Submit your paper : editorIJETjournal@gmail.com Paper Title : CREDIT CARD FRAUD DETECTION USING RANDOM FOREST CLASSIFIER ISSN : 2395-1303 Year of Publication : 2022 10.5281/zenodo.6894668 MLA Style: - M. SUSHMA, INDU VADANA CIGA, G.V. DEVAKI NANDAN, A. SATISH KUMAR, CREDIT CARD FRAUD DETECTION USING RANDOM FOREST CLASSIFIER , Volume 8 - Issue 4 July- August 2022 International Journal of Engineering and Techniques (IJET) ,ISSN:2395-1303 , www.ijetjournal.org APA Style: - M. SUSHMA, INDU VADANA CIGA, G.V. DEVAKI NANDAN, A. SATISH KUMAR, CREDIT CARD FRAUD DETECTION USING RANDOM FOREST CLASSIFIER , Volume 8 - Issue 4 July- August 2022 International Journal of Engineering and Techniques (IJET) ,ISSN:2395-1303 , www.ijetjournal.org Abstract In huge organizations, transactions take place constantly. There are studies which show that fraudulent transactions take place quite often. This causes significant amount of damage to the customers and the organizations due to loss of trust. It is not sensible to investigate every transaction mainly because it is highly time consuming which leads to customers exasperated. In our project, we are focusing on credit card fraud detection in the real-world. There are already many approaches taken to reduce and detect the credit card fraud transactions. The results of these are not very accurate. Our approach in improving the accuracy is using popular machine learning algorithm that belongs to supervised learning technique. Based on Accuracy, specificity, sensitivity and precision of the techniques, its performance is evaluated. Reference 1. P. Richhariya and P. K. Singh, “Evaluating and emerging payment card fraud challenges and resolution,” International Journal of Computer Applications, vol. 107, no. 14, pp. 5 – 10, 2014. 2. 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. 3. A.DalPozzolo,O.Caelen,Y.-A.LeBorgne, 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. 4. C. Phua, D. Alahakoon, and V. Lee, “Minority report in fraud detection: classification of skewed data,” ACM SIGKDD explorations newsletter, vol. 6, no. 1, pp. 50–59, 2004. 5. Z.-H. Zhou and X.-Y. Liu, “Training cost- sensitive neural networks with methods addressing the class imbalance problem,” IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 1, pp. 63– 77, 2006. 6. S. Ertekin, J. Huang, and C. L. Giles, “Active learning for class imbalance problem,” The 30th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 823–824, 2007. 7. M.WasikowskiandX. -w.Chen,“Combating the small sample class imbalance problem using feature selection,” IEEE Transactions on knowledge and data engineering, vol. 22, no. 10, pp. 1388– 1400, 2010. 8. S. Wang and X. Yao, “Multiclass imbalance problems: Analysis and potential solutions,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 42, no. 4, pp. 1119– 1130, 2012. 9. R. J. Bolton and D. J. Hand, “Statistical fraud detection: A review,” Statistical science, pp. 235–249, 2002. 10. D. J. Weston, D. J. Hand, N. M. Adams, and C. Whitrow, “Plastic card fraud detection using peer group analysis,” vol. 2, pp. 45–62, 2008. 11. 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. Keywords - CREDIT CARD FRAUD DETECTION USING RANDOM FOREST CLASSIFIER |