Data Mining Techniques in Fraud Detection

Authors

Rekha Bhowmik, University of Texas at DallasFollow

Prior Publisher

The Association of Digital Forensics, Security and Law (ADFSL)

Abstract

The paper presents application of data mining techniques to fraud analysis. We present some classification and prediction data mining techniques which we consider important to handle fraud detection. There exist a number of data mining algorithms and we present statistics-based algorithm, decision treebased algorithm and rule-based algorithm. We present Bayesian classification model to detect fraud in automobile insurance. Naïve Bayesian visualization is selected to analyze and interpret the classifier predictions. We illustrate how ROC curves can be deployed for model assessment in order to provide a more intuitive analysis of the models.

References

Barse, E., Kvarnstrom, H. & Jonsson, E. (2003). Synthesizing Test Data for Fraud Detection Systems. Proc. of the 19th Annual Computer Security Applications Conference, 384-395.

Bell, T. & Carcello, J. (2000). A Decision Aid for Assessing the Likelihood of Fraudulent Financial Reporting. Auditing: A Journal of Practice and Theory 10(1): 271-309.

Belhadji, E., Dionne, G. & Tarkhani, F. (2000). A Model for the Detection of Insurance Fraud. The Geneva Papers on Risk and Insurance 25(4): 517-538.

Bentley, P. (2000). Evolutionary, my dear Watson: Investigating Committeebased Evolution of Fuzzy Rules for the Detection of Suspicious Insurance Claims. Proc. of GECCO2000.

Bentley, P., Kim, J., Jung., G. & Choi, J. (2000). Fuzzy Darwinian Detection of Credit Card Fraud. Proc. of 14th Annual Fall Symposium of the Korean Information Processing Society.

Bhargava, B., Zhong, Y., & Lu, Y. (2003). Fraud Formalization and Detection. Proc. of DaWaK2003, 330-339.

Bolton, R. & Hand, D. (2002). Statistical Fraud Detection: A Review (With Discussion). Statistical Science 17(3): 235-255.

Bolton, R. & Hand, D. (2001). Unsupervised Profiling Methods for Fraud Detection. Credit Scoring and Credit Control VII.

Bonchi, F., Giannotti, F., Mainetto, G., Pedreschi, D. (1999). A Classificationbased Methodology for Planning Auditing Strategies in Fraud Detection. Proc. of SIGKDD99, 175-184.

Brause, R., Langsdorf, T. & Hepp, M. (1999). Neural Data Mining for Credit Card Fraud Detection. Proc. of 11th IEEE International Conference on Tools with Artificial Intelligence.

Brockett, P., Derrig, R., Golden, L., Levine, A. & Alpert, M. (2002). Fraud Classification using Principal Component Analysis of RIDITs. Journal of Risk and Insurance 69(3): 341-371.

Burge, P. & Shawe-Taylor, J. (2001). An Unsupervised Neural Network Approach to Profiling the Behavior of Mobile Phone Users for Use in Fraud Detection. Journal of Parallel and Distributed Computing 61: 915-925.

Cahill, M., Chen, F., Lambert, D., Pinheiro, J. & Sun, D. (2002). Detecting Fraud in the Real World. Handbook of Massive Datasets 911-930.

Chan, P., Fan, W., Prodromidis, A. & Stolfo, S. (1999). Distributed Data Mining in Credit Card Fraud Detection. IEEE Intelligent Systems 14: 67-74.

Chen, R., Chiu, M., Huang, Y. & Chen, L. (2004). Detecting Credit Card Fraud by Using Questionnaire-Responded Transaction Model Based on Support Vector Machines. Proc. of IDEAL2004, 800-806.

Cortes, C., Pregibon, D. & Volinsky, C. (2003). Computational Methods for Dynamic Graphs. Journal of Computational and Graphical Statistics 12: 950- 970.

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, 111-134. John Wiley.

Elkan, C. (2001). Magical Thinking in Data Mining: Lessons from CoIL Challenge 2000. Proc. of SIGKDD01, 426-431.

Ezawa, K. & Norton, S. (1996). Constructing Bayesian Networks to Predict Uncollectible Telecommunications Accounts. IEEE Expert October: 45-51.

Fan, W. (2004). Systematic Data Selection to Mine Concept- Drifting Data Streams. Proc. of SIGKDD04, 128-137. Fawcett, T. (2004). ROC graphs: Notes and practical considerations for researchers. Machine Learning, 3.

Fawcett, T., & Flach, P. A. (2005). A response to web and Ting’s on the application of ROC analysis to predict classification performance under varying class distributions. Machine Learning, 58(1): 33-38.

Flach, P. (2004). Tutorial at ICML 2004: The many faces of ROC analysis in machine learning. Unpublished manuscript.

Flach, P., Blockeel, H., Ferri, C., Hernandez-Orallo, J., & Struyf, J. (2003). Decision support for data mining: Introduction to ROC analysis and its applications. Data mining and decision support: Aspects of integration and collaboration, 81-90.

Flach, P. A. (2003). The geometry of ROC space: Understanding machine learning metrics through ROC isometrics. Proceedings of the Twentieth International Conference on Machine Learning, 194–201.

Foster, D. & Stine, R. (2004). Variable Selection in Data Mining: Building a Predictive Model for Bankruptcy. Journal of American Statistical Association 99: 303-313.

He H, Wang J, Graco W and Hawkins S.(1997). Application of Neural Networks to Detection of Medical Fraud. Expert Systems with Applications, 13, 329-336.

James F.(2002). FBI has eye on business databases. Chicago Tribune, Knight Ridder/ Tribune Business News.

Kim, H., Pang, S., Je, H., Kim, D. & Bang, S. (2003). Constructing Support Vector Machine Ensemble. Pattern Recognition 36: 2757-2767.

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.

Lin, J., Hwang, M. & Becker, J. (2003). A Fuzzy Neural Network for Assessing the Risk of Fraudulent Financial Reporting. Managerial Auditing Journal 18(8): 657-665.

Little, B., Johnston, W., Lovell, A., Rejesus, R. & Steed, S. (2002). Collusion in the US Crop Insurance Program: Applied Data Mining. Proc. of SIGKDD02, 594-598.

Maes, S., Tuyls, K., Vanschoenwinkel, B. & Manderick, B. (2002). Credit Card Fraud Detection using Bayesian and Neural Networks. Proc. of the 1st International NAISO Congress on Neuro Fuzzy Technologies.

Magnify(2002). FraudFocus Advanced Fraud Detection, White Paper, Chicago.

Magnify(2002). The Evolution of insurance Fraud Detection: Lessons learnt from other industries, White Paper, Chicago.

Major, J. & Riedinger, D. (2002). EFD: A Hybrid Knowledge/Statistical-based system for the Detection of Fraud. Journal of Risk and Insurance 69(3): 309- 324.

Meena J(2003). Data mining for Homeland Security. Executive Briefing, VA.

Meena J(2003). Investigative Data Mining for Security and Criminal Detection, Butterworth Heinemann, MA.

McGibney, J. & Hearne, S. (2003). An Approach to Rules-based Fraud Management in Emerging Converged Networks. Proc. Of IEI/IEEE ITSRS 2003.

Moreau, Y. & Vandewalle, J. (1997). Detection of Mobile Phone Fraud Using Supervised Neural Networks: A First Prototype. Proc. of 1997 International Conference on Artificial Neural Networks, 1065-1070.

Ormerod T., Morley N., Ball L., Langley C., and Spenser C. (2003). ‘Using Ethnography To Design a Mass Detection Tool (MDT) For The Early Discovery of Insurance Fraud’, Computer Human Interaction, April 5-10, Ft. Lauderdale, Florida.

Phua, C., Alahakoon, D. & Lee, V. (2004). Minority Report in Fraud Detection: Classification of Skewed Data, SIGKDD Explorations 6(1): 50-59.

Provost, F., Fawcett, T., & Kohavi, R. (1998). The case against accuracy estimation for comparing induction algorithms. Proceedings of the Fifteenth InternationalConference on Machine Learning, , 445–453.

Rosset, S., Murad, U., Neumann, E., Idan, Y. & Pinkas, G. (1999). Discovery of Fraud Rules for Telecommunications - Challenges and Solutions. Proc. of SIGKDD99, 409-413.

SAS e-Intelligence(2000). Data Mining in the Insurance industry: Solving Business problems using SAS Enterprise Miner Software, White Paper.

Shao, H., Zhao, H. & Chang, G. (2002). Applying Data Mining to Detect Fraud Behavior in Customs Declaration. Proc. of 1st International Conference on Machine Learning and Cybernetics, 1241-1244.

Sherman, E. (2002). Fighting Web Fraud. Newsweek, June 10.

SPSS(2003). Data mining and Crime analysis in the Richmond Police Department, White Paper, Virginia.

Stefano, B. & Gisella, F. (2001). Insurance Fraud Evaluation: A Fuzzy Expert System. Proc. of IEEE International Fuzzy Systems Conference, 1491-1494.

Syeda, M., Zhang, Y. & Pan, Y. (2002). Parallel Granular Neural Networks for Fast Credit Card Fraud Detection. Proc. of the 2002 IEEE International Conference on Fuzzy Systems.

Swets, J. A., Dawes, R. M., & Monahan, J. (2000). Better decisions through science. Scientific American; Scientific American, 283(4), 82-87.

Von Altrock, C. (1997). Fuzzy Logic and Neurofuzzy Applications in Business and Finance. 286-294. Prentice Hall.

Viaene, S., Derrig, R. & Dedene, G. (2004). A Case Study of Applying Boosting Naive Bayes to Claim Fraud Diagnosis. IEEE Transactions on Knowledge and Data Engineering 16(5): 612-620

Yamanishi, K., Takeuchi, J., Williams, G. & Milne, P. (2004). On-Line Unsupervised Outlier Detection Using Finite Mixtures with Discounting Learning Algorithms. Data Mining and Knowledge Discovery 8: 275-300.

Weatherford, M.(2002). Mining for Fraud. IEEE Intelligent Systems, July/ August, 4-6.

Wheeler, R. & Aitken, S. (2000). Multiple Algorithms for Fraud Detection. Knowledge-Based Systems 13(3): 93-99.

Williams, G. J. and Huang, Z.(1997). ‘Mining the Knowledge: Mine the Hot Spots Methodology for Mining Large Real World Databases’, 10th Australian Joint Conference on Artificial Intelligence, Published in Lecture Notes in Artificial Intelligence, Springer-Verlag, December, Perth, Western Australia.

Williams, G.(1999). ‘Evolutionary Hot Spots Data Mining: An Architecture for Exploring for Interesting Discoveries’, Proceedings of the 3rd Pacific-Asia Conference in Knowledge Discovery and Data Mining, Beijing, China.

Recommended Citation

Bhowmik, Rekha (2008) "Data Mining Techniques in Fraud Detection," Journal of Digital Forensics, Security and Law: Vol. 3 , Article 3.
DOI: https://doi.org/10.15394/jdfsl.2008.1040
Available at: https://commons.erau.edu/jdfsl/vol3/iss2/3