Anti-Fraud System for Credit Card By Using Hybrid Technique

Hybrid 기법을 적용한 효율적인 신용카드판단시스템

  • Published : 2004.09.01

Abstract

An anti-fraud system that utilizes association rules of fraud as well as AFS (Anti Fraud System) for credit card payments in e-commerce is proposed. The association rules are found by applying the data mining algorithm to millions of transaction records that have been generated as a result of orders on goods through the Internet. When a customer begins to process an order by using transaction components of a secure messaging protocol, the degree of risk for the transaction is assessed by using the found rules. More credit information will be requested or the transaction is rejected if it is interpreted as risky.

인터넷상의 전자상거래 주문에서 발생하는 수백만건의 트랜잭션 레코드들에 대해 Hybrid 기법으로 데이터마이닝 기술인 연관규칙 탐사기법과 AFS (Anti Fraud System) 를 활용하여 전자상거래 과정에서 흔히 일어날 수 있는 부정 거래를 최소화할 수 있는 새로운 전자결제 신용카드 사기방어시스템을 제안한다. 고객이 웹 상의 거래 콤포넌트에 의한 보안 메세징 프로토콜을 사용하여 거래를 시도하면 과거 트랜잭션 데이터를 이용하여 미리 생성해 둔 사기성 거래에 대한 연관규칙의 적용으로 거래의 위험도를 판단하여 위험도가 높다고 판단될 경우 부가적 신용 정보를 요구하거나 거래를 중단하는 시스템이다 본 시스템의 장점은 기존의 사기방지시스템 보다 빠른 응답성과 그에 따른 효율성을 들 수 있다.

Keywords

References

  1. Glasheen, C. and Dowling, S., Increasing Internet Sales, Comm. IDC, Bulletin #W25213 - July 2001, Internet URL
  2. Information Technology OSI Systems Management., Objects and Attributes for Access Control, ISO/IEC 10164-9, JTC1, 1995
  3. P.K. Chan, and Fan, W., Distributed Data Mining in Credit Card Fraud Detection, IEEE Intelligent Systems, November/December 1999, pp. 67-74 https://doi.org/10.1109/5254.809570
  4. SAS Institute Inc., Using Data Mining Techniques for Fraud Detection: A Best Practices Approach to Government Technology Solutions, Internet URL
  5. Provost, F. and Fawcett, T., Robust Classification for Imprecise Environments. In: Machine Learning, Vol. 42, No. 3, pp. 203-231, 2001 https://doi.org/10.1023/A:1007601015854
  6. Stolfo, S. Fan, W. Lee, W. Prodromidis, A. and Chan, P., Cost-Based Modeling for Fraud and Instruction Detection: Results from the JAM Project, Proc. DARPA Information Survivability Conference and Exposition, IEEE Computer Press, pp.l30-144, 2000
  7. Merchant Works., E-commerce Glossary, Internet URL
  8. Verisign White Paper., Alternative Approaches to Managing Fraud, Internet URL
  9. 송용욱, 성기윤, 인터넷상의 전자 지불 시스템, Biz-on-Net, pp. 298-299, Internet URL
  10. Master Card International s., The Developments of the SET Protocol, Internet URL < http://www.mastercard.com/set/>
  11. J.S. Park, M. Chen, and P.S. Yu., Using a Hash-Based Method with Transaction Trimming for Mining Association Rules, IEEE Transactions on Knowledge and Data Engineering, Vol. 9, No.5, pp.813-825, Sept. 1997. https://doi.org/10.1109/69.634757
  12. J. Han, and Kamber, M., Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers, 2000
  13. Agrawal, R. Mannila, H. Srikant, R. Toivonen, H. and A.I. Verkamo, Fast Discovery of Association Rules, In Advances in Knowledge Discovery and Data Mining, U.M. Fayyad, Piatetsky-Shapiro, G. Smith, P. and Uthurusamy, R. ed., AAAI Press/The MIT Press, pp. 307-328, 1996
  14. Bonchi, F. Giannotti, F. Mainetto, G. and Pedreschi, D., A classification-based Methodology for Planning Audit Strategies in Fraud Detection, ACM Press New York, NY, USA, pp. 175 - 184, Series-Proceeding-Article Year of Publication, 1999 https://doi.org/10.1145/312129.312224
  15. Tse-Hua, Lan. and Ahmed, H., Fraud Detection and Self Embedding, ACM Press New York, NY, USA, pp. 33 - 36, Series-Proceeding-Article Year of Publication, 1999 https://doi.org/10.1145/319878.319887
  16. Rosset, S. Murad, U. Neumann, E. Idan, Y. and Pinkas, G., Discovery of Fraud Rules for Telecommunications-Challenges and Solutions, ACM Press New York, USA, pp 409 - 413, Series - Proceeding-Article Year of Publication, 1999 https://doi.org/10.1145/312129.312303
  17. Soheila, E., The Enhancement of Credit Card Fraud Detection Systems Using Machine Learning Methodology, University Of Toronto (Canada), Mai, 38/06, p. 1640, Dec 2000
  18. Lisa, M. Sas, K., No. 82 Aid Auditors in Financial Statement Fraud Detection?, University Of Colorado Boulder, DAI-A 58/07, p. 2732, Jan 1998
  19. Charles, C. Steve, S., A Critical Examination of Reengineered Audit Processes and The Likelihood of Detecting Fraud, No.3, pp. 297-310, 2002
  20. Rosset, S. Murad, U. Neumann, E. Idan, Y. and Pinkas, G., Discovery of Fraud Rules for Telecommunications-Challenges and Solutions, ACM Press, New York, USA, pp. 409 - 413, Series- Proceeding-Article, Year of Publication, 1999 https://doi.org/10.1145/312129.312303