An Investigation of the Challenges of Implementing AI in Retail Banking: A Case Study of First Bank, Anambra State
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Background of the Study
Artificial Intelligence (AI) has the potential to revolutionize retail banking by automating routine processes, improving decision-making, and enhancing customer interactions. First Bank in Anambra State has embarked on integrating AI technologies—such as machine learning algorithms, chatbots, and predictive analytics—into its operations. These innovations are designed to streamline processes like credit scoring, customer service, and fraud detection, thereby reducing costs and improving efficiency (Ibrahim, 2023). However, the implementation of AI is not without its challenges. The integration process often involves overcoming technical barriers, such as the compatibility of AI systems with legacy infrastructure and ensuring data quality for accurate predictions. Additionally, concerns over data privacy, algorithmic transparency, and the potential displacement of human workers present significant hurdles. Regulatory uncertainties also add complexity to the deployment of AI in banking operations. As First Bank seeks to harness AI for competitive advantage, it must address these challenges to ensure that the technology delivers its full potential without compromising service quality or customer trust (Chinwe, 2024; Akinola, 2025).
Statement of the Problem
Despite the promising benefits of AI, First Bank faces several challenges in its implementation that hinder its effectiveness. Technical integration with existing legacy systems is often problematic, leading to system glitches and data inconsistencies. These issues can result in errors in automated decision-making and undermine the reliability of AI-driven processes. Additionally, concerns regarding data privacy and the lack of transparency in AI algorithms have raised ethical and regulatory challenges that the bank must navigate carefully. There is also resistance from employees who fear job displacement, which can affect the overall organizational culture and adoption of AI solutions. The rapid pace of technological change means that continuous investment in infrastructure and skills development is required, placing a strain on resources. These challenges collectively contribute to a gap between the theoretical advantages of AI and its practical application, potentially limiting its impact on operational efficiency and customer service quality. This study aims to identify and analyze these challenges, with the goal of proposing strategies to overcome them and enhance the effective implementation of AI in retail banking.
Objectives of the Study
• To identify the technical and operational challenges in implementing AI at First Bank.
• To evaluate the impact of data privacy and regulatory concerns on AI adoption.
• To recommend strategies for improving AI integration and organizational readiness.
Research Questions
• What are the main technical challenges in integrating AI with legacy systems at First Bank?
• How do data privacy and regulatory issues affect AI implementation?
• What measures can be taken to facilitate smoother AI adoption?
Research Hypotheses
• H₁: Integration issues with legacy systems significantly hinder AI performance.
• H₂: Data privacy concerns are inversely related to employee and customer acceptance of AI.
• H₃: Continuous training and regulatory clarity improve AI implementation outcomes.
Scope and Limitations of the Study
This study focuses on First Bank’s AI implementation initiatives in Anambra State. Limitations include evolving technology standards, potential data access constraints, and resistance to organizational change.
Definitions of Terms
• Artificial Intelligence (AI): Technologies that enable automated decision-making and process optimization.
• Legacy Systems: Older computing systems that may not seamlessly integrate with modern technologies.
• Algorithmic Transparency: The clarity with which AI decision-making processes are understood.
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