A Review of Predictive Analytics in Real-Time Fraud Monitoring: A Case Study of Banking Institutions in Kebbi State
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- 1-5 Chapters
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- NGN 5000
Background of the Study
Fraud in the banking sector has become increasingly sophisticated, posing significant risks to both financial institutions and their customers. With the growing volume of transactions and the complexity of fraud schemes, traditional methods of fraud detection—such as rule-based systems—are proving insufficient. Predictive analytics has emerged as a powerful tool for real-time fraud monitoring, allowing banks to detect and prevent fraudulent activities before they can cause significant financial damage. Predictive analytics involves using statistical algorithms and machine learning techniques to analyze historical data and identify patterns that can predict future events, such as fraudulent transactions (Jiang & Wu, 2024).
In Kebbi State, banks are beginning to adopt predictive analytics models to enhance their fraud detection systems. These models leverage data from various sources, such as transaction records, customer behavior, and external threat intelligence, to identify anomalies that may indicate fraudulent activities. By analyzing these patterns in real time, banks can quickly flag suspicious transactions and take appropriate action before the fraud is completed. However, the implementation of predictive analytics in fraud monitoring presents challenges, including data privacy concerns, the need for skilled personnel, and the integration of predictive models with legacy banking systems (Oladipo & Onu, 2023). This review investigates the role of predictive analytics in real-time fraud monitoring within the banking institutions of Kebbi State, evaluating its effectiveness, challenges, and opportunities.
Statement of the Problem
While predictive analytics has proven effective in reducing fraud risks in various financial sectors globally, its application in Kebbi State’s banking institutions remains underexplored. Many banks in the region still rely on traditional fraud detection systems that are reactive rather than proactive, making it difficult to catch fraud before it happens. This review aims to assess the extent to which predictive analytics is being implemented in real-time fraud monitoring and to identify the barriers to its adoption in Kebbi State’s banking sector.
Objectives of the Study
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To evaluate the effectiveness of predictive analytics in real-time fraud monitoring in Kebbi State’s banking institutions.
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To identify the challenges faced by banks in Kebbi State in adopting predictive analytics for fraud monitoring.
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To recommend strategies for improving the implementation of predictive analytics in real-time fraud detection in Kebbi State’s banks.
Research Questions
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How effective is predictive analytics in detecting and preventing fraud in Kebbi State’s banking institutions?
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What challenges do banks in Kebbi State face when implementing predictive analytics for fraud monitoring?
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What strategies can enhance the adoption and effectiveness of predictive analytics in real-time fraud detection in Kebbi State’s banks?
Research Hypotheses
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Predictive analytics does not significantly improve fraud detection in real-time transactions in Kebbi State’s banks.
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The challenges faced by Kebbi State’s banks, such as data privacy concerns and system integration issues, do not significantly hinder the adoption of predictive analytics for fraud monitoring.
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Strategies for improving the implementation of predictive analytics will not significantly enhance fraud detection outcomes in Kebbi State’s banking institutions.
Scope and Limitations of the Study
This review focuses on banking institutions in Kebbi State that have adopted or are exploring predictive analytics for real-time fraud monitoring. The study is limited by access to proprietary banking data, the potential reluctance of banks to share insights, and the evolving nature of fraud detection technologies.
Definitions of Terms
Predictive Analytics: The use of statistical algorithms and machine learning to analyze historical data and predict future events or behaviors, such as fraud.
Fraud Monitoring: The process of detecting, preventing, and investigating fraudulent activities, typically through automated systems or analytics.
Banking Institutions: Financial organizations that offer services such as loans, savings accounts, and fraud prevention.
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