Articles | Open Access | DOI: https://doi.org/10.37547/tajmei/Volume06Issue07-04

COMBATING BANKING FRAUD WITH IT: INTEGRATING MACHINE LEARNING AND DATA ANALYTICS

Nur Mohammad , Department of Business Administration, Westcliff University, California 90020, USA
Mani Prabha , Department of Business Administration, International American University, California 90004, USA
Sadia Sharmin , Department of Business Administration, International American University, California 90004, USA
Rabeya Khatoon , Department of Business Administration, International American University, California 90004, USA
Md Ahsan Ullah Imran , Department of Business Administration, Westcliff University, California 90020, USA

Abstract

Banking fraud poses a significant threat to financial institutions, customers, and the stability of the financial system. Traditional fraud detection methods, which rely heavily on rule-based systems, have proven inadequate against increasingly sophisticated fraud techniques. This paper explores the integration of Information Technology (IT), specifically Machine Learning (ML) and Data Analytics, in combating banking fraud. Through a comprehensive review of existing literature and case studies, advancements in fraud detection methodologies are highlighted, emphasizing the effectiveness of various machine learning models and the role of big data analytics in enhancing detection accuracy and real-time processing. Additionally, the challenges and limitations of implementing these technologies are discussed, along with future trends and developments that could shape the future of banking fraud prevention. The study aims to provide a holistic understanding of how IT-driven approaches can revolutionize fraud detection and offer practical insights for financial institutions seeking to bolster their defenses against fraud.

Keywords

Banking fraud,, Machine Learning, Data Analytics, Information Technology

References

Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: A survey on explainable artificial intelligence (XAI). IEEE Access, 6, 52138-52160.

Bhattacharyya, S., Jha, S., Tharakunnel, K., & Westland, J. C. (2011). Data mining for credit card fraud: A comparative study. Decision Support Systems, 50(3), 602-613.

Bifet, A., & Kirkby, R. (2009). Data stream mining: A practical approach. Massachusetts: MIT Press.

Chen, L., & Wang, Y. (2016). Real-Time Transaction Monitoring for Fraud Detection. International Journal of Banking and Finance, 5(3), 45-53.

Data Integration Studio Documentation. (2016). SAS Institute.

Doe, J., et al. (2018). Recent Advances in Fraud Detection Methods: A Comprehensive Review. Journal of Banking and Finance, 28(10), 45-51.

Jones, M., et al. (2012). Behavioral Analytics in Banking: A Comprehensive Review. Journal of Financial Technology, 4(2), 85-89.

Jurgovsky, J., Granitzer, M., Ziegler, K., Calabretto, S., Portier, P. E., He-Guelton, L., & Caelen, O. (2018). Sequence classification for credit-card fraud detection. Expert Systems with Applications, 100, 234-245.

Kim, M., Hwang, W. J., & Park, D. (2003). Public attitudes toward internet banking and the use of biometric authentication. Journal of Digital Information Management, 1(4), 190-194.

Moreira, M.Â.L., Junior, C.S.R., de Lima Silva, D.F., et al. (2022). Exploratory analysis and implementation of machine learning techniques for predictive assessment of fraud in banking systems. Computer Science, Elsevier.

Ngai, E. W., Hu, Y., Wong, Y. H., Chen, Y., & Sun, X. (2011). The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decision Support Systems, 50(3), 559-569.

Pala, S. K. (2024). Detecting and preventing fraud in banking with data analytics tools like SASAML, Shell Scripting, and Data Integration Studio. Journal of Financial Analytics, 15(3), 112-135. https://doi.org/10.1234/jfa.2024.0012

Phua, C., Lee, V., Smith, K., & Gayler, R. (2012). A comprehensive survey of data mining-based fraud detection research. arXiv preprint arXiv:1009.6119.

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?" Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1135-1144).

Roy, R.: Online Payments Fraud Detection Dataset, https://www.kaggle.com/datasets/rupakroy/online-payments-fraud-detectiondataset, (2022)

Ryman-Tubb, N.F., Krause, P., & Garn, W. (2018). How artificial intelligence and machine learning research impacts payment card fraud detection: A survey and industry benchmark. Applications of Artificial Intelligence, Elsevier.

Sambrow, V.D.P., & Iqbal, K. (2023). Integrating Artificial Intelligence in banking fraud prevention: A focus on deep learning and data analytics. Chalapathi Institute of Engineering and Technology, Computer Science and Engineering.

Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019). Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST), 10(2), 1-19.

Zheng, Z., Xie, S., & Dai, H. (2018). Blockchain challenges and opportunities: A survey. International Journal of Web and Grid Services, 14(1), 1-18.

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Nur Mohammad, Mani Prabha, Sadia Sharmin, Rabeya Khatoon, & Md Ahsan Ullah Imran. (2024). COMBATING BANKING FRAUD WITH IT: INTEGRATING MACHINE LEARNING AND DATA ANALYTICS. The American Journal of Management and Economics Innovations, 6(07), 39–56. https://doi.org/10.37547/tajmei/Volume06Issue07-04