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Reseach Article

A Survey on Financial Fraud Detection Methodologies

by Pankaj Richhariya, Prashant K Singh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 45 - Number 22
Year of Publication: 2012
Authors: Pankaj Richhariya, Prashant K Singh

Pankaj Richhariya, Prashant K Singh . A Survey on Financial Fraud Detection Methodologies. International Journal of Computer Applications. 45, 22 ( May 2012), 15-22. DOI=10.5120/7080-9373

@article{ 10.5120/7080-9373,
author = { Pankaj Richhariya, Prashant K Singh },
title = { A Survey on Financial Fraud Detection Methodologies },
journal = { International Journal of Computer Applications },
issue_date = { May 2012 },
volume = { 45 },
number = { 22 },
month = { May },
year = { 2012 },
issn = { 0975-8887 },
pages = { 15-22 },
numpages = {9},
url = { },
doi = { 10.5120/7080-9373 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T20:38:14.942334+05:30
%A Pankaj Richhariya
%A Prashant K Singh
%T A Survey on Financial Fraud Detection Methodologies
%J International Journal of Computer Applications
%@ 0975-8887
%V 45
%N 22
%P 15-22
%D 2012
%I Foundation of Computer Science (FCS), NY, USA

Owing to levitate and rapid escalation of E-Commerce, cases of financial fraud allied with it are also intensifying and which results in trouncing of billions of dollars worldwide each year. Fraud detection involves scrutinizing the behavior of populations of users in order to ballpark figure, detect, or steer clear of objectionable behavior: Undesirable behavior is a extensive term including delinquency: swindle, infringement, and account evasion. Factually, swindle transactions are speckled with genuine transactions and simple pattern matching techniques are not often sufficient to detect those frauds accurately. In this survey we, will focuses on classifying fraudulent behaviors, identifying the major sources and characteristics of the data based on which fraud detection has been conducted. This paper provide a comprehensive survey and review of different techniques to detect the financial fraud detection used in various fraud like credit card fraud detection, online auction fraud, telecommunication fraud detection, and computer intrusion detection.

  1. Investigating Fraudulent Acts, 2000 UNIVERSITY OF HOUSTON SYSTEM ADMINISTRATIVE MEMORANDUM, http://www. uhsa. uh. edu/sam/AM/01C04. htm
  2. Bologna, Jack & Robert J. Lindquist, 1987. Fraud Auditing & Forensic Accounting, New York: John Wiley & Sons.
  3. Prabin K Panigrahi,2011. "A Framework for Discovering Internal Financial Fraud Using Analytics" in Communication Systems and Network Technologies (CSNT), IEEE 2011 International Conference , pp. 323 - 327
  4. Stream Base, 2008 Entrust www. entrust. com
  5. M. S. Beasley,1996. "An empirical analysis of the relation between the board of director composition and financial statement fraud," The Accounting Review, vol. 71, no. 4, pp. 443-465.
  6. J. V. Hansen, J. B. McDonald, and W. F. Messier, 1997. "A generalized qualitative-response model and the analysis of management fraud", Management Science, vol. 42, pp. 1022-1032.
  7. M. M. Eining, DS. R. Jones, and J. K. Loebbecke, 1997. "Reliance on decision aids: an examination of auditors' assessment of management fraud," Auditing: A Journal of Practice and Theory, vol. 16, pp. 1-19.
  8. B. P. Green, and J. H. Choi, 1997. "Assessing the risk of management fraud through neural network technology," Auditing, vol. 16, pp. 14-28.
  9. K. Fanning and K. Cogger, 1998. "Neural network detection of management fraud using published financial data," International Journal of Intelligent Systems in Accounting, Finance & Management, vol. 7, no. 1, pp. 21-24.
  10. M. D. Beneish, 1999. "Incentives and penalities related to earnings overstatements that violate GAAP," Accounting Review, vol. 4, pp. 425-457.
  11. L. J. Abbott, S. Parker, and G. F. Peters, 2001. "Audit committee characteristics and financial misstatement: A study of the efficacy of certain blue ribbon committee recommendation," Proceedings of the Auditing Section of the AAA Meeting.
  12. K. Fanning, K. , Cogger, and R. Srivastava, 1995. "Detection of management fraud: a neural network approach", International Journal of Intelligent Systems in Accounting, Finance & Management, vol. 4(2), pp. 113-126.
  13. Agyemang, M. , Barker, K. , & Alhajj, 2006. "A comprehensive survey of numeric and symbolic outlier mining techniques", Intelligent Data Analysis, vol. 10, pp. 521-538.
  14. Kou, Y. , Lu, C. , & Sirwongwattana, 2004. "Survey of Fraud Detection Techniques", In International Conference on Networking, Sensing, and Control, pp. 749-754.
  15. Massey, K. Massey, 2005. "Combating eFraud – a next generation approach", Financial Insights White Paper.
  16. Fair Isaac, 2005 "The evolving threat of card skimming", Fair Isaac White Paper.
  17. Ghosh, S. & Reilly, 2004. "Credit Card Fraud Detection with a Neural Network" In Proc. of 27th Hawaii International Conference on Systems Science vol. 3, pp. 621-630.
  18. Barse, E. , Kvarnstrom, H. & Jonson, 2003. "Synthesizing Test Data for Fraud Detection Systems" In Proc. of the 19th Annual Computer Security Applications Conference, pp. 384-395.
  19. Syeda, M. , Zhang, Y. & Pan, 2002. "Parallel Granular Neural Networks for Fast Credit Card Fraud Detection" In Proc. of the 2002 IEEE International Conference on Fuzzy Systems. 2002. Newsweek.
  20. Maes, S. , Tuyls, K. , Vanschoenwinkel, B. & Manderick, 2002. "Credit Card Fraud Detection using Bayesian and Neural Networks" In Proc. of the 1st International NAISO Congress on Neuro Fuzzy Technologies.
  21. Chiu, C. & Tsai, 2004. "A Web Services-Based Collaborative Scheme for Credit Card Fraud Detection" In Proc. of 2004 IEEE International Conference on e-Technology, e-Commerce and e- Service.
  22. Kim, J. , Ong, A. & Overill, R, 2003. "Design of an Artificial Immune System as a Novel Anomaly Detector for Combating Financial Fraud in Retail Sector", Congress on Evolutionary Computation.
  23. Ezawa, K. & Norton, S. , 1996 "Constructing Bayesian Networks to Predict Uncollectible Telecommunications Accounts", IEEE Expert pp. 45-51.
  24. Bentley, P. , Kim, J. , Jung. , G. & Choi, J. , 2000. "Fuzzy Darwinian Detection of Credit Card Fraud. " In Proc. of 14th Annual Fall Symposium of the Korean Information Processing Society.
  25. Major, J. & Riedinger, D. , 2002. "EFD: A Hybrid Knowledge/ Statistical-based system for the Detection of Fraud. ", Journal of Risk and Insurance vol. 69 (3), pp. 309-324.
  26. Pathak, J. , Vidyarthi, N. & summers, S. , 2003. "A Fuzzy-base Algorithm for Auditors to Detect Element of Fraud in Settled Insurance Claims", Odette School of Business Administration.
  27. Stefano, B. & Gisella, F. , 2001. "Insurance Fraud Evaluation: A Fuzzy Expert System. " In Proc. Of IEEE International Fuzzy Systems Conference, pp. 1491-1494.
  28. Von Altrock, C. , 1997. "Fuzzy Logic and Neurofuzzy Applications in Business and Finance. " pp. 286-294. Prentice Hall.
  29. Deshmukh, A. & Talluru, T. , 1997. "A Rule Based Fuzzy Reasoning System for Assessing the Risk of Management Fraud. " Journal of Intelligent Systems in Accounting, Finance & Management vol. 7 (4), pp. 669-673.
  30. Perlich, C. & Provost F. , 2003. "Aggregation-based Feature Invention and Relational Concept Classes. " In Proc. of SIGKDD03, pp. 167-176.
  31. Chan, P. , Fan, W. , Prodromidis, A. & Stolfo, S. , 1999. "Distributed Data Mining in Credit Card Fraud Detection. " IEEE Intelligent Systems vol. 14, pp. 67-74.
  32. Stefano, B. & Gisella, F. , 2001. "Insurance Fraud Evaluation: A Fuzzy Expert System. " In Proc. of IEEE International Fuzzy Systems Conference, pp. 1491-1494.
  33. Phua, C. , Alahakoon, D. & Lee. , 2004. "Minority Report in Fraud Detection: Classification of Skewed Data", SIGKDD Explorations vol. 6(1), pp. 50-59.
  34. Cortes, C. & Pregibon, D. , 2001. "Signature-Based Methods for Data Streams. ", In Data Mining and Knowledge Discovery vol. 5, pp. 167- 182.
  35. Cahill, M. , Chen, F. , Lambert, D. , Pinheiro, J. & Sun, D. , 2002. "Detecting Fraud in the Real World", Handbook of Massive Datasets pp. 911-930.
  36. Moreau, Y. , Lerouge, E. , Verrelst, H. , Vandewalle, J. , Stormann, C. & Burge, P. , 1999. "BRUTUS: A Hybrid System for Fraud Detection in Mobile Communications. " In Proc. Of European Symposium on Artificial Neural Networks, pp. 447-454.
  37. Taniguchi, M. , Haft, M. , Hollmen, J. & Tresp, 1998. "Fraud Detection in Communication Networks using Neural and Probabilistic Methods. " In Proc. of 1998 IEEE International Conference in Acoustics, Speech and Signal Processing, pp. 1241- 1244.
  38. Williams, G. , 1999. "Evolutionary Hot Spots Data Mining: An Architecture for Exploring for Interesting Discoveries. " In Proc. of PAKDD99.
  39. Murad, U. & Pinkas, G. , 1999. "Unsupervised Profiling for Identifying Superimposed Fraud. " In Proc. of PKDD99.
  40. Cox, E. , 1995. "A Fuzzy System for Detecting Anomalous Behaviors in Healthcare Provider Claims. " In Goonatilake, S. & Treleaven, P. (eds. ) Intelligent Systems for Finance and Business, pp. 111-134. John Wiley and Sons Ltd.
  41. Kim, H. , Pang, S. , Je, H. , Kim, D. & Bang, S. , 2003. "Constructing Support Vector Machine Ensemble. " In Pattern Recognition vol. 36, pp. 2757-2767.
  42. Murad, U. & Pinkas, G. ,1999. "Unsupervised Profiling for Identifying Superimposed Fraud. ", In Proc. of PKDD99.
  43. Aleskerov, E. , Freisleben, B. & Rao, B. , 1997 "CARDWATCH: A Neural Network-Based Database Mining System for Credit Card Fraud Detection. " In Proc. of the IEEE/IAFE on Computational Intelligence for Financial Engineering, pp. 220-226.
  44. Kokkinaki, A. , 1997. "On Atypical Database Transactions: Identification of Probable Frauds using Machine Learning for User Profiling. " In Proc. of IEEE Knowledge and Data Engineering Exchange Workshop, pp. 107-113.
  45. Netmap. Fraud and Crime Example Brochure. 2004.
  46. Bolton, R. & Hand, D. , 2001. "Unsupervised Profiling Methods for Fraud Detection. ", Credit Scoring and Credit Control VII.
  47. Y. Moreau, B. Preneel, P. Burge, J. Shawe-Taylor, C. Stoermann, and C. Cooke. , 1997. "Novel techniques for fraud detection in mobile telecommunication networks. " In ACTS Mobile Summit, Grenada, Spain.
  48. B. Gavish and C. L. Tucci, 2008. "Reducing Internet Auction Fraud," Communications of the ACM (CACM), vol. 51 (5), pp. 89-97.
  49. S. Ba and P. Pavlou, 2002. "Evidence of the Effect of Trust Building Technology in Electronic Markets: Price Premiums and Buyer Behavior," MIS Quarterly, vol. 26 (3), pp. 243-268.
  50. T. D. Garvey and T. F. Lunt. , 1991. "Model based intrusion detection. " In Proceedings of the 14th National Computer Security Conference.
  51. A. K. Ghosh and A. Schwartzbard. , 1999. "A study in using neural networks for anomaly and misuse detection. " In Proceedings of the 8th USENIX Security Symposium, D. C.
  52. S. E. Smaha and J. Winslow. , 1994. "Misuse detection tools. " In Computer Security Journal vol. 10 (1), pp. 39 – 49, Spring.
Index Terms

Computer Science
Information Sciences


Fraud Detection Data Mining Neural Network