An Assessment of Predictive Analytics in Fraud Detection: A Case Study of Fintech Startups in Jigawa State
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- NGN 5000
Background of the Study
Fraud detection remains a critical challenge in the financial technology (fintech) industry, where the rapid digitization of financial services increases exposure to fraudulent activities. Predictive analytics leverages historical data, statistical algorithms, and machine learning techniques to identify patterns and predict future fraudulent activities (Adebayo & Johnson, 2024). Globally, predictive analytics has been instrumental in reducing fraud, enhancing transaction security, and protecting consumer trust.
In Jigawa State, fintech startups are emerging as key players in financial inclusion, providing innovative services such as mobile payments and lending platforms. However, their rapid growth is accompanied by heightened risks of fraud, threatening their operational efficiency and customer confidence (Musa & Bello, 2023). Despite the potential of predictive analytics to address these challenges, its adoption in Jigawa State's fintech sector remains limited and underexplored. This study evaluates the role of predictive analytics in enhancing fraud detection and prevention among fintech startups in Jigawa State.
Statement of the Problem
Fraudulent activities significantly undermine the trust and financial stability of fintech startups in Jigawa State. Traditional fraud detection systems often react after the occurrence, leading to financial losses and reputational damage. Predictive analytics offers proactive solutions but faces barriers such as cost, technical complexity, and limited expertise in the local fintech ecosystem (Okoro & Aliyu, 2025). This study addresses the gap by assessing the effectiveness and challenges of adopting predictive analytics for fraud detection in fintech startups in Jigawa State.
Objectives of the Study
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To assess the adoption level of predictive analytics in fraud detection among fintech startups in Jigawa State.
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To evaluate the effectiveness of predictive analytics in identifying and mitigating fraud.
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To identify challenges fintech startups face in implementing predictive analytics for fraud detection.
Research Questions
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What is the adoption level of predictive analytics in fraud detection among fintech startups in Jigawa State?
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How effective is predictive analytics in identifying and mitigating fraud?
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What challenges do fintech startups face in implementing predictive analytics?
Research Hypotheses
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There is no significant relationship between the adoption of predictive analytics and the effectiveness of fraud detection.
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Predictive analytics does not significantly reduce fraud in fintech startups in Jigawa State.
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The challenges in adopting predictive analytics are not significant among fintech startups.
Scope and Limitations of the Study
The study focuses on fintech startups operating in Jigawa State, examining their use of predictive analytics for fraud detection. Limitations include variations in fintech startups' technological capabilities and access to proprietary fraud detection data.
Definitions of Terms
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Predictive Analytics: The use of data, statistical algorithms, and machine learning to forecast outcomes.
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Fraud Detection: Techniques and processes used to identify and prevent fraudulent activities.
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Fintech Startups: Emerging companies that use technology to provide innovative financial services.
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