Blockchain-Based Credit Scoring Models and Financial Inclusion in a Developing Economy

This study examined the impact of block-chain based credit scoring models on financial inclusion in a developing economy using Nigeria as a case study, covering the period 1999–2024. Using an ex post facto design, the study analyzed secondary data sourced from EFInA, CBN, NDIC, and IMF, focusing on Credit Access Rate (CAR), Loan Default Rate (LDR), Average Loan Size (ALS), Repayment Timeliness (RT), and the Financial Inclusion Index (FII). Ordinary Least Squares (OLS) regression was employed in E-Views 9.0, with diagnostic tests including Variance Inflation Factor, BreuschPagan, and DurbinWatson to confirm robustness. The results showed that CAR positively and significantly influenced financial inclusion, underscoring the importance of expanding access through blockchain transparency. LDR exerted a negative and significant effect, confirming that defaults weakened inclusion but could be mitigated through blockchain enforcement mechanisms. ALS had a positive and significant effect, revealing that larger loan sizes, enabled by blockchain verification, enhanced inclusion by deepening financial participation. RT, however, was statistically insignificant, indicating that repayment timeliness did not independently explain inclusion once other variables were controlled. These findings supported Information Asymmetry Theory, as blockchain reduced inefficiencies in borrower profiling, and the Technology Acceptance Model, as blockchain improved perceived usefulness and adoption. The study concluded that blockchain credit scoring models provided a viable pathway to advance financial inclusion in Nigeria by improving access, reducing defaults, and enabling larger loans. Recommendations included adopting blockchain for borrower profiling, leveraging smart contracts to curb defaults, scaling affordable larger loans through blockchain registries, and enhancing repayment monitoring through digital tools. This research contributed to knowledge by empirically linking blockchain-based credit scoring models to measurable inclusion outcomes in Nigeria, offering insights for policymakers, financial institutions, and regulators seeking to achieve inclusive growth.