A survey of signature based methods for financial fraud detection

Computers and Security

Volumn 28, Issue 6, 2009, Pages 381-394

  Edge, Michael Edward ^a   Falcone Sampaio, Pedro R ^a  

^a UNIVERSITY OF MANCHESTER   (United Kingdom)

Author keywords

Account signatures; Behaviour scoring; Fraud detection; Fraud management; Identity theft; User profiling

Indexed keywords

ACCOUNT SIGNATURES; BEHAVIOUR SCORING; FRAUD DETECTION; FRAUD MANAGEMENT; IDENTITY THEFT; USER PROFILING;

ALARM SYSTEMS; DATA PROCESSING; EDUCATION; LEARNING ALGORITHMS; SURVEYS;

FINANCIAL DATA PROCESSING;

EID: 67649739099     PISSN: 01674048     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cose.2009.02.001     Document Type: Article

References (50)

1. Actimize Inc (2008). www.actimize.com/
2. Aleskerov E, Freisleben B, Rao B. CARDWATCH: a neural network based database mining system for credit card fraud detection. In: Proceedings of the IEEE/IAFE; 1997. p. 220-226.
3. APACS - the UK payments association (2006). www.apacs.org.uk
4. Bae J., Bae H., Kang S.-H., and Kim Y. Automatic control of workflow processes using ECA rules. IEEE Transactions on Knowledge and Data Engineering 16 (2004) 1010-1023
5. (2005). http://news.bbc.co.uk/1/hi/england/kent/4332504.stm BBC News
6. (2006). http://news.bbc.co.uk/1/hi/england/essex/4714164.stm BBC News
7. Bicakci K., and Baykal N. One-time passwords: security analysis using BAN logic and integrating with smartcard authentication. ISCIS vol. 2869 (2003) 794-801
8. Boukerche Azzedine, Notare MSMA. Neural fraud detection in mobile phone operations. In: IPDPS Workshops, vol. 1800; 2000. p. 636-644.
9. BrauseR, Langsdorf T, Hepp M. Neural data mining for credit card fraud detection. In: ICTAI; 1999. p. 103-106.
10. Cahill M, Lambert D, Pinheiro J, Sun D. Detecting fraud in the real world. Technical report, Bell Labs, Lucent Technologies; 2000.
11. The chip and PIN consumer guide (2006). www.cardwatch.co.uk
12. Coral 8 Inc (2008). www.coral8.com/
13. Cortes C., and Pregibon D. Signature-based methods for data streams. Data Mining and Knowledge Discovery 5 (2001) 167-182
14. Cortes C., Fisher K., Pregibon D., and Rogers A. Hancock: a language for extracting signatures from data streams. Knowledge Discovery and Data Mining (2000) 9-17
15. Damianou N., Dulay N., Lupu E., and Sloman M. The ponder policy specification language. Proceedings of the international workshop on policies for distributed systems and networks (2001), Springer-Verlag 18-38
16. Danciu VA, Kempter B. From processes to policies - concepts for large scale policy generation. In: Network operations and management symposium, 2004. NOMS 2004. IEEE/IFIP, vol. 1; 2004. p. 17-30.
17. Dekhil M, Machiraju V, Wurster K, Griss M. Generalized policy model for application and service management. In: Policy Workshop, Bristol, UK; 1999.
18. Edge ME, Sampaio PRF. Expressing signature based policies for proactive fraud management in FFML, submitted for publication.
19. Edge M.E., Sampaio P.R.F., and Choudhary M. Towards a proactive fraud management framework for financial data streams. The 3rd IEEE international symposium on dependable, autonomic and secure computing (DASC'07) (2007), Loyola College Graduate Center, Columbia, MD, USA 55-62
20. Edge ME, Sampaio PRF, Philpott O, Choudhary M. A policy distribution service for proactive fraud management over financial data streams. In: IEEE international conference on services computing, 2008 (SCC'08), Honolulu, Hawaii, USA; 2008. p. 31-38.
21. Entrust (2008). www.entrust.com
22. The evolving threat of card skimming. Fair Isaac (2005) White Paper
23. Falcon Fraud Manager - Fair Isaac Corporation (2008). www.fairisaac.com
24. Fawcett T., and Provost F. Adaptisve fraud detection. Data Mining and Knowledge Discovery (1997) 291-316
25. Ferreira P, Alves R, Belo O, Cortesao L. Establishing fraud detection patterns based on signatures. In: Industrial conference on data mining, Leipzig, Germany; 2006. p. 526-538.
26. In: Ghosh S., and Reilly D.L. (Eds). Credit card fraud detection with a neural-network vol. 3 (1994), IEEE Computer Society Press 621-630
27. Hilas CS, Sahalos JN. Testing the fraud detection ability of different user profiles by means of FF-NN classifiers. In: ICANN, Athens; 2006. pp. 872-883.
28. Kim K.-j., and Ahn H. Using a clustering genetic algorithm to support customer segmentation for personalized recommender systems. Artificial Intelligence and Simulation (2005) 409-415
29. Kim G, Kim J, Na J. Design and implementation of policy decision point in policy-based network. In: ISCIS; 2005. p. 534.
30. Kim S.-Y., Jung T.-S., Suh E.-H., and Hwang H.-S. Customer segmentation and strategy development based on customer lifetime value: a case study. Expert Systems with Applications 31 (2006) 101-107
31. Kou Y, Lu C-T, Sirwongwattana S, Huang Y-P. Survey of fraud detection techniques. In: IEEE International Conference on Networking, Sensing and Control, vol. 2; 2004. p. 749-754.
32. Lobo J, Bhatia R, Naqvi SA. A policy description language. In: AAAI; 1999. p. 291-298.
33. Luckham D. The RAPIDE pattern language. The power of events: an introduction to complex event processing in distributed enterprise systems. 3rd ed. (2005), Pearson Education 145-174
34. Lunt T.F. A survey of intrusion detection techniques. Computer Security 12 (1993) 405-418
35. Maes S., Tuyls K., Vanschoenwinkel B., and Manderick B. Credit card fraud detection using bayesian and neural networks (1996)
36. Massey K. Combating eFraud - a next generation approach. Financial Insights (2005) White Paper
37. Paton N.W., and Diaz O. Active database systems. ACM Computing Surveys 31 (1999) 63-103
38. Phua C, Lee V, Smith K, Gayler R. A comprehensive survey of data mining-based fraud detection research; 2005.
39. Ramshaw L, Sahai A, Saxe J, Singhal S. Cauldron: a policy-based design tool. In: Policies for Distributed Systems and Networks, 2006. Policy 2006. Seventh IEEE International Workshop on; 2006.
40. RSA eFraud Network (2008). www.rsa.com
41. Protecting against phishing by implementing strong two-factor authentication. RSA Security (2004) White Paper
42. Ruiz J.P., Chebat J.C., and Hansen P. Another trip to the mall: a segmentation study of customers based on their activities. Journal of Retailing and Consumer Services (2004)
43. Rygielski C., Wang J.-C., and Yen D.C. Data mining techniques for customer relationship management. Technology in Society 24 (2002) 483-502
44. Shaw M.J., Subramaniam C., Tan G.W., and Welge M.E. Knowledge management and data mining for marketing. Decision Support Systems 31 (2001) 127-137
45. StreamBase (2008). www.streambase.co.uk
46. www.cl.cam.ac.uk/research/security/projects/banking/ University of Cambridge, 2007.
47. Viaene S., Dedene G., and Derrig R.A. Auto claim fraud detection using Bayesian learning neural networks. Expert Systems with Applications 29 (2005) 653-666
48. Widom J., and Ceri S. Active database systems: triggers and rules for advanced data processing (1996), Morgan Kaufmann
49. Wu J., and Lin Z. Research on customer segmentation model by clustering. Proceedings of the 7th international conference on electronic commerce (2005), ACM Press, Xi'an, China 316-318
50. Zakrzewska D, Murlewski J. Clustering algorithms for bank customer segmentation. In: Intelligent Systems Design and Applications, 2005. ISDA '05. Proceedings. 5th International Conference; 2005. p. 197-202.