AN ANALYTICAL STUDY ON THE APPLICATION OF MACHINE LEARNING TECHNIQUES IN FRAUD DETECTION SYSTEMS
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Fraud detection has become one of the most critical challenges confronting the modern digital economy, with global financial losses attributable to fraudulent transactions projected to surpass USD 400 billion over the next decade. Traditional rule-based systems, while interpretable, are increasingly inadequate against dynamic, adaptive fraud schemes orchestrated through coordinated fraud rings, synthetic identities, and adversarial transaction manipulation. This review paper presents a systematic and analytical examination of machine learning (ML) techniques applied to fraud detection across financial, e-commerce, insurance, and healthcare sectors. Employing a structured literature survey methodology aligned with PRISMA guidelines, this study synthesizes findings from over 60 peer-reviewed publications spanning 2019 to 2025. The review comprehensively covers classical supervised methods (Logistic Regression, Decision Trees, Random Forest, Support Vector Machines), ensemble techniques (XGBoost, LightGBM, CatBoost), deep learning architectures (LSTM, Autoencoder, CNN, Transformer), and cutting-edge Graph Neural Network (GNN) models such as CARE-GNN, STA-GT, and FraudGT. Persistent challenges including class imbalance, concept drift, adversarial evasion, computational latency, and regulatory interpretability requirements are critically analyzed alongside mitigation strategies. Emerging paradigms including federated learning, explainable AI (XAI), and large language model (LLM)-based approaches are identified as defining future directions. This review serves as a consolidated reference for researchers and practitioners navigating the rapidly evolving landscape of ML-driven fraud detection.
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Agarwal, L. (2026). An Analytical Study on the Application of Machine Learning Techniques in Fraud Detection Systems. International Journal of Science, Strategic Management and Technology, 02(05). https://doi.org/10.55041/ijsmt.v2i5.208
Agarwal, Laksh. "An Analytical Study on the Application of Machine Learning Techniques in Fraud Detection Systems." International Journal of Science, Strategic Management and Technology, vol. 02, no. 05, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i5.208.
Agarwal, Laksh. "An Analytical Study on the Application of Machine Learning Techniques in Fraud Detection Systems." International Journal of Science, Strategic Management and Technology 02, no. 05 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i5.208.
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