An Assessment of the Impact of Machine Learning in Fraud Detection: A Study of Fintech Firms in Kwara State
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Background of the Study
Machine learning (ML) has become an integral tool in fraud detection, enabling organizations to analyze vast datasets and identify anomalous patterns indicative of fraudulent activities. ML algorithms such as decision trees, neural networks, and support vector machines are used to detect and prevent fraud in real time, enhancing the security of financial transactions (Adesanya & Yusuf, 2025).
Fintech firms in Kwara State rely heavily on digital platforms for their operations, making them susceptible to fraud. While machine learning presents opportunities for enhanced fraud detection, its implementation and effectiveness in the local context remain underexplored. This study investigates the impact of ML on fraud detection in fintech firms in Kwara State.
Statement of the Problem
Fraudulent activities in the fintech sector undermine customer trust, increase operational costs, and pose significant risks to financial stability. Despite the potential of ML to combat fraud, many fintech firms in Kwara State face challenges such as limited technical expertise, high implementation costs, and algorithmic biases. This study seeks to evaluate the effectiveness of ML in addressing fraud detection challenges.
Objectives of the Study
- To assess the effectiveness of machine learning in detecting fraud in fintech firms in Kwara State.
- To identify challenges fintech firms face in implementing ML for fraud detection.
- To explore strategies for optimizing ML applications in fraud detection.
Research Questions
- How effective is machine learning in detecting fraud in fintech firms in Kwara State?
- What challenges do fintech firms face in implementing ML for fraud detection?
- What strategies can optimize the use of ML in fraud detection?
Research Hypotheses
- Machine learning is not significantly effective in detecting fraud in fintech firms in Kwara State.
- Challenges in implementing ML do not significantly hinder its effectiveness in fraud detection.
- Strategies for optimizing ML applications do not significantly improve fraud detection outcomes.
Scope and Limitations of the Study
The study focuses on fintech firms in Kwara State that have adopted machine learning for fraud detection between 2023 and 2025. Limitations include variability in ML adoption levels and access to sensitive fraud detection data.
Definitions of Terms
Machine Learning (ML): A subset of artificial intelligence enabling systems to learn and make predictions from data.
Fraud Detection: The process of identifying fraudulent activities in financial transactions.
Fintech Firms: Companies leveraging technology to provide financial services.
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