순차패턴 분석을 통한 이상금융거래탐지 연구: 선불전자지급수단 거래를 중심으로 A Study on the Fraud Detection through Sequential Pattern Analysis: Focused on Transactions of Electronic Prepayment원문보기
최병호
(Department of Industrial & Information Systems, Graduate school of Public Policy and Information Technology, Seoul National University of Science & Technology)
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조남욱
(Department of Industrial & Information Systems Engineering, Seoul National University of Science & Technology)
정보통신기술의 발달로 전자금융서비스가 활성화됨에 따라 선불전자지급수단을 이용한 전자금융거래도 증가하고 있다. 선불전자지급수단의 다양한 순기능에도 불구하고, 현금화가 용이하다는 점 때문에 전자금융사기에 악용되는 사례가 증가하고 있다. 본 논문에서는 선불전자 지급수단의 금융거래내역에 순차패턴 마이닝 기법을 적용하여 이상금융거래를 탐지하는 방안을 제시하였다. 선불전자지급수단의 금융거래내역을 서비스이용 순서로 나열한 다음 순차패턴 마이닝을 통해 이상금융거래 탐지패턴을 추출하였다. 도출된 패턴을 실제 금융거래 데이터에 적용하는 실험을 통해 방법론의 효과성을 검증하였다. 실험결과 테스트 데이터의 탐지성능 정확도가 95.6퍼센트로 나타나 제시된 방법론이 이상금융거래를 효과적으로 탐지할 수 있음을 확인하였다. 본 논문에서 제시한 방법론은 향후 이상금융거래탐지시스템 분석모델에 적용함으로써 전자금융사고 피해를 줄이는데 활용될 수 있을 것으로 기대된다.
정보통신기술의 발달로 전자금융서비스가 활성화됨에 따라 선불전자지급수단을 이용한 전자금융거래도 증가하고 있다. 선불전자지급수단의 다양한 순기능에도 불구하고, 현금화가 용이하다는 점 때문에 전자금융사기에 악용되는 사례가 증가하고 있다. 본 논문에서는 선불전자 지급수단의 금융거래내역에 순차패턴 마이닝 기법을 적용하여 이상금융거래를 탐지하는 방안을 제시하였다. 선불전자지급수단의 금융거래내역을 서비스이용 순서로 나열한 다음 순차패턴 마이닝을 통해 이상금융거래 탐지패턴을 추출하였다. 도출된 패턴을 실제 금융거래 데이터에 적용하는 실험을 통해 방법론의 효과성을 검증하였다. 실험결과 테스트 데이터의 탐지성능 정확도가 95.6퍼센트로 나타나 제시된 방법론이 이상금융거래를 효과적으로 탐지할 수 있음을 확인하였다. 본 논문에서 제시한 방법론은 향후 이상금융거래탐지시스템 분석모델에 적용함으로써 전자금융사고 피해를 줄이는데 활용될 수 있을 것으로 기대된다.
Due to the recent development in electronic financial services, transactions of electronic prepayment are rapidly increasing. The increased transactions of electronic prepayment, however, also leads to the increased fraud attempts. It is mainly because electronic prepayment can easily be converted i...
Due to the recent development in electronic financial services, transactions of electronic prepayment are rapidly increasing. The increased transactions of electronic prepayment, however, also leads to the increased fraud attempts. It is mainly because electronic prepayment can easily be converted into cash. The objective of this paper is to develop a methodology that can effectively detect fraud transactions in electronic prepayment, by using sequential pattern mining techniques. To validate our approach, experiments on real transaction data were conducted and the applicability of the proposed method was demonstrated. As a result, the accuracy of the proposed method has been 95.6 percent, showing that the proposed method can effectively detect fraud transactions. The proposed method could be used to reduce the damage caused by the fraud attempts of electronic prepayment.
Due to the recent development in electronic financial services, transactions of electronic prepayment are rapidly increasing. The increased transactions of electronic prepayment, however, also leads to the increased fraud attempts. It is mainly because electronic prepayment can easily be converted into cash. The objective of this paper is to develop a methodology that can effectively detect fraud transactions in electronic prepayment, by using sequential pattern mining techniques. To validate our approach, experiments on real transaction data were conducted and the applicability of the proposed method was demonstrated. As a result, the accuracy of the proposed method has been 95.6 percent, showing that the proposed method can effectively detect fraud transactions. The proposed method could be used to reduce the damage caused by the fraud attempts of electronic prepayment.
Agrawal, R. and Srikant, R., "Mining Sequential Patterns," In Proc. Intl. Conf. on Data Engineering, 1995.
Bank of Korea, "Electronic payment service usage during the first half of 2020," Bank of Korea Press Releases, 2020.
Choi, E. S. and Lee, K. H., "A Study on Improvement of Effectiveness Using Anomaly Analysis rule modification in Electronic Finance Trading," Journal of the Korea Institute of Information Security and Cryptology, Vol. 25, No. 3, pp. 615-625, 2015.
Choi, P. S., "Sequential Pattern Mining based on Dynamic Weight in Data Stream," Chonnam National University, 2013.
Financial Security Institute, "Fraud Detection System Technology Guard," Financial Security Institute, 2014-08, 2014.
Financial Services Commission, "Electronic financial transactions ACT," Korea Ministry of Government Legislation, No. 17354, 2020.
Financial Services Commission, "FSC Designates Two More Financial Solutions as 'Innovative Financial Services' ", Financial Services Commission Press Releases, 2021.
Financial Services Commission, "Government Unveils Plans to Root Out Vishing. Financial Services Commission," Financial Services Commission, 2020.
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 and Cryptology, Vol. 26, No. 6, pp. 1527-1537, 2016.
Hwang, Y. J., "Searching for Frequent Failure Patterns of Control Systems through Sequential Pattern Mining of Events," Chungbuk National University, 2014.
Jun, C. H., Data Mining Techniques, pp. 437-462, published by Hannarae Publishing Co, Seoul, 2012.
Kim, H., "Efficient Interval Sequence Pattern Mining Using Minimizing Candidate set for Event Sequence," Chonnam National University, 2014.
Kim, W. S., Kim, Y. H., Park, H. S., and Park, J. K., "Analysis of Traffic Card Big Data by Hadoop and Sequential Mining Technique," Journal of Information Technology Applications & Management, Vol. 24, No. 4, pp. 187-196, 2017.
Lim, C. H., "The Need for Active Judicial Relief against Electronic Financial Fraud," Kyungpook National University Law Journal, Vol. 65, pp. 257-282, 2019.
Park, C. S. and Lee, J. H., "General Study Paper: A Review of Sequential Pattern Mining Algorithms," The Statictical Review, Vol. 11, pp. 56-73, 2003.
Park, E. Y. and Yoon, J. W., "A Study of Accident Prevention Effect through Anomaly Analysis in E-Banking," The Journal of Society for e-Business Studies, Vol. 19, No. 4, pp. 119-134, 2014.
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 and Cryptology, Vol. 25, No. 1, pp. 133-146, 2015.
Yoo, S. W., "Study on a Real Time Based Suspicious Transaction Detection and Analysis Model to Prevent Illegal Money Transfer Through E-Banking Channels," Journal of the Korea Institute of Information Security and Cryptology, Vol. 26, No. 6, pp. 1513-1526, 2016.
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