A Study on the Fraud Detection for Electronic Prepayment using Machine Learning

Byung-Ho Choi, Nam-Wook Cho


Due to the recent development in electronic financial services, transactions of electronic prepayment are rapidly growing, leading to growing fraud attempts. This paper proposes a methodology that can effectively detect fraud transactions in electronic prepayment by machine learning algorithms, including support vector machines, decision trees, and artificial neural networks. Actual transaction data of electronic prepayment services were collected and preprocessed to extract the most relevant variables from raw data. Two different approaches were explored in the paper. One is a transaction-based approach, and the other is a user ID-based approach. For the transaction-based approach, the first model is primarily based on raw data features, while the second model uses extra features in addition to the first model. The user ID-based approach also used feature engineering to extract and transform the most relevant features. Overall, the user ID-based approach showed a better performance than the transaction-based approach, where the artificial neural networks showed the best performance. The proposed method could be used to reduce the damage caused by financial accidents by detecting and blocking fraud attempts. 


Full Text:



Bank of Korea, “Economic Statistics System - Search in Statistical Classification,” https://ecos.bok.or.kr/flex/EasySearch_e.jsp, 2022.01.29.

Breiman, L., Stone, C. J., Feieman, J. H., and Olshen, L. A., “Classification and regression trees,” Chapman & Hall/CRC, London, 1984.

Choi, B. H. and Cho, N. W., “A study on the fraud detection through sequential pattern analysis,” The Journal of Society for e-Business Studies, Vol. 26, No. 3, pp. 21-32, 2021.

Chung, Y. M. and Lim, H. Y., “An experimental study on text categorization using an SVM classifier,” Journal of the Korean Society for information Management, Vol. 17, No. 4, pp. 229-248, 2001.

Cortes, C. and Vapnik, V., “Support-vector networks,” Machine Learning, Vol. 20, No. 3, pp. 273-297, 1995.

Han, H. C., Kim, H. N., and Kim, H. K., “Fraud detection system in mobile payment service using data mining,” The Journal of Korea Institute of Information Security & Cryptology, Vol. 26, No. 6, pp. 1527-1537, 2016.

Hwang, S. W., “A study on distinction of deterioration of high speed railway track using an SVM,” Kangwon National University, 2013.

Jan, S. U., Lee, Y. D., Shin, J. P., and Koo, I. S., “Sensor fault classification based on support vector machine and statistical time-domain features,” IEEE Access, Vol. 5, pp. 8682-8690, 2017.

Jun, C. H., “Data Mining Techniques,” Hannarae Publishing Co, Seoul, 2012.

Korean National Police Agency, Cyber Investigation - Status for cyber crime arrest, https://www.police.go.kr/eng/statistics/statisticsSm/statistics04.jsp, 2022.01.29.

Lantz, B., “machine learning with R - Second Edition,” (Yoon, S. J., Trans), acornpub.co, Seoul, 2017 (Original work published 2015).

Lee, G. H., Shin, B. C., and Hur, J. W., “Fault classification of gear pumps using SVM,” Journal of Applied Reliability, Vol. 20, No. 2, pp. 187-196, 2020.

Lee, T. H. and Kook, K. H., “A study on detection of small size malicious code using data mining method,” Journal of Information and Security, Vol. 19, No. 1, pp. 11-17, 2019.

McCulloch, W. S. and Pitts, W., “A logical calculus of the ideas immanent in nervous activity,” The Bulletin of Mathematical Biophysics, Vol. 5, No. 4, pp. 115-133, 1943.

Park, J. H., Kim, H. K., and Kim, E. J., “Effective normalization method for fraud detection using a decision tree,” The Journal of Korea Institute of Information Security & Cryptology, Vol. 25, No. 1, pp. 133-146, 2015.

Park, K. R., Kim, J. H., and Lee, S. H., “facial Feature Verification System based on SVM Classifier,” KIPS Transactions on Software and Data Engineering, Vol. 11, No. 6, pp. 675-682, 2004.

Seo, J. H., “A Study on the Performance Evaluation of Unbalanced Intrusion Detection Dataset Classification based on Machine Learning,” Journal of Korean Institute of Intelligent Systems, Vol. 27, No. 5, pp. 466-474, 2017.

Seo, M. K., “Practical data processing and analysis using R,” Gilbut, Seoul, 2019.

Seoulshinmun, “Customer lost his phone and all his assets were stolen with Kakao Pay, but NaverPay was different,” https://www.seoul.co.kr/news/newsView.php?id=20220109500066, 2022.01.09.

Vapnik, V., “An overview of statistical learning theory,” IEEE Transactions on Neural Networks, Vol. 10, No. 5, pp. 988-999, 1999.

Vapnik, V., “The Nature of Statistical Learning Theory,” Springer, New York, NY, 1995.

Yang, E. M. and Seo, C. H., “A study on intrusion detection in network intrusion detection system using SVM,” The Society of Digital Policy & Management, Vol. 16, No. 5, pp. 399-406, 2018.

Yang, J. W., Lee, Y. D., and Koo, I. S., “Sensor fault detection scheme based on deep learning and support vector machine,” The Journal of The Institute of Internet Broadcasting and Communication (IIBC), Vol. 18, No. 2, pp. 185-195, 2018.

Yeo, W. K., Seo, Y. M., Lee, S. Y., and Jee, H. K., “Study on water stage prediction using hybrid model of artificial neural network and genetic algorithm,” Journal of Korea Water Resources Association, Vol. 43, No. 8, pp. 721-731, 2010.

Zhou, Z. H., “Machine Learning,” (Kim, K. H., Trans), Jeipub, Paju, Gyeonggi-do, 2020 (Original work published 2016).


  • There are currently no refbacks.