Priya Krishnan
Independent Researcher
India
Abstract
This manuscript explores data mining approaches for fraud detection in banking systems, focusing on techniques available up to 2015. It presents an overview of common fraud patterns, a structured literature review of key studies in tabular form, statistical analyses, and detailed methodological procedures. The research outlines five core objectives, discusses results obtained from applying clustering and classification algorithms, and concludes with insights on system performance. Finally, it offers future research directions to enhance fraud detection capabilities in banking environments.
Keywords
Data mining, Fraud detection, Banking systems, Classification, Clustering
References
- Bolton, R. J., & Hand, D. J. (2002). Statistical fraud detection: A review. Statistical Science, 17(3), 235–249.
- Cortes, C., & Pregibon, D. (2001). Fraud detection using regression analysis. Proceedings of the Workshop on Data Analysis, 15–26.
- Ngai, E. W. T., 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.
- Nishanth, S., Sabitha, R., & Soman, K. P. (2009). Loan fraud detection using decision trees and neural networks. International Journal of Computer Applications, 10(2), 24–29.
- Phua, C., Lee, V., Smith, K., & Gayler, R. (2005). A comprehensive survey of data mining-based fraud detection research. Artificial Intelligence Review, 33(1–2), 1–14.
- Chan, P. K., & Stolfo, S. J. (1998). 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, 164–168.
- Whitrow, C., Hand, D. J., Juszczak, P., Weston, D., & Adams, N. M. (2009). Transaction aggregation as a strategy for credit card fraud detection. Data Mining and Knowledge Discovery, 18(1), 30–55.
- Sahin, Y., & Duman, E. (2011). Detecting credit card fraud by decision trees and support vector machines. International MultiConference of Engineers and Computer Scientists, 1, 442–447.
- 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.
- West, J., Dellana, S., & Qian, J. (2006). Neural network ensemble strategies for financial decision applications. Computers & Operations Research, 33(10), 2944–2961.
