The Adoption of Predictive Analytics in Fraud Prevention: A Case Study of E-Commerce Firms in Yobe State
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- NGN 5000
Background of the Study
Fraud prevention is a critical concern for e-commerce firms as online transactions become increasingly susceptible to cyber threats. Predictive analytics has emerged as a powerful tool for identifying and mitigating fraudulent activities by leveraging data mining, machine learning, and statistical modeling (Usman & Musa, 2023). By analyzing historical data, predictive analytics enables firms to detect anomalies and patterns that indicate potential fraud.
In Yobe State, where the e-commerce sector is rapidly growing, firms are increasingly adopting predictive analytics to enhance transaction security and build customer trust. However, challenges such as data quality issues, high costs of implementation, and a lack of skilled personnel limit the effectiveness of these solutions (Ahmed & Ibrahim, 2024). This study explores the role of predictive analytics in fraud prevention and its impact on the operational efficiency of e-commerce firms in Yobe State.
Statement of the Problem
E-commerce firms in Yobe State face significant risks related to fraudulent activities, which undermine customer trust and lead to financial losses. While predictive analytics offers solutions for fraud prevention, its adoption is hindered by challenges such as insufficient expertise, inadequate data infrastructure, and the high cost of implementation (Bello et al., 2023).
The lack of localized research on predictive analytics adoption in e-commerce fraud prevention creates a gap in understanding its effectiveness and the barriers faced by firms in Yobe State. This study aims to address this gap by examining the adoption of predictive analytics in fraud prevention and its impact on e-commerce operations.
Objectives of the Study
1. To assess the role of predictive analytics in fraud prevention among e-commerce firms in Yobe State.
2. To identify the challenges e-commerce firms face in adopting predictive analytics for fraud prevention.
3. To evaluate the impact of predictive analytics adoption on the operational efficiency of e-commerce firms.
Research Questions
1. What role does predictive analytics play in fraud prevention among e-commerce firms in Yobe State?
2. What challenges do e-commerce firms face in adopting predictive analytics for fraud prevention?
3. How does the adoption of predictive analytics impact the operational efficiency of e-commerce firms?
Research Hypotheses
1. Predictive analytics adoption does not significantly reduce fraud in e-commerce firms in Yobe State.
2. Challenges related to cost and data quality significantly hinder the adoption of predictive analytics.
3. Predictive analytics adoption significantly improves operational efficiency in e-commerce firms.
Scope and Limitations of the Study
The study focuses on e-commerce firms in Yobe State and their adoption of predictive analytics for fraud prevention. Limitations may include variations in data quality and reluctance of firms to share proprietary information on fraud prevention mechanisms.
Definitions of Terms
• Predictive Analytics: The use of statistical models and machine learning to predict future outcomes based on historical data.
• Fraud Prevention: Measures taken to detect and prevent fraudulent activities.
• E-Commerce Firms: Businesses that operate online platforms for buying and selling goods or services.
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