Transforming Fraud Detection in Finance with Machine Learning Technologies
Year: 2025
Type: article
Abstract: This article explores how machine learning technologies are revolutionizing fraud detection in financial institutions. It examines the fundamental processes behind ML-based fraud detection systems, from data collection to model deployment, in terms accessible to beginners. Machine learning approaches—including supervised, unsupervised, semi-supervised, and deep learning techniques—offer significant advantages over traditional rule-based systems in detecting fraudulent activities. The implementation of these technologies enables real-time detection capabilities, reduces false positives, automates routine monitoring tasks, and enhances customer experience through a more nuanced understanding of behavioral patterns. However, challenges remain in model maintenance, explainability, and regulatory compliance. The article presents emerging technologies such as federated learning, adaptive learning strategies, and graph-based analytics that show promise for addressing current limitations while expanding fraud detection capabilities. Through understanding these technologies, even those without technical expertise can appreciate how artificial intelligence is strengthening financial security and protecting consumer assets.
Author: Sharath Reddy Polu
Institution: University of the Cumberlands
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6930474914
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