The effect of fraud detection technology on minimizing losses in banking: a case study of Fidelity Bank Nigeria
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Background of the Study
With the rise of sophisticated cyber threats, fraud detection technology has become critical for minimizing financial losses in the banking sector. Fidelity Bank Nigeria has implemented advanced fraud detection systems, including machine learning algorithms, real-time monitoring, and biometric verification, to safeguard its transactions and customer data (Adeyemi, 2023). The theoretical framework of this study is anchored in risk management and cybersecurity theories, which posit that proactive fraud detection not only reduces financial losses but also enhances customer trust. Fidelity Bank’s approach involves integrating these technologies with existing operational systems to create a seamless defense mechanism against fraud. By continuously updating its fraud detection protocols and training staff on the latest security measures, the bank aims to stay ahead of increasingly sophisticated fraud schemes (Chinwe, 2024). Empirical research suggests that banks leveraging robust fraud detection technologies experience lower incidences of fraud-related losses and enjoy higher customer satisfaction. However, challenges such as integration issues with legacy systems and high false-positive rates occasionally hinder the overall effectiveness of these systems. This study seeks to evaluate the impact of fraud detection technology on loss prevention at Fidelity Bank Nigeria, identifying both technological and operational challenges and proposing strategies for improvement (Okeke, 2025).
Statement of the Problem
Despite significant investments in fraud detection technology, Fidelity Bank Nigeria continues to suffer financial losses due to fraud. Integration issues between the new technologies and legacy systems sometimes result in delayed identification of fraudulent activities, allowing losses to occur (Olu, 2023). Moreover, high rates of false positives can disrupt legitimate transactions, frustrating customers and increasing operational costs. These challenges highlight the gap between the theoretical promise of fraud detection technology and its practical effectiveness in reducing losses. Additionally, the rapid evolution of fraud tactics means that the bank must continuously update its systems, a process that can strain resources and create temporary vulnerabilities. The study seeks to investigate these issues, exploring the extent to which current fraud detection technology minimizes losses and identifying areas where further enhancements are necessary to achieve optimal loss prevention (Emeka, 2024).
Objectives of the Study
To evaluate the effectiveness of fraud detection technology in minimizing losses at Fidelity Bank Nigeria.
To identify integration and operational challenges affecting fraud detection performance.
To recommend strategies for improving fraud detection accuracy and reducing false positives.
Research Questions
How effective is fraud detection technology in reducing financial losses at Fidelity Bank Nigeria?
What integration issues hinder the optimal performance of fraud detection systems?
How can fraud detection technology be improved to reduce false positives and enhance loss prevention?
Research Hypotheses
Advanced fraud detection technology is positively correlated with reduced financial losses.
Integration challenges with legacy systems negatively impact fraud detection effectiveness.
Continuous system updates and enhanced training reduce false-positive rates and improve loss prevention.
Scope and Limitations of the Study
This study focuses on the fraud detection systems at Fidelity Bank Nigeria over the past three years. Limitations include restricted access to sensitive internal data and the rapidly changing nature of cyber threats.
Definitions of Terms
• Fraud Detection Technology: Tools and systems used to identify and prevent fraudulent transactions.
• Loss Prevention: The reduction of financial losses due to fraud.
• False Positives: Incorrect identification of legitimate transactions as fraudulent.
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