Artificial Intelligence-Driven Credit Management and Financial Performance of Deposit Money Banks (DMBs) In Abuja, Nigeria

This study examined Artificial Intelligence–Driven and Credit Management (AICM) on the financial performance of selected Deposit Money Banks (DMBs) in Abuja, Nigeria. Specifically, the research focused on two key AICM indicators Automated Credit Scoring Systems and Predictive Risk Analytics and their influence on profitability, asset quality, and overall financial stability. The study was motivated by the increasing integration of intelligent technologies in banking operations and the need to evaluate their measurable performance outcomes within Nigeria’s financial sector. The population comprised 652 management staff and employees of selected DMBs in Abuja, from which a sample size of 248 respondents was determined using an appropriate sampling technique. Primary data were collected through structured questionnaires designed to capture perceptions and operational realities of AI-driven credit tools. Data analysis was conducted using the Statistical Package for Social Sciences (SPSS Version 27.0). Multiple linear regression, correlation analysis, and Analysis of Variance (ANOVA) were employed to test the study hypotheses and determine the strength, direction, and significance of relationships among variables. The findings revealed that Automated Credit Scoring Systems significantly enhance financial performance by improving credit appraisal efficiency, reducing default rates, and strengthening loan portfolio quality among the selected DMBs in Abuja, Nigeria. Similarly, Predictive Risk Analytics demonstrated a strong positive effect on financial performance through early risk detection, improved decision accuracy, and proactive credit monitoring. The regression results indicated that both variables jointly explain a substantial proportion of variations in financial performance among the selected banks in Abuja. The study concludes that AI-driven credit management serves as a strategic enabler of operational efficiency and financial sustainability of DMBs in Abuja, Nigeria. It recommends increased investment in intelligent credit technologies, continuous staff training, and the development of robust data governance frameworks to maximize the benefits of AI integration in Nigeria’s banking sector.

Artificial Intelligence–Driven Credit Management, Automated Credit Scoring Systems, Predictive Risk Analytics, Financial Performance

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