Impact of AI-Powered Inter-Bank Settlement Systems on Customer Satisfaction in Nigerian Fintechs

International Journal of Economics and Management Studies

© 2025 by SSRG - IJEMS Journal

Volume 12 Issue 6

Year of Publication : 2025

Authors : Abdullahi Ya’u Usman, Saidu Ibrahim Halidu, Iselowo Kolawole Kehinde

10.14445/23939125/IJEMS-V12I6P102

How to Cite?

Abdullahi Ya’u Usman, Saidu Ibrahim Halidu, Iselowo Kolawole Kehinde, "Impact of AI-Powered Inter-Bank Settlement Systems on Customer Satisfaction in Nigerian Fintechs," SSRG International Journal of Economics and Management Studies, vol. 12,  no. 6, pp. 23-31, 2025. Crossref, https://doi.org/10.14445/23939125/IJEMS-V12I6P102

Abstract:

The rapid growth of Nigeria’s fintech sector has underscored persistent challenges in Inter-Bank Settlement Systems (IBSS)—notably transaction latency, manual reconciliation, and fraud vulnerabilities—that undermine customer confidence and service uptake. This study examines the impact of Artificial Intelligence (AI)–powered IBSS on customer satisfaction within three major urban fintech markets: Lagos, Abuja, and Port Harcourt. Employing a descriptive quantitative design, the study collected 460 valid responses via a structured Likert-scale survey instrument, complemented by secondary transaction-log analyses to validate self-reported measures. Data were analyzed using SPSS v.28, with descriptive statistics, paired-sample t-tests, multiple regression, and Confirmatory Factor Analysis (CFA) conducted to assess three hypotheses relating AI-enabled transaction speed, fraud detection, and dispute resolution to overall satisfaction. Results indicate statistically significant improvements across all dimensions: AI integration reduced average settlement times from 2.3 minutes to under 10 seconds (t(459) = 25.4, p < .001), fraud-detection reliability positively predicted trust (β = 0.57, p < .001), and automated reconciliation yielded a 54% decrease in dispute resolution time (χ²(1,460) = 72.8, p < .001). Transaction speed emerged as the strongest satisfaction driver (R² = 0.46), followed by fraud prevention (R² = 0.33) and dispute handling. These findings extend the Technology Acceptance Model and Diffusion of Innovation frameworks in an emerging-market IBSS context and offer actionable insights for fintech providers and regulators: prioritizing machine-learning modules for real-time routing and anomaly detection can maximize service quality and customer loyalty.

Keywords:

AI-powered IBSS, Customer Satisfaction, Fintech, Nigeria, Technology Acceptance Model.

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