Abstract
Fraud detection is a rapidly developing field; several technologies have been used to prevent fraud such as data mining (DM). The use of data mining applications have shown their utility in different fields and have attracted increasing attention and popularity in the financial world. Data mining plays an important role in the field of fraud because it is often applied to extract and discover the truths hidden behind very large amounts of data. For this purpose, our contribution explores the applications of data mining techniques to fraud detection, and groups the various researches carried out in this field from 1966 to 2017. The result of this study will support and guide future research in this field.
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References
Bolton, R. J., Hand, D.J. Statistical fraud detection: a review. Stat. Sci. 17(3), 35–255 (2002)
Phua, C., Lee, V., Smith, K., Gayler, R.: A comprehensive survey of data mining-based fraud detection research. Artif. Intell. Rev. 2005, 1–14 (2005)
Yue, X., Wu, Y., Wang, Y. L., Chu, C.: A review of data mining-based financial fraud detection research. In: International Conference on Wireless Communications, Networking and Mobile Computing, pp. 5519–5522 (2007)
Fawcett, T., Provost, F.: Adaptive fraud detection. Data Min. Knowl. Disc. 1(3), 91–316 (1997)
Zhang, D., Zhou, L.: Discovering golden nuggets: data mining in financial application. IEEE Trans. Syst. Man Cybern. 34(4) (Nov 2004)
Ngai, E., Hu, Y., Wong, Y., Chen, Y., Sun, X.: The application of data mining techniques in financial fraud detection: a classification framework and an academic review of literature. Decis. Support Syst. 50(3), 559–569 (2011)
Spathis, C.T., Doumpos, M., Zopounidis, C.: Detecting falsified financial statements: a comparative study using multicriteria analysis and multivariate statistical techniques. Eur. Account. Rev. 11(3), 509–535 (2002)
Ravisankar, P., Ravi, V., Raghava Rao, G., Bose, I.: Detection of financial statement fraud and feature selection using data mining techniques (2011)
Oxford Concise English Dictionary, 10th edn. Publisher (1999)
Kirkos, E., Spathis, C., Manolopoulos, Y.: Data mining techniques for the detection of fraudulent financial statements. Expert Syst. Appl. 32(4), 995–1003 (2007)
Bashir, A., Khan, L., Awad, M.: Bayesian networks. In: Wang, J. (ed.) Encyclopedia of Data Warehousing and Mining: I-Z, pp. 89–92. Idea Group Inc., Hershey, PA (2006)
Lin, C.C., Huang, S.Y., Chiu, A.A.: Fraud detection using fraud triangle risk factors with analytic hierarchy process. In: 2012 Annual Meeting of the American Accounting Association (2012)
Goode, S., Lacey, D.: Detecting complex account fraud in the enterprise: the role of technical and non-technical controls. Decis. Support Syst. 50(4), 702–714 (2011)
Viaene, S., Dedene, G., Derrig, R.A.: Auto claim fraud detection using bayesian learning neural networks. Expert Syst. Appl. 29(3) [41], 653–666 (2005)
Fanning, K.M., Cogger, K.O.: Neural network detection of management fraud using published financial data. Int. J. Intell. Syst. Account. Financ. Manag. 7(1), 21–41 (1998)
Cerullo, M.J., Cerullo, V.: Using neural networks to predict financial reporting fraud. Comput. Fraud Secur., 14–17 (May/June 1999)
Green, P., Choi, J.H.: Assessing the risk of management fraud through neural network technology. Auditing: J. Pract. Theor. 16(1), 14–28 (1997)
Yuan, J., Yuan, C., Deng, X., Yuan, C.: The effects of manager compensation and market competition on financial fraud in public companies: an empirical study in China. Int. J. Manag. 25(2), 322–335 (2008)
Bell, T.B., Carcello, J.V.: A decision aid for assessing the likelihood of fraudulent financial reporting. Auditing: J. Pract. Theor. 19(1), 169–174 (2000)
Bermúdez, L., Pérez, J.M., Ayuso, M., Gómez, E., Vázquez, F.J.: A Bayesian dichotomous, model with asymmetric link for fraud in insurance. Insur.: Math. Econ. 42(2), 779–786 (2008)
Quah, J.T.S., Sriganesh, M.: Real-time credit card fraud detection using computational intelligence. Expert Syst. Appl. 35(4) (2008)
Kotsiantis, S., Koumanakos, E., Tzelepis, D., Tampakas, V.: Forecasting fraudulent financial statements using data mining. Int. J. Comput. Intell. 3(2), 104–110 (2006)
Caudill, S.B., Ayuso, M., Guillén, M.: Fraud detection using a multinominal logit model with missing information. J. Risk Insur. 72(4), 539–550 (2005)
Yeh, I.C., Lien, C.: The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients. Expert Syst. Appl. 36(2), 2473–2480 (2008)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco, CA, USA (2001)
Huang, M.J., Chen, M.Y., Lee, S.C.: Integrating data mining with casebased reasoning for chronic diseases prognosis and diagnosis. Expert Syst. Appl. 32(3), 856–867 (2007)
Ameur, F., Tkiouat, M.: Taxpayers Fraudulent behavior modeling the use of datamining in fiscal fraud detecting Moroccan case. Appl. Math. 3(10), 1207–1213 (2012). https://doi.org/10.4236/am.2012.310176
Gray, G.L., Debreceny, R.S. (2014)
Siciliano, R., Conversano, C.: Decision tree induction. In: Wang, J. (ed.) Encyclopedia of Data Warehousing and Mining: I-Z, pp. 353–358. Idea Group Inc., Hershey, PA (2006)
Yang, W., Hwang, S.: A process-mining framework for the detection of healthcare fraud and abuse. Expert Syst. Appl. 31(1), 56–68 (2006)
Lee, T.S., Chiu, C.C., Chou, Y.C., Lu, C.J.: Mining the customer credit using classification and regression tree and multivariate adaptive regression splines. Comput. Stat. Data Anal. 50(4), 1113–1130 (2006)
Smith, K.A.: Neural networks for prediction and classification. In: Wang J. (ed.) Encyclopedia of Data Warehousing and Mining: I–Z. Idea Group Inc., Hershey, PA, pp. 865–869 (2006)
Zhang, G.P.: Neural networks for classification: a survey. IEEE Trans. Syst. Man Cybern. 30(4), 451–462 (2000)
Steinwart, I., Christmann, A.: Support Vector Machines. Springer Science, New York, NY (2008)
Cox, E.: Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration. Morgan Kaufmann, San Francisco (2005)
Chen, W.-S., Du, Y.-K.: Using neural networks and data mining techniques for the financial distress prediction model (2009)
Koh, H.C., Low, C.K.: Going concern prediction using data mining techniques. Manag. Auditing J. 19(3), 462–476 (2004)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2006)
Berkhin, P.: A survey of clustering data mining techniques. In: Kogan, J., Nicholas, C., Teboulle, M. (eds.) Grouping Multidimensional Data: Recent Advances in Clustering, pp. 25–71. Springer, Heidelberg (2006)
Kohonen, T.: Self-Organizing Maps, 3rd edn. Springer, Heidelberg (2000)
Frawley, W.J., Paitetsjy-Shapiro, G., Matheus, C.J.: Knowledge discovery in databases: an overview. AI Mag. 13, 57–70 (1992)
Lu, C.L., Chen, T.C.: A study of applying data mining approach to the information disclosure for Taiwan’s stock market investors. Expert Syst. Appl. 36(2), 3536–3542 (2009)
Yue, D., Wu, X., Wang, Y., Li, Y., Chu, C.H.: A Review of Data Mining-Based Financial Fraud Detection Research, International Conference on Wireless Communications, Networking and Mobile Computing, pp. 5519–5522 (2007)
Chun, S.H., Park, Y.J.: A new hybrid data mining technique using a regression case based reasoning: application to financial forecasting. Expert Syst. Appl. 31(2), 329–336 (2006)
Kantardzic, M.: Data Mining: Concepts, Models, Methods, and Algorithms’. Wiley, IEEE Press (2002)
Zhou, W., Kapoor, G.: Detecting Evolutionary Financial Statement Fraud (2011)
Breiman, L., Friedman, J., Olshen, R., Stone, S.: Classification and Regression Trees. Chapman and Hall/CRC Press, Boca Raton, FL (1984)
Lin, C.-C., Chiu, A.-A., Huang, S.Y., Yen, D.C.: Detecting the financial statement fraud: the analysis of the differences between data mining techniques and experts’ judgments (2015)
Carneiroa, N., Figueiraa, G., Costac, M.: A data mining based system for credit-card fraud detection in e-tail (2017)
Whitrow, C., Hand, D.J., Juszczak, P., Weston, D., Adams, N.M.: Transaction aggregation as a strategy for credit card fraud detection. Data Min. Knowl. Disc. 18(1) 30–55 (2009). http://www.springerlink.com/index/10.1007/s10618-008-0116-z, http://dx.doi.org/10.1007/s10618-008-0116-z
Lee, T.S., Yeh, Y.H.: Corporate governance and financial distress: evidence from Taiwan. Corp. Gov. Int. Rev. 12(3), 378–388 (2004)
Meyer, P.A., Pifer, H.: Prediction of bank failures. J. Financ. 25, 853–868 (1970)
Spathis, C.T.: Detecting false financial statements using published data: some evidence from Greece. Manag. Auditing J. 17(4), 179–191 (2002)
Dimitras, A.I., Zanakis, S.H., Zopounidis, C.: A survey of business failure with an emphasis on prediction methods and industrial applications. Eur. J. Oper. Res. 90(3), 487–513 (1996)
Altman, E.L., Edward, I., Haldeman, R., Narayanan, P.: A new model to identify bankruptcy risk of corporations. J. Banking Financ. 1, 29–54 (1977)
Blum, M.: Failing company discriminant analysis. J. Account. Res., 1–25 (1974)
Laitinen, E.K., Laitinen, T.: Bankruptcy prediction application of the Taylor’s expansion in logistic regression. Int. Rev. Financ. Anal. 9, 327–349 (2000)
Shin, K.S., Lee, Y.J.: A genetic algorithm application in bankruptcy prediction modeling. Expert Syst. Appl. 23(3), 321–328 (2002)
Tsai, C.F.: Feature selection in bankruptcy prediction. Knowl.-Based Syst. 22(2), 120–127 (2009)
Bhattacharyya, S., Jha, S., Tharakunnel, K., Westland, J.C.: Data mining for credit card fraud: a comparative study. Decis. Support Syst. 50(3), 602–613 (2011)
Feroz, E.H., Kwon, T.M., Pastena, V., Park, K.J.: The efficacy of red flags in predicting the SEC’s targets: an artificial neural networks approach. Int. J. Intell. Syst. Account. Financ. Manag. 9(3), 145–157 (2000)
Pacheco, R., Martins, A., Barcia, R.M., Khator, S.: A hybrid intelligent system applied to financial statement analysis. In: Proceedings of the 5th IEEE Conference on Fuzzy Systems, vol. 2, pp. 1007–10128, New Orleans, LA, USA (1996)
Koskivaara, E.: Different pre-processing models for financial accounts when using neural networks for auditing. In: Proceedings of the 8th European Conference on Information Systems, vol. 1, pp. 326–3328, Vienna, Austria (2000)
Busta, B., Weinberg, R.: Using Benford’s law and neural networks as a review procedure. Manag. Auditing J. 13(6), 356–366 (1998)
Sohl, J.E., Venkatachalam, A.R.: A neural network approach to forecasting model selection. Inf. Manag. 29(6), 297–303 (1995)
Calderon, T.G., Cheh, J.J.: A roadmap for future neural networks research in auditing and risk assessment. Int. J. Account. Inf. Syst. 3(4), 203–236 (2002)
Sugumaran, V., Muralidharan, V., Ramachandran, K.I.: Feature selection using decision tree and classification through proximal support vector machine for fault diagnostics of roller bearing. Mech. Syst. Signal Process. 21(2), 930–942 (2007)
Olszewski, D.: Fraud detection using self-organizing map visualizing the user profiles. Knowl.-Based Syst. 70, 324–334 (2014)
Chen, A.P., Chen, C.C.: A new efficient approach for data clustering in electronic library using ant colony clustering algorithm. Electron. Libr. 24(4), 548–559 (2006)
Srivastava, A., Kundu, A., Sural, S., Majumdar, A.: Credit card fraud detection using hidden Markov model. IEEE Trans. Dependable Secur. Comput. 5(1), 37–48 (2008)
Dorronsoro, J.R., Ginel, F., Sánchez, C., Cruz, C.S.: Neural fraud detection in credit card operations. IEEE Trans. Neural Netw. 8(4), 827–834 (1997)
Chen, R., Chen, T., Lin, C.: A new binary support vector system for increasing detection rate of credit card fraud. Int. J. Pattern Recogn. Artif. Intell. 20(2), 227–239 (2006)
Zaslavsky, V., Strizhak, A.: Credit card fraud detection using self-organizing maps. Inf. Secur. 18, 48–63 (2006)
Gao, Z., Ye, M.: A framework for data mining-based anti-money laundering research. J. Money Laundering Control 10(2), 170–179 (2007)
Atwood, J.A., Robinson-Cox, J.F., Shaik, S.: Estimating the prevalence and cost of yield-switching fraud in the federal crop insurance program. Am. J. Agric. Econ. 88(2), 365–381 (2006)
Jin, Y., Rejesus, R.M., Little, B.B.: Binary choice models for rare events data: a crop insurance fraud application. Appl. Econ. 37(7), 841–848 (2005)
Major, J.A., Riedinger, D.R.: EFD: a hybrid knowledge/statistical-based system for the detection of fraud. J. Risk Insur. 69(3), 309–324 (2002)
He, H., Wang, J., Graco, W., Hawkins, S.: Application of neural networks to detection of medical fraud. Expert Syst. Appl. 13(4), 329–336 (1997)
Sokol, L., Garcia, B., Rodriguez, J., West, M., Johnson, K.: Using data mining to find fraud in HCFA health care claims. Top. Health Inf. Manag. 22(1), 1–13 (2001)
Yamanishi, K., Takeuchi, J., Williams, G., Milne, P.: On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms. Data Min. Knowl. Discov. 8(3), 275–300 (2004).[47]
Brockett, P.L., Derrig, R.A., Golden, L.L.: Fraud classification using principal component analysis of RIDITS. J. Risk Insur. 69(3), 341–371 (2002)
Viaene, S., Derrig, R.A., Baesens, B., Dedene, G.: A comparison of state-of-the-art classification techniques for expert automobile insurance claim fraud detection. J. Risk Insur. 69(3), 373–421 (2002) [50]
Pathak, J., Vidyarthi, N., Summers, S.L.: A fuzzy-based algorithm for auditors to detect elements of fraud in settled insurance claims. Manag. Auditing J. 20(6), 632–644 (2005)
Tennyson, S., Salsas-Forn, P.: Claims auditing in automobile insurance: fraud detection and deterrence objectives. J. Risk Insur. 69(3), 289–308 (2002)
Artı́s, M., Ayuso, M., Guillén, M.: Modelling different types of automobile insurance fraud behaviour in the Spanish market. Insur. Math. Econ. 24(1), 67–81 (1999)
Artı́s, M., Ayuso, M., Guillén, M.: Detection of automobile insurance fraud with discrete choice models and misclassified claims. J. Risk Insur. 69(3), 325–340 (2002)
Viaene, S., Ayuso, M., Guillén, M., Van Gheel, D., Dedene, G.: Strategies for detecting fraudulent claims in the automobile insurance industry. Eur. J. Oper. Res. 176(1), 565–583 (2007)
Deshmukh, A., Romine, J., Siegel, P.H.: Measurement and combination of red flags to assess the risk of management fraud: a fuzzy set approach. Manag. Financ. 23(6), 35–48 (1997)
Weisberg, H.I., Derrig, R.A.: Quantitative methods for detecting fraudulent automobile bodily injury claims. Risques 35, 75–101 (1998)
Brockett, P.L., Xia, X., Derrig, R.A.: Using Kononen’s self-organizing feature map to uncover automobile bodily injury claims fraud. J. Risk Insur. 65(2), 245–274 (1998)
Viaene, S., Derrig, R.A., Dedene, G.: A case study of applying boosting naive Bayes to claim fraud diagnosis. IEEE Trans. Knowl. Data Eng. 16(5), 612–620 (2004)
Sternberg, M., Reynolds, R.G.: Using cultural algorithms to support re-engineering of rule-based expert systems, in dynamic performance environments: a case study in fraud detection. IEEE Trans. Evol. Comput. 1(4), 225–243 (1997)
Crocker, K.J., Tennyson, S.: Insurance fraud and optimal claims settlement strategies. J. Law Econ. 45, 469–507 (2002)
Belhadji, E.B., Dionne, G., Tarkhani, F.: A model for the detection of insurance fraud. Geneva Papers Risk Insur. 25(4), 517–538 (2000)
Welch, J., Reeves, T.E., Welch, S.T.: Using a genetic algorithm-based classifier system for modeling auditor decision behavior in a fraud setting. Int. J. Intell. Syst. Account. Financ. Manag. 7(3), 173–186 (1998)
Deshmukh, A., Deshmukh, Talluru: A rule-based fuzzy reasoning system for assessing the risk of management fraud. Int. J. Intell. Syst. Account. Financ. Manag. 7(4), 223–241 (1998)
Bai, B., Yen, J., Yang, X.: False financial statements: characteristics of China’s listed companies and CART detecting approach. Int. J. Inf. Technol. Decis. Making 7(2), 339–359 (2008)
Lin, J.W., Hwang, M.I., Becker, J.D.: A fuzzy neural network for assessing the risk of fraudulent financial reporting. Manag. Auditing J. 18(8), 657–665 (2003)
Holton, C.: Identifying disgruntled employee systems fraud risk through text mining: a simple solution for a multi-billion dollar problem. Decis. Support Syst. 46(4), 853–864 (2009)
Koskivaara, E.: Artificial neural networks in auditing: state of the art. ICFAI J. Audit Pract. 1(4), 12–33 (2004)
Chen, H.J., Huang, S.Y., Shih, Y.N., Hsiao, C.T.: Discussing the financial fraud factor detection. Chin. Manage. Rev. 12(4), 1–22 (2009)
Huang, S.Y., Lin, C.C., Chiu, A.A.: Using data mining techniques to identify and rank the fraud factors. In: American Accounting Association Annual Meeting and Conference on Teaching and Learning in Accounting (2014)
Altman, E.L.: Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. J. Financ. 23(3), 589–609 (1968)
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Bouazza, I., Ameur, E.B., Ameur, F. (2019). Datamining for Fraud Detecting, State of the Art. In: Ezziyyani, M. (eds) Advanced Intelligent Systems for Sustainable Development (AI2SD’2018). AI2SD 2018. Advances in Intelligent Systems and Computing, vol 915. Springer, Cham. https://doi.org/10.1007/978-3-030-11928-7_17
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