Email this article A New Hybrid Method For Credit Card Fraud Detection On Financial Data
Abstract
Credit card fraud is a major issue in financial administrations. Billions of dollars are lost because of credit card misrepresentation consistently. There is an absence of research contemplates on breaking down certifiable Visa information attributable to privacy issues. In this paper, AI algorithms are utilized to identify Visa misrepresentation. Standard models are right off the bat utilized. At that point, half breed strategies which use AdaBoost and greater part casting ballot techniques are connected. To assess the model adequacy, a freely accessible credit card informational collection is utilized. At that point, a genuine Visa informational index from a money related organization is investigated. What's more, commotion is added to the information tests to further survey the robustness of the algorithms.
References
Y. Sahin, S. Bulkan, and E. Duman, “A cost-sensitive decision tree approach for fraud detection,†Expert Systems with Applications, vol. 40, no. 15, pp. 5916–5923, 2013.
A. O. Adewumi and A. A. Akinyelu, “A survey of machine-learning and nature-inspired based credit card fraud detection techniques,†International Journal of System Assurance Engineering and Management, vol. 8, pp. 937–953, 2017.
A. Srivastava, A. Kundu, S. Sural, A. Majumdar, “Credit card fraud detection using hidden Markov model,†IEEE Transactions on Dependable and Secure Computing, vol. 5, no. 1, pp. 37–48, 2008.
The Nilson Report (October 2016) [Online]. Available: https://www.nilsonreport.com/upload/content_promo/The_Nilson_R eport_10-17-2016.pdf
J. T. Quah, and M. Sriganesh, “Real-time credit card fraud detection using computational intelligence,†Expert Systems with Applications, vol. 35, no. 4, pp. 1721–1732, 2008.
S. Bhattacharyya, S. Jha, K. Tharakunnel, and J. C., “Data mining for credit card fraud: A comparative study,†Decision Support Systems, vol. 50, no. 3, pp. 602–613, 2011. [
N. S. Halvaiee and M. K. Akbari, “A novel model for credit card fraud detection using Artificial Immune Systems,†Applied Soft Computing, vol. 24, pp. 40–49, 2014.
S. Panigrahi, A. Kundu, S. Sural, and A. K. Majumdar, “Credit card fraud detection: A fusion approach using Dempster–Shafer theory and Bayesian learning,†Data Fusion, vol. 10, no. 4, pp. 354–363, 2009. [9] N. Mahmoudi and E. Duman, “Detecting credit card fraud by modified Fisher discriminant analysis,†Expert Systems with Applications, vol. 42, no. 5, pp. 2510–2516, 2015.
D. Sánchez, M. A. Vila, L. Cerda, and J. M. Serrano, “Association rules applied to credit card fraud detection,†Expert Systems with Applications, vol. 36, no. 2, pp. 3630–3640, 2009.
E. Duman and M. H. Ozcelik, “Detecting credit card fraud by genetic algorithm and scatter search,†Expert Systems with Applications, vol. 38, no. 10, pp. 13057–13063, 2011.
P. Ravisankar, V. Ravi, G. R. Rao, and I. Bose, “Detection of financial statement fraud and feature selection using data mining
techniques,†Decision Support Systems, vol. 50, no. 2, pp. 491–500, 2011.
E. Kirkos, C. Spathis, and Y. Manolopoulos, “Data mining techniques for the detection of fraudulent financial statements,†Expert Systems with Applications, vol. 32, no. 4, pp. 995–1003, 2007.
F. H. Glancy and S. B. Yadav, “A computational model for financial reporting fraud detection,†Decision Support Systems, vol. 50, no. 3, pp. 595–601, 2011.
D. Olszewski, “Fraud detection using self-organizing map visualizing the Client profiles,†Knowledge-Based Systems, vol. 70, pp. 324–334, 2014.
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