AI-Powered Fraud Prevention Systems in Financial Services

  • Authors

    • Ethan Harris Senior Software Engineer, Google, USA. Author

    DOI:

    https://doi.org/10.67228/3071642X/IJCFDE-2020PII3C8N

    Published 08-04-2020

  • Artificial Intelligence, Fraud Detection, Financial Services, Machine Learning, Deep Learning, Anomaly Detection, Financial Security, Predictive Analytics

    Issue

    Vol. 3 No. 2 (2020)

    Section

    Articles

    How to Cite

    Harris, E. (2020). AI-Powered Fraud Prevention Systems in Financial Services. International Journal of Commerce, Finance and Digital Economy, 3(2), 01-15. https://doi.org/10.67228/3071642X/IJCFDE-2020PII3C8N

  • Abstract

    The rapid growth of digital banking, e-commerce, electronic payments, and financial technology has increased both the volume and complexity of financial transactions, leading to greater fraud risks. Traditional rule-based fraud detection systems struggle to identify evolving and sophisticated fraud patterns. Artificial Intelligence (AI) offers an effective solution through machine learning, pattern recognition, and predictive analytics. This study explores AI-based fraud prevention systems in financial services, focusing on data acquisition, preprocessing, feature engineering, classification, anomaly detection, and real-time monitoring. Various machine learning models, including Random Forest, Support Vector Machines, Artificial Neural Networks, and Deep Learning, are evaluated for fraud detection. The findings indicate that AI-driven systems significantly improve fraud detection accuracy, reduce false positives, minimize operational losses, and enhance customer trust. The study concludes that AI is a critical component of modern financial security infrastructure and will play an increasingly important role in combating financial fraud.

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