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.
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Agarwal, R., Caesar, M., Godfrey, B., Zhao, B.Y.: Shortest paths in microseconds. CoRR, abs/1309.0874 (2013)
Akoglu, L., McGlohon, M., Faloutsos, C.: Anomaly Detection in Large Graphs. Technical Report CMU-CS-09-173, Carnegie Mellon University, November 2009
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)
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)
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)
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 Conference
Brown, A., Divitt, D., Rolfe, A.: Card fraud report 2015. Technical report, Alaric, March 2015
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
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)
FICO: FICO Falcon Fraud Manager for Debit and Credit Card. Technical report, FICO (2012)
fiserv: fiserv: Compliance & fraud management (2015). https://www.fiserv.com/risk-compliance/financial-crime-risk-management.aspx
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)
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 2012
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)
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)
Klimt, B., Yang, Y.: The enron corpus. In: ECML, pp. 217–226 (2004)
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 2013
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)
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)
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)
Nakamoto, S.: Bitcoin: a peer-to-peer electronic cash system (2009). https://bitcoin.org/bitcoin.pdf
NICE: Nice actimize: Fraud detection & prevention (2015). http://www.niceactimize.com/fraud-detection-and-prevention
RSA: RSA Discovers Massive Boleto Fraud Ring in Brazil. Technical report, EMC, July 2014
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
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)
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)
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)
van Dongen, S.: Graph Clustering by Flow Simulation. Ph.D. thesis, University of Utrecht (2000)
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)
Zhang, T., Ramakrishnan, R., Livny, M.: BIRCH: an efficient data clustering method for very large databases. ACM SIGMOD 25(2), 103–111 (1996)
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Molloy, I. et al. (2017). Graph Analytics for Real-Time Scoring of Cross-Channel Transactional Fraud. In: Grossklags, J., Preneel, B. (eds) Financial Cryptography and Data Security. FC 2016. Lecture Notes in Computer Science(), vol 9603. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-54970-4_2
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DOI: https://doi.org/10.1007/978-3-662-54970-4_2
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