An Evaluation of Machine Learning in Fraud Risk Assessment: A Case Study of Digital Payment Platforms in Benue State
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- NGN 5000
Background of the Study
Fraud risk assessment is an essential part of ensuring the security and trustworthiness of digital payment platforms. Machine learning algorithms are increasingly being used in fraud detection to analyze patterns, identify anomalies, and predict fraudulent activities based on historical data (Taylor & Nwachukwu, 2024). These algorithms learn from data over time, improving their predictive capabilities and helping businesses detect and prevent fraud more effectively.
In Benue State, digital payment platforms are adopting machine learning to enhance their fraud risk assessment processes. While these platforms promise greater accuracy in fraud detection, there are concerns about the adequacy of these systems in the context of local regulatory frameworks, customer behaviors, and data availability. This study assesses the role and effectiveness of machine learning in fraud risk assessment within digital payment platforms in Benue State.
Statement of the Problem
Digital payment platforms in Benue State face significant challenges in identifying and mitigating fraudulent activities due to the complex nature of digital transactions (Okoro & Daramola, 2023). While machine learning offers a promising solution, its integration into fraud risk assessment systems has been inconsistent and may not fully address the unique challenges faced by these platforms, such as limited data quality and access to advanced computational resources. This study explores the impact of machine learning on improving fraud detection and risk assessment in digital payment platforms.
Objectives of the Study
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To evaluate the use of machine learning algorithms in fraud risk assessment by digital payment platforms in Benue State.
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To assess the effectiveness of machine learning models in detecting and preventing fraudulent activities on these platforms.
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To identify the challenges faced by digital payment platforms in implementing machine learning-based fraud risk assessments.
Research Questions
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To what extent are machine learning algorithms used in fraud risk assessment by digital payment platforms in Benue State?
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How effective are machine learning models in detecting and preventing fraudulent activities?
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What challenges hinder the implementation of machine learning-based fraud risk assessments in Benue State’s digital payment platforms?
Research Hypotheses
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There is no significant relationship between the use of machine learning algorithms and the effectiveness of fraud risk assessment.
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Machine learning models do not significantly improve fraud detection and prevention in digital payment platforms.
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Challenges in implementing machine learning algorithms significantly hinder fraud risk assessment in Benue State.
Scope and Limitations of the Study
The study focuses on digital payment platforms operating in Benue State. Limitations include the availability of fraud-related data, the quality of machine learning models, and varying levels of technology adoption across platforms.
Definitions of Terms
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Machine Learning: A type of artificial intelligence that enables systems to learn from data and make predictions without explicit programming.
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Fraud Risk Assessment: The process of identifying, analyzing, and mitigating potential fraud risks in financial transactions.
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Digital Payment Platforms: Online platforms that facilitate electronic transactions between businesses and customers.
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