An Appraisal of AI-Driven Fraud Detection Models in Nigerian Banks: A Case Study in Yobe State
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- 1-5 Chapters
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- NGN 5000
Background of the Study
Fraud detection is a critical component of the banking sector’s operational framework, with artificial intelligence (AI) playing a transformative role. AI-driven models, such as neural networks and machine learning algorithms, enable banks to identify fraudulent transactions in real-time by analyzing patterns and anomalies in financial data.
In Nigeria, banks face increasing risks of fraud due to the growing sophistication of cyberattacks. Yobe State, like other parts of the country, has experienced significant challenges in mitigating fraud, necessitating the adoption of advanced technologies. Research by Nwachukwu and Oladipo (2024) highlights the potential of AI-driven models in reducing fraud and improving financial security. However, the effectiveness and adoption barriers of these models in Yobe State require further examination.
Statement of the Problem
Fraud remains a persistent challenge for Nigerian banks, with traditional detection methods proving inadequate against modern cyber threats. Banks in Yobe State face additional hurdles such as limited technical resources and high implementation costs for AI-driven solutions.
Although AI models offer enhanced capabilities, such as predictive analytics and real-time monitoring, their adoption in Yobe State’s banking sector has been inconsistent. Yusuf and Ibrahim (2023) observed that challenges such as data quality and lack of expertise often hinder the effectiveness of these models. This study appraises the use, impact, and challenges of AI-driven fraud detection models in Yobe State’s banking sector.
Objectives of the Study
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To evaluate the adoption level of AI-driven fraud detection models in banks in Yobe State.
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To assess the effectiveness of these models in reducing fraud.
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To identify challenges affecting their implementation in Yobe State.
Research Questions
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How widely are AI-driven fraud detection models adopted by banks in Yobe State?
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How effective are these models in mitigating fraud in the banking sector?
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What challenges hinder the implementation of AI-driven fraud detection models in Yobe State?
Research Hypotheses
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AI-driven fraud detection models are not significantly adopted by banks in Yobe State.
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AI-driven fraud detection models do not significantly reduce fraud in the banking sector.
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Challenges do not significantly hinder the adoption of AI-driven fraud detection models in Yobe State.
Scope and Limitations of the Study
The study focuses on banks in Yobe State, appraising the adoption and impact of AI-driven fraud detection models. Limitations include variability in model sophistication and restricted access to detailed fraud data for analysis.
Definitions of Terms
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AI-Driven Models: Algorithms and systems that utilize artificial intelligence for decision-making.
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Fraud Detection: The process of identifying and preventing fraudulent activities.
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Banking Sector: Financial institutions providing monetary services.
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