Effect of Artificial Intelligence on Predictive Financial Analysis in Selected Deposit Money Banks in Anambra State, Nigeria
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Today, as the banking sector incorporates AI technologies, understanding their impact on customer satisfaction has become paramount. This study investigates therefore examines the effect of artificial intelligence on predictive financial analysis in selected deposit money banks in Anambra State, Nigeria. The research modelled several independent variables, including the level of AI technology adoption, the use of machine learning algorithms, the volume of data processed by AI systems, the number of personnel trained in AI technologies, financial resources allocated for AI development, and the compatibility of AI systems with existing banking infrastructure. Utilizing a sample of 216 respondents, the study employs the Ordinary Least Squares (OLS) econometric regression technique to analyze the data. The regression results reveal that all independent variables have a significant positive impact on customer satisfaction. Specifically, the coefficient estimates indicate that for every unit increase in AI technology adoption, customer satisfaction increases by 0.32 (p < 0.01). The implementation of machine learning algorithms correlates with a 0.25 rise in customer satisfaction (p < 0.05), while an increase in the volume of data processed leads to a 0.30 (p < 0.01) improvement. The training of personnel contributes a 0.18 increase (p < 0.05), and financial resources dedicated to AI development show a significant coefficient of 0.22 (p < 0.01). Lastly, compatibility of AI systems has a coefficient of 0.27 (p < 0.01), indicating its critical role in improving services. The findings suggest that banks should prioritize the strategic adoption and integration of advanced AI technologies by assessing existing processes and fostering a culture of innovation. Investing in tailored machine learning solutions, robust data management infrastructure, and ongoing staff training will enhance personalized services and customer satisfaction. Additionally, ensuring compatibility of new AI systems with current infrastructure through careful planning and phased implementation will facilitate seamless integration and sustained service quality. The implications of this study are profound, highlighting the necessity for Nigerian banks to embrace AI not only as a technological upgrade but also as a driver for improved customer engagement and satisfaction. As the competitive landscape continues to evolve, those institutions that invest in AI-driven predictive analytics will likely secure a more stable and loyal customer base in the dynamic Nigerian financial market.
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