The Effect of Artificial Intelligence on Personalized Banking Experiences: A Case Study of Access Bank, Ekiti State
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Background of the Study:
Artificial Intelligence (AI) has emerged as a key driver in transforming the retail banking experience by enabling personalized services that cater to individual customer needs. In Ekiti State, Access Bank is leveraging AI to deliver customized banking experiences through advanced data analytics, machine learning, and natural language processing. These technologies allow the bank to analyze customer behavior, predict financial needs, and tailor product recommendations accordingly. AI-driven personalization not only enhances customer satisfaction by providing relevant and timely services but also increases operational efficiency by automating routine tasks and optimizing resource allocation (Ibrahim, 2024). Access Bank’s initiatives include AI-powered chatbots, personalized financial planning tools, and targeted marketing campaigns that deliver customized offers based on customer profiles. The integration of AI is expected to build deeper customer relationships and foster loyalty, as customers experience a banking service that adapts to their unique circumstances. However, challenges such as data privacy concerns, the complexity of AI algorithms, and potential biases in personalization remain. These issues can impact the overall customer experience and hinder the full realization of AI’s benefits in personalization. This study examines how AI influences personalized banking experiences at Access Bank, assessing its impact on customer satisfaction, retention, and competitive positioning in a digital era (Okafor, 2023).
Statement of the Problem:
Although Access Bank has invested substantially in AI technologies to personalize customer experiences, several challenges continue to undermine these initiatives. Customers have expressed concerns over data privacy and the transparency of AI-driven recommendations, which may lead to mistrust and reduced engagement. Technical issues such as algorithmic bias and the inability to fully capture diverse customer needs can result in generic or inaccurate service suggestions. Additionally, the integration of AI with existing legacy systems often presents operational hurdles that delay the delivery of personalized services. These challenges create a gap between the intended benefits of AI personalization and the actual customer experience, potentially diminishing satisfaction and loyalty. The bank must address these shortcomings to ensure that AI systems deliver truly personalized and secure banking experiences that meet customer expectations in an increasingly competitive market. This study aims to identify the root causes of these challenges and propose strategic improvements to enhance the effectiveness of AI in delivering personalized banking services (Chinwe, 2023).
Objectives of the Study:
• To evaluate the impact of AI on personalization in banking services at Access Bank.
• To identify technical and privacy challenges affecting AI-driven personalization.
• To recommend strategies for optimizing AI systems to improve personalized customer experiences.
Research Questions:
• How does AI influence the personalization of banking services at Access Bank?
• What challenges limit the effectiveness of AI-driven personalization?
• What measures can enhance the reliability and security of personalized banking experiences?
Research Hypotheses:
• H₁: AI significantly improves the customization of banking services.
• H₂: Data privacy and algorithmic bias negatively affect customer trust in personalized services.
• H₃: Enhanced system integration and transparency measures improve personalized banking outcomes.
Scope and Limitations of the Study:
This study focuses on Access Bank’s AI personalization initiatives in Ekiti State, using customer surveys, performance data, and technical evaluations. Limitations include evolving AI technology and potential biases in customer feedback.
Definitions of Terms:
• Artificial Intelligence (AI): Technologies that enable machines to perform tasks that typically require human intelligence.
• Personalized Banking: Tailoring banking services to meet individual customer needs based on data analysis.
• Algorithmic Bias: Systematic errors in AI systems that lead to unfair outcomes.
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