State-of-the-Art Machine Learning approaches for Fraud Detection in Financial Institutions
DOI:
https://doi.org/10.56472/ICCSAIML25-110Keywords:
Fraud detection, Machine Learning, Financial Institutions, Anomaly Detection, Supervised Learning, Unsupervised Learning, Deep Learning, Reinforcement Learning, Hybrid Models, Credit Card Fraud, Identity Theft, Data Preprocessing, Model Evaluation, Financial Security, Artificial IntelligenceAbstract
Financial fraud poses a significant threat to the viability & the integrity of financial institutions, leading to the significant monetary losses, reputational damage & their regulatory complications. Although conventional rule-based fraud detection systems are more effective, they encounter difficulty in adapting to the evolving & intricate nature of their fraudulent activity. ML has become a useful tool for their fraud detection since it helps to analyze large transactional data, identify complex patterns, and really discover anomalies by timing. This work explores hybrid, unsupervised, and supervised models as advanced ML techniques for fraud detection. Decision trees, random forests & the deep neural networks are the most frequently employed supervised learning methods due to their exceptional accuracy in identifying their fraudulent transactions. Unsupervised models, including autoencoders & clustering algorithms, are particularly adept at identifying emerging fraud trends without the necessity of previously labeled content. Furthermore, hybrid approaches take advantage of both paradigms to improve the detection's performance. The article highlights significant developments in the domains of graph-based fraud detection, FL for the privacy-preserving analysis & explainable artificial intelligence (XAI) integration to increase the model interpretability and their regulatory conformity. The accuracy, adaptability & the efficacy of ML-based fraud detection systems are significantly higher than those of traditional methods, as evidenced by experimental findings & case analyses. Nevertheless, challenges continue to exist, including the need for the continuous model retraining, aggressive assaults & data asymmetry. The results highlight the need of reaching financial fraud reduction by means of the scalable, interpretable, and strong ML models. By offering a thorough analysis of the sophisticated ML algorithms, their practical uses, and the possible research prospects in the field of fraud detection inside the financial institutions, this work adds to the present body of knowledge. Legislators, financial experts, and data scientists working to improve their fraud detection systems in a financial environment going more and more digital and complicated depend on the insights provided
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References
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