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.

  • References

    [1] R. J. Bolton and D. J. Hand, “Statistical fraud detection: A review,” Statistical Science, vol. 17, no. 3, pp. 235–255, 2002.

    [2] P. K. Chan and S. J. Stolfo, “Toward scalable learning with non-uniform class and cost distributions: A case study in credit card fraud detection,” Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, pp. 164–168, 1998.

    [3] C. Phua, V. Lee, K. Smith, and R. Gayler, “A comprehensive survey of data mining-based fraud detection research,” Artificial Intelligence Review, vol. 34, no. 1, pp. 1–14, 2010.

    [4] D. Dorronsoro, F. Ginel, C. Sánchez, and C. Cruz, “Neural fraud detection in credit card operations,” IEEE Transactions on Neural Networks, vol. 8, no. 4, pp. 827–834, 1997.

    [5] S. Bhattacharyya, S. Jha, K. Tharakunnel, and J. C. Westland, “Data mining for credit card fraud: A comparative study,” Decision Support Systems, vol. 50, no. 3, pp. 602–613, 2011.

    [6] A. Dal Pozzolo, O. Caelen, Y. Le Borgne, S. Waterschoot, and G. Bontempi, “Learned lessons in credit card fraud detection from a practitioner perspective,” Expert Systems with Applications, vol. 41, no. 10, pp. 4915–4928, 2014.

    [7] A. Ngai, Y. Hu, Y. Wong, Y. Chen, and X. Sun, “The application of data mining techniques in financial fraud detection: A classification framework and academic review,” Decision Support Systems, vol. 50, no. 3, pp. 559–569, 2011.

    [8] V. J. Hodge and J. Austin, “A survey of outlier detection methodologies,” Artificial Intelligence Review, vol. 22, no. 2, pp. 85–126, 2004.

    [9] [9] T. Fawcett and F. Provost, “Adaptive fraud detection,” Data Mining and Knowledge Discovery, vol. 1, no. 3, pp. 291–316, 1997.

    [10] N. Jurgovsky, M. Granitzer, S. Ziegler, S. Calabretto, P. Portier, L. He-Guelton, and O. Caelen, “Sequence classification for credit-card fraud detection,” Expert Systems with Applications, vol. 100, pp. 234–245, 2018.

    [11] J. West and M. Bhattacharya, “Intelligent financial fraud detection practices: An investigation,” Computers & Security, vol. 36, pp. 47–66, 2013.

    [12] Y. Sahin and E. Duman, “Detecting credit card fraud by decision trees and support vector machines,” International MultiConference of Engineers and Computer Scientists, vol. 1, pp. 442–447, 2011.

    [13] A. Whitrow, D. Hand, P. Juszczak, D. Weston, and N. Adams, “Transaction aggregation as a strategy for credit card fraud detection,” Data Mining and Knowledge Discovery, vol. 18, no. 1, pp. 30–55, 2009.

    [14] K. Randhawa, C. Loo, M. Seera, C. Lim, A. Nandi, and R. Raihani, “Credit card fraud detection using AdaBoost and majority voting,” IEEE Access, vol. 6, pp. 14277–14284, 2018.

    [15] J. Carcillo, Y. Le Borgne, O. Caelen, and G. Bontempi, “Streaming active learning strategies for real-life credit card fraud detection,” Data Mining and Knowledge Discovery, vol. 32, no. 3, pp. 734–761, 2018.

    [16] A. Fiore, A. De Santis, F. Perla, P. Zanetti, and F. Palmieri, “Using generative adversarial networks for improving classification effectiveness in credit card fraud detection,” Information Sciences, vol. 479, pp. 448–455, 2019.

    [17] S. Roy and M. Sunitha, “Fraud detection in credit card transactions using machine learning algorithms,” Procedia Computer Science, vol. 165, pp. 631–641, 2019.

    [18] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016.

    [19] D. Kingma and M. Welling, “Auto-encoding variational Bayes,” International Conference on Learning Representations (ICLR), 2014.

    [20] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in Neural Information Processing Systems, vol. 30, pp. 5998–6008, 2017.

    [21] S. Lundberg and S. Lee, “A unified approach to interpreting model predictions,” Advances in Neural Information Processing Systems, vol. 30, pp. 4765–4774, 2017.

    [22] N. Kshetri, “The role of artificial intelligence in combating financial fraud,” IT Professional, vol. 22, no. 1, pp. 67–71, 2020.

    [23] Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015.

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