Advertisement

Graph Analytics for Real-Time Scoring of Cross-Channel Transactional Fraud

  • Ian MolloyEmail author
  • Suresh Chari
  • Ulrich Finkler
  • Mark Wiggerman
  • Coen Jonker
  • Ted Habeck
  • Youngja Park
  • Frank Jordens
  • Ron van Schaik
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9603)

Abstract

We present a new approach to cross channel fraud detection: build graphs representing transactions from all channels and use analytics on features extracted from these graphs. Our underlying hypothesis is community based fraud detection: an account (holder) performs normal or trusted transactions within a community that is “local” to the account. We explore several notions of community based on graph properties. Our results show that properties such as shortest distance between transaction endpoints, whether they are in the same strongly connected component, whether the destination has high page rank, etc., provide excellent discriminators of fraudulent and normal transactions whereas traditional social network analysis yields poor results. Evaluation on a large dataset from a European bank shows that such methods can substantially reduce false positives in traditional fraud scoring. We show that classifiers built purely out of graph properties are very promising, with high AUC, and can complement existing fraud detection approaches.

Keywords

Short Path Social Network Analysis Trust Relationship Graph Feature Fraud Detection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Agarwal, R., Caesar, M., Godfrey, B., Zhao, B.Y.: Shortest paths in microseconds. CoRR, abs/1309.0874 (2013)Google Scholar
  2. 2.
    Akoglu, L., McGlohon, M., Faloutsos, C.: Anomaly Detection in Large Graphs. Technical Report CMU-CS-09-173, Carnegie Mellon University, November 2009Google Scholar
  3. 3.
    Aleskerov, E., Freisleben, B., Rao, B.: CARDWATCH: a neural network based database mining system for credit card fraud detection. In: IEEE/IAFE 1997 Computational Intelligence for Financial Engineering (CIFEr) (1997)Google Scholar
  4. 4.
    Bond, M., Choudary, O., Murdoch, S.J., Skorobogatov, S., Anderson, R., Chip, S.: Cloning EMV cards with the pre-play attack. In: 2014 IEEE Symposium on Security and Privacy (SP), pp. 49–64 (2014)Google Scholar
  5. 5.
    Brause, R., Langsdorf, T., Hepp, M.: Neural data mining for credit card fraud detection. In: 11th International Conference on Tools with Artificial Intelligence, TAI 1999, pp. 103–106 (1999)Google Scholar
  6. 6.
    Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. Comput. Netw. ISDN Syst. 30(1–7), 107–117 (1998). Proceedings of the Seventh International World Wide Web ConferenceCrossRefGoogle Scholar
  7. 7.
    Brown, A., Divitt, D., Rolfe, A.: Card fraud report 2015. Technical report, Alaric, March 2015Google Scholar
  8. 8.
    Duman, E., Elikucuk, I.: Solving credit card fraud detection problem by the new metaheuristics migrating birds optimization. In: Rojas, I., Joya, G., Cabestany, J. (eds.) IWANN 2013. LNCS, vol. 7903, pp. 62–71. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-38682-4_8 CrossRefGoogle Scholar
  9. 9.
    Fader, P.S., Hardie, B., Lee, K.L.: RFM and CLV: using iso-value curves for customer base analysis. J. Mark. Res. 42(4), 415–430 (2005)CrossRefGoogle Scholar
  10. 10.
    FICO: FICO Falcon Fraud Manager for Debit and Credit Card. Technical report, FICO (2012)Google Scholar
  11. 11.
    fiserv: fiserv: Compliance & fraud management (2015). https://www.fiserv.com/risk-compliance/financial-crime-risk-management.aspx
  12. 12.
    Gong, N.Z., Frank, M., Mittal, P.: SybilBelief: a semi-supervised learning approach for structure-based sybil detection. IEEE Trans. Inf. Forensics Secur. 9, 976–987 (2014)CrossRefGoogle Scholar
  13. 13.
    Gong, N.Z., Xu, W., Huang, L., Mittal, P., Stefanov, E., Sekar, V., Song, D.: Evolution of social-attribute networks: measurements, modeling, and implications using Google+. In: The 2012 ACM Conference, pp. 131–144. ACM, New York, November 2012Google Scholar
  14. 14.
    Grier, C., Thomas, K., Paxson, V., Zhang, M.: @spam: the Underground on 140 Characters or Less. In: Proceedings of the 17th ACM Conference on Computer and Communications Security, pp. 27–37 (2010)Google Scholar
  15. 15.
    Gubichev, A., Bedathur, S., Seufert, S., Weikum, G.: Fast and accurate estimation of shortest paths in large graphs. In: CIKM 2010: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 499–508 (2010)Google Scholar
  16. 16.
    Klimt, B., Yang, Y.: The enron corpus. In: ECML, pp. 217–226 (2004)Google Scholar
  17. 17.
    Chandy, R., Faloutsos, C., Akoglu, L.: Opinion fraud detection in online reviews by network effects. In: International AAAI Conference on Weblogs and Social Media, pp. 1–10, April 2013Google Scholar
  18. 18.
    Maes, S., Tuyls, K., Vanschoenwinkel, B.: Credit card fraud detection using Bayesian and neural networks. In: Proceedings of the 1st International NAISO Congress on Neuro Fuzzy Technologies (2002)Google Scholar
  19. 19.
    Mislove, A., Marcon, M., Gummadi, P.K., Druschel, P., Bhattacharjee, B.: Measurement and analysis of online social networks. In: Internet Measurement Comference, pp. 29–42 (2007)Google Scholar
  20. 20.
    Murdoch, S.J., Drimer, S., Anderson, R., Bond, M.: Chip and PIN is broken. In: 2010 IEEE Symposium on Security and Privacy, pp. 433–446. IEEE (2010)Google Scholar
  21. 21.
    Nakamoto, S.: Bitcoin: a peer-to-peer electronic cash system (2009). https://bitcoin.org/bitcoin.pdf
  22. 22.
    NICE: Nice actimize: Fraud detection & prevention (2015). http://www.niceactimize.com/fraud-detection-and-prevention
  23. 23.
    RSA: RSA Discovers Massive Boleto Fraud Ring in Brazil. Technical report, EMC, July 2014Google Scholar
  24. 24.
    Saini, S., Chang, J., Jin, H.: Performance evaluation of the intel sandy bridge based NASA pleiades using scientific and engineering applications. In: Jarvis, S.A., Wright, S.A., Hammond, S.D. (eds.) PMBS 2013. LNCS, vol. 8551, pp. 25–51. Springer, Cham (2014). doi: 10.1007/978-3-319-10214-6_2 Google Scholar
  25. 25.
    Sánchez, D., Vila, M.A., Cerda, L., Serrano, J.M.: Association rules applied to credit card fraud detection. Expert Syst. Appl. Int. J. 36(2), 3630–3640 (2009)CrossRefGoogle Scholar
  26. 26.
    Shen, A., Tong, R., Deng, Y.: Application of classification models on credit card fraud detection. In: 2007 International Conference on Service Systems and Service Management, pp. 1–4. IEEE (2007)Google Scholar
  27. 27.
    Syeda, M., Zhang, Y.-Q., Pan, Y.: Parallel granular neural networks for fast credit card fraud detection. In: 2002 IEEE World Congress on Computational Intelligence, 2002 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2002, pp. 572–577. IEEE (2002)Google Scholar
  28. 28.
    van Dongen, S.: Graph Clustering by Flow Simulation. Ph.D. thesis, University of Utrecht (2000)Google Scholar
  29. 29.
    Van Vlasselaer, V., Bravo, C., Caelen, O., Eliassi-Rad, T., Akoglu, L., Snoeck, M., Baesens, B.: APATE: a novel approach for automated credit card transaction fraud detection using network-based extensions. Decis. Support Syst. 75, 38–48 (2015)CrossRefGoogle Scholar
  30. 30.
    Zhang, T., Ramakrishnan, R., Livny, M.: BIRCH: an efficient data clustering method for very large databases. ACM SIGMOD 25(2), 103–111 (1996)CrossRefGoogle Scholar

Copyright information

© International Financial Cryptography Association 2017

Authors and Affiliations

  • Ian Molloy
    • 1
    Email author
  • Suresh Chari
    • 1
  • Ulrich Finkler
    • 1
  • Mark Wiggerman
    • 2
  • Coen Jonker
    • 2
  • Ted Habeck
    • 1
  • Youngja Park
    • 1
  • Frank Jordens
    • 2
  • Ron van Schaik
    • 2
  1. 1.IBM Thomas J. Watson Research CenterYorktown HeightsUSA
  2. 2.ABN AMRO Bank N.V.AmsterdamThe Netherlands

Personalised recommendations