varies markedly across the six geopolitical zones, the interaction between perceived algorithmic bias and credit-market outcomes remains under-explored. This study investigates how borrowers’ perceptions of algorithmic fairness relate to credit denial, cost perception, and regional inequality. A quantitative, cross-sectional survey was administered online in March–May 2025. A structured questionnaire containing 50 five-point Likert items was completed by 34 adult respondents who had applied for formal or digital credit within the preceding twelve months. The sample comprised 12 academics, 10 international business professionals, and 12 investors/fin-tech entrepreneurs, representing all six zones. Composite scales were constructed for perceived algorithmic bias (BIAS, items 21-32; Cronbach’s α = 0.89) and trust in regulators (TRUST, items 46-47; α = 0.82). Zone-level Gini coefficients were merged from the National Bureau of Statistics (2023). Descriptive analysis showed a mean BIAS score of 3.42 ± 0.71 and a credit-denial rate of 38 %. Correlation analysis revealed a significant positive association between BIAS and DENIAL (r = 0.42, p < 0.05) and a negative link with perceived cost fairness (r = -0.38, p < 0.05). Logistic regression confirmed that higher BIAS increased the odds of denial (OR = 1.28, p = 0.018) and that this effect was amplified in zones with higher Gini values (interaction OR = 1.35, p = 0.004). Ordinary least-squares regression indicated that greater TRUST reduced BIAS (β = -0.30, p = 0.002) and that awareness of anti-discrimination legislation moderated the bias-cost relationship (β = -0.25, p = 0.009). Diagnostic tests (VIF < 2, Hosmer-Lemeshow p = 0.562) confirmed model adequacy, and robustness checks (split-sample, alternative scaling) supported the stability of the findings. The results substantiate four hypotheses: perceived algorithmic bias elevates credit-denial risk, especially in more unequal zones; trust in regulatory bodies mitigates bias perception; and legal awareness attenuates the adverse impact of bias on perceived interest-rate fairness. These outcomes extend the literature on statistical discrimination (Arrow, 1973) and procedural fairness (Miller & Smith, 2019) to a Sub-Saharan context, highlighting the need for mandatory algorithmic audits, enhanced transparency, and targeted inclusion policies. The study contributes a multidisciplinary framework that links algorithmic fairness with regional socio-economic inequality, offering actionable insights for policymakers, lenders, and researchers seeking to promote equitable credit access in Nigeria and comparable emerging economies.

Keywords

algorithmic biascredit marketssocio-economic inequalityNigeriageopolitical zonesfairnesstrustregulation.

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Algorithm Bias in Credit Markets and Socio-Economic Inequality – A Survey of The Six Geopolitical Zones in Nigeria

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