Articles Open Access
Cloud-Integrated Machine Learning Techniques for Intelligent Credit Card Fraud Detection Applications
Senior Software Engineer, UK
Abstract
The rapid growth of digital payment systems and online banking has significantly increased the risk of credit card fraud across global financial networks. Traditional fraud detection systems often struggle to identify sophisticated fraudulent activities due to the large volume of transactions and evolving attack patterns. Cloud-integrated machine learning techniques provide a scalable, efficient, and intelligent solution for detecting fraudulent transactions in real time. This study explores the application of machine learning algorithms combined with cloud computing technologies for intelligent credit card fraud detection. The proposed framework utilizes cloud infrastructure to process large-scale transactional data while employing machine learning models such as Decision Trees, Random Forest, Support Vector Machines, Neural Networks, and Deep Learning techniques to identify suspicious behavior patterns. Cloud integration enhances storage capacity, computational efficiency, data accessibility, and real-time monitoring capabilities. The study also discusses data preprocessing, feature engineering, anomaly detection, and model evaluation methods used in fraud detection systems. Furthermore, the research highlights the advantages and challenges associated with cloud-based fraud detection applications, including scalability, security concerns, latency, and privacy issues. The findings indicate that cloud-supported machine learning systems significantly improve fraud detection accuracy, reduce false positives, and strengthen financial transaction security in modern banking environments.
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Publication Information
| Journal | International Research Journal of Innovative Engineering |
|---|---|
| Volume | 1 |
| Number | 3 |
| Pages | 721-729 |
| Published | 2017-11-06 |
| Issue | Vol. 1 No. 3 (2017): International Research Journal of Innovative Engineering (IRJIE) |
References
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36. Amutha, M., & Sugumar, R. (2015). A survey on dynamic data replication system in cloud computing. International Journal of Innovative Research in Science, Engineering and Technology, 4(4), 1454-1467.
37. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian Journal of Science and Technology, 8(35), 1-5.
38. G. Vimal Raja, K. K. Sharma (2015). Applying Clustering technique on Climatic Data. Envirogeochimica Acta, 2(1), 21-27.
39. Z. Brijet, N. Bharathi, G. Shanmugapriya. (2015). Design of Model Predictive Controller for Fluid Catalytic Cracking Unit. Fluid-IJCTA, 8(5), 1917-1926.
40. Chiranjeevi, K. G., Latha, R., & Kumar, S. S. (2016). Enlarge Storing Concept in an Efficient Handoff Allocation during Travel by Time Based Algorithm. Indian Journal of Science and Technology, 9, 40.
41. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
42. Karthika, V., Brijet, Z., & Bharathi, N. (2012). Design of optimal controller for fluid catalytic cracking unit. Procedia Engineering, 38, 1150-1160.
2. Bahnsen, A. C., Aouada, D., Ottersten, B. (2014). Example-dependent cost-sensitive logistic regression for credit card fraud detection. International Conference on Machine Learning and Applications, 333–338.
3. 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.
4. Bolton, R. J., & Hand, D. J. (2012). Statistical fraud detection: A review. Statistical Science, 17(3), 235–255.
5. Carcillo, F., Dal Pozzolo, A., Le Borgne, Y., et al. (2015). Real-time scalable fraud detection framework using Spark. IEEE Big Data Conference, 1–8.
6. Chan, P. K., Fan, W., Prodromidis, A. L., & Stolfo, S. J. (2011). Distributed data mining in credit card fraud detection. IEEE Intelligent Systems, 14(6), 67–74.
7. Delamaire, L., Abdou, H., & Pointon, J. (2013). Credit card fraud and detection techniques: A review. Banks and Bank Systems, 4(2), 57–68.
8. Duman, E., & Ozcelik, M. H. (2011). Detecting credit card fraud by genetic algorithm and scatter search. Expert Systems with Applications, 38(10), 13057–13063.
9. Ghosh, S., & Reilly, D. L. (2010). Credit card fraud detection with a neural-network. System Sciences Journal, 3(4), 621–630.
10. Jha, S., Guillen, M., & Westland, J. C. (2012). Employing transaction aggregation strategy to detect credit card fraud. Expert Systems with Applications, 39(16), 12650–12657.
11. Maes, S., Tuyls, K., Vanschoenwinkel, B., & Manderick, B. (2010). Credit card fraud detection using Bayesian and neural networks. Proceedings of NAISO Congress, 261–270.
12. Ngai, E. W. T., Hu, Y., Wong, Y. H., et al. (2011). The application of data mining techniques in financial fraud detection. Decision Support Systems, 50(3), 559–569.
13. Panigrahi, S., Kundu, A., Sural, S., & Majumdar, A. K. (2012). Credit card fraud detection: A fusion approach using Dempster–Shafer theory and Bayesian learning. Information Fusion, 10(4), 354–363.
14. Phua, C., Lee, V., Smith, K., & Gayler, R. (2010). A comprehensive survey of data mining-based fraud detection research. Artificial Intelligence Review, 34(1), 1–14.
15. Quah, J. T. S., & Sriganesh, M. (2010). Real-time credit card fraud detection using computational intelligence. Expert Systems with Applications, 35(4), 1721–1732.
16. Sahin, Y., & Duman, E. (2011). Detecting credit card fraud by ANN and logistic regression. International Symposium on Innovations in Intelligent Systems and Applications, 315–319.
17. Srivastava, A., Kundu, A., Sural, S., & Majumdar, A. K. (2013). Credit card fraud detection using hidden Markov model. IEEE Transactions on Dependable and Secure Computing, 5(1), 37–48.
18. Stolfo, S., Fan, D., Lee, W., et al. (2011). Credit card fraud detection using meta-learning. KDD Workshop on Data Mining, 83–90.
19. Syeda, M., Zhang, Y. Q., & Pan, Y. (2012). Parallel granular neural networks for fast credit card fraud detection. IEEE International Conference on Fuzzy Systems, 572–577.
20. Whitrow, C., Hand, D. J., Juszczak, P., Weston, D., & Adams, N. M. (2011). Transaction aggregation as a strategy for credit card fraud detection. Data Mining and Knowledge Discovery, 18(1), 30–55.
21. Alex, A. T., Asari, V. K., & Mathew, A. (2012, October). Gradient feature matching for expression invariant face recognition using single reference image. In 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 851-856). IEEE.
22. Mathew, A., & Asari, V. K. (2012, August). Local histogram based descriptor for tracking in wide area imagery. In International Conference on Information Processing (pp. 119-128). Berlin, Heidelberg: Springer Berlin Heidelberg.
23. Murugeshwari, B., Sarukesi, K., & Jayakumar, C. (2010, March). An efficient method for knowledge hiding through database extension. In 2010 International Conference on Recent Trends in Information, Telecommunication and Computing (pp. 342-344). IEEE.
24. Sulthana, A. R., & Murugeswari, B. (2011, March). ARIPSO: Association rule interactive postmining using schemas and ontologies. In 2011 International Conference on Emerging Trends in Electrical and Computer Technology (pp. 941-946). IEEE.
25. Murugeshwari, B., Jayakumar, C., & Sarukesi, K. (2012). Secure Multi Party Computation Technique for Classification Rule Sharing. International Journal of Computer Applications, 55(7).
26. Singh, T. J., & Sugumar, R. (2012, December). An extensibility of THEMIS billing system for the cloud computing environment. In 2012 Fourth International Conference on Advanced Computing (ICoAC) (pp. 1-6). IEEE.
27. Kumar, J. (2013). Preservation of the Privacy for Multiple Custodian Systems with Rule Sharing. Journal of Computer Science.
28. Alex, A. T., Asari, V. K., & Mathew, A. (2013, March). Gradient feature matching for in-plane rotation invariant face sketch recognition. In Image Processing: Machine Vision Applications VI (Vol. 8661, pp. 46-58). SPIE.
29. Mathew, A., & Asari, V. K. (2013, March). Tracking small targets in wide area motion imagery data. In Video Surveillance and Transportation Imaging Applications (Vol. 8663, pp. 69-78). SPIE.
30. Natarajan, R., & Sugumar, R. (2014). A survey on attacks in web usage mining. International Journal of Innovative Research in Computer and Communication Engineering, 2(5), 4470-5.
31. Sugumar, R. (2014). A technique to stock market prediction using fuzzy clustering and artificial neural networks.
32. Priya, P. S., & Sugumar, R. (2014). Multi Keyword Searching Techniques over Encrypted Cloud Data. In IJSR.
33. Jagadeesh, S., & Soundappan, R. S. (2014). Survey on knowledge discovery in speech emotion detection. International Journal of Innovative Research in Computer and Communication Engineering, 2(5), 4476-4481.
34. Murugeshwari, B., & Sujatha, R. (2014). Preservation of Privacy for Multiparty Computation System with Homomorphic Encryption. International Journal of Emerging Technology and Advanced Engineering, 4(3), 530-535.
35. Mathew, A., & Asari, V. K. (2014, March). Rotation-invariant histogram features for threat object detection on pipeline right-of-way. In Video Surveillance and Transportation Imaging Applications 2014 (Vol. 9026, pp. 19-26). SPIE.
36. Amutha, M., & Sugumar, R. (2015). A survey on dynamic data replication system in cloud computing. International Journal of Innovative Research in Science, Engineering and Technology, 4(4), 1454-1467.
37. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian Journal of Science and Technology, 8(35), 1-5.
38. G. Vimal Raja, K. K. Sharma (2015). Applying Clustering technique on Climatic Data. Envirogeochimica Acta, 2(1), 21-27.
39. Z. Brijet, N. Bharathi, G. Shanmugapriya. (2015). Design of Model Predictive Controller for Fluid Catalytic Cracking Unit. Fluid-IJCTA, 8(5), 1917-1926.
40. Chiranjeevi, K. G., Latha, R., & Kumar, S. S. (2016). Enlarge Storing Concept in an Efficient Handoff Allocation during Travel by Time Based Algorithm. Indian Journal of Science and Technology, 9, 40.
41. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
42. Karthika, V., Brijet, Z., & Bharathi, N. (2012). Design of optimal controller for fluid catalytic cracking unit. Procedia Engineering, 38, 1150-1160.
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