EFFECT OF ARTIFICIAL INTELLIGENCE ON NON-FINANCIAL PERFORMANCE OF DEPOSIT MONEY BANKS IN GOMBE METROPOLIS, NIGERIA

Abstract

This study examines the effect of artificial intelligence adoption on the non-financial performance of deposit money banks in Gombe Metropolis, Nigeria. Specifically, it investigates how AI influences customer service quality, operational efficiency, risk management effectiveness, and employee productivity. A descriptive cross-sectional survey design was adopted. The target population comprised 506 staff from Operations, Customer Service, Information Technology, and Risk Management departments across five Tier-1 deposit money banks. Samples of 223 respondents were determined using Yamane's formula and proportionally allocated across the banks. Data were collected using a structured questionnaire adapted from validated instruments, achieving a Cronbach's Alpha reliability coefficient of 0.892. A total of 199 valid responses (89.2% response rate) were analyzed using descriptive statistics and multiple regression analysis. The results reveal that AI adoption has a significant positive effect on all four dimensions of non-financial performance. The combined effect shows that AI adoption explains 58.4% of the variance in non-financial performance. The study concludes that AI adoption significantly enhances non-financial performance in deposit money banks, with the most substantial impact on operational processes and customer-facing activities. AI capabilities serve as strategic resources that enable banks to achieve superior service quality, streamlined operations, robust risk management, and improved workforce productivity. Therefore, the study recommended among others that, bank management should accelerate AI integration across all operational areas, prioritizing applications that enhance operational efficiency and customer service quality. Comprehensive training programs should be developed to equip employees with skills to work effectively with AI tools, addressing the relatively lower impact on productivity. Also, banks should strengthen AI-powered risk management systems, particularly fraud detection and credit scoring applications.