You are here

Finansal Suçların Tespitinde Yaklaşımı ve Literatüre Bakış

Data Mining Approach In Financial Fraud Detection and a Literature Review

Journal Name:

Publication Year:

Abstract (2. Language): 
Only in USA Stock Exchanges, daily avarage trading volume is about 7 billion units. Just depending on this statistics, the necessity of information discovery hidden in data is a reality to tackle the problems in strategic, tactical and operational decision processes with lower costs and higher reliability. Information discovery from databases, namely, data mining is an effective method for auditing financial statements in companies and providing flow of information between market players as well as risk and portfolio management as banking applications. In this study, 79 journal articles related to the subject published 1994-2015 have been classified and evaluated in terms of data mining techniques. It has been found that data mining techniques have been applied most extensively to detection of banking and insurance fraud. However, the findings of literature review show that the number of studies in detection and prediction of financial fraud is not enough in Turkey.
Abstract (Original Language): 
Sadece Amerika Birleşik Devletleri hisse senedi piyasalarında günlük ortalama işlem miktarının 7 milyar adet olarak gerçekleştiği bile baz alındığında, stratejik, taktik ve operasyonel karar süreçlerindeki problemlerin daha düşük maliyetle ve yüksek güvenilirlikle çözülebilmesi için veri içerisinde saklı bulunan bilgilerin keşfedilmesi gerektiği bir gerçektir. Veri madenciliği olarak adlandırılan bu bilgi keşfi süreci; risk ve portföy yönetimi gibi bankacılık uygulamalarının yanısıra; şirketlerdeki finansal raporlamaların denetlenmesi ve piyasa oyuncuları arasında doğru bilgi akışının sağlanmasında etkin bir şekilde kullanılmaktadır. Bu çalışmada, 1994 - 2015 yılları arasında yayınlanan 79 adet bilimsel makale, finansal suç kategorisine göre sınıflandırılmış ve veri madenciliği tekniklerine göre değerlendirilmiştir. Çalışmada, veri madenciliği tekniklerinin çoğunlukla bankacılık ve sigorta suçlarının tespitinde kullanıldığı tespit edilmiş olup; finansal suçların veri madenciliğiyle tespiti ve tahminlenmesine yönelik Türkiye'deki çalışmaların yetersiz olduğu sonucuna varılmıştır.
93
118

REFERENCES

References: 

ACFE (2012), ''ACFE Report to the nations on occupational fraud and abuse, Technical report- Global fraud survey 2012'', http://www.acfe.com (Erişim: 01.06.2014).
Aleskerov, E., Freisleben, B., Rao, B. (1997), ''CARDWATCH: A Neural Network-Based Database Mining System for Credit Card Fraud Detection'', Proc. of the IEEE/IAFE on Computational Intelligence for Financial Engineering, 220-226.
Artis, M., Ayuso, M., Guillen, M. (1999), ''Modelling different types of automobile insurance fraud behaviour in the Spanish market'', Insurance: Mathematics and Economics, 24, 67-81.
Ata,
H
. Ali, Seyrek, İbrahim H. (2009), ''The Use Of Data Mining Techniques In Detecting Fraudulent Financial Statements: An Application on Manufacturing Firms", Suleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakultesi Dergisi, 14(2), 157-170.
Atwood, J.A., Robison, J.F., Shaik, S. (2006), ''Estimating the prevalence and cost of yield-switching fraud in the federal crop insurance program'', American Journal of Agricultural Economics, 88(2), 365-381.
B. Bell, Timothy, V. Carcello, J. (2000), ''A Decision Aid for Assessing the Likelihood of Fraudulent Financial Reporting'', Auditing: A Journal of Practice & Theory, 19(1),
169-184.
Bai, B. J., Yang, Yen, X. (2008), ''False financial statements: Characteristics of China's listed companies and cart detecting approach'', International Journal of Information Technology & Decision Making, 7, 339-359.
Beasley, Mark S. (1996), ''An Empirical Analysis of the Relation between the Board of Director Composition and Financial Statement Fraud'', The Accounting Review,
71(4), 443-465.
Benford, F. (1938), "The law of anomalous numbers", Proceedings of the American Philosophical Society, 78 (4), 551-572.
Bermudez, Ll., Perez, J.M., Ayuso, M. Gomez, E., Vazquez, F.J. (2008), ''A Bayesian dichotomous model with asymmetric link for fraud in insurance'', Insurance: Mathematics and Economics, 42(2), 779-786.
Bhattacharyya, S., Jha, S., Tharakunnel, K., Christopher Westland, J. (2011), ''Data mining for credit card fraud: A comparative study'', Decision Support Systems,
50(3), 602-613.
AĞUSTOS 2016
109
Bozdogan, H. (2004), Statistical Data Mining and Knowledge Discovery, Washington: A CRC Press Company.
Böhme,
Rainer
, Holz, Thorsten (2006), ''The Effect of Stock Spam on Financial Markets'', http://ssrn.com/abstract=897431, 04.05.2014 (Erişim: 12.05.2015).
Brabazon, A., Cahill, J., Keenan, P., Walsh, D. (2010), ''Identifying online credit card fraud using Artificial Immune Systems'', IEEE Congress on Evolutionary Computation, IEEE, 1-7.
Brockett, P. L., R. A. Derrig, Xia, X. (1998), ''Using Kohonen's Self-Organizing Feature Map to Uncover Automobile Bodily Injury Claims Fraud'', Journal of Risk and Insurance, 65, 245-274.
Brockett, P. L., Derrig, R.A., Golden, L.L., Levine, Alpert, A. M. (2002), ''Fraud Classification Using Principal Component Analysis of RIDITs'', Journal of Risk and Insurance, Vol. 69, Issue 3, s. 341-372.
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. K., Fan, W., Prodromidis, A. L., Stolfo, S. J. (1999), ''Distributed data mining in credit card fraud detection'', IEEE Intelligent Systems, 14( 6), 67-74.
Chan, P. K., Fan, W., Prodromidis, A. L., Stolfo, S. J. (1999), ''Distributed data mining in credit" .
Chen, M.Y. (2011), ''Predicting corporate financial distress based on integration of decision tree classification and logistic regression'', Expert Systems with
Applications, 38(9), 11261-11272.
Cox, E. (1995), ''A Fuzzy System for Detecting Anomalous Behaviors in Healthcare Provider Claims'', Intelligent Systems for Finance and Business, 111-134.
Deshmukh, A., Romine, J., Siegel, P.H. (1997), ''Measurement and combination of red flags to assess the risk of management fraud: a fuzzy set approach'', Managerial Finance, 23(6), 35-48.
Dıaz, D.,
Theodoulidis
, B., Sampaio, P. (2011), ''Analysis of stock market manipulations using knowledge discovery techniques applied to intraday trade prices'', Expert Systems with Applications, 38, 12757-12771.
Didimo ,Walter, Liotta, Giuseppe, Montecchiani, Fabrizio (2014), ''Network visualization for financial crime detection'', Journal of Visual Languages & Computing, 25(4), 433-451.
110
ESKİŞEHİR OSMANGAZİ ÜNİVERSİTESİ İİBF DERGİSİ
D. T. Larouse (2004), Discovering Knowledge in Data: An Introduction to Data Mining, John Wiley & Sons .
Don, M., Casey, C. (1998), Building a Better Data Warehouse, USA: Prentice Hall
Donoho, S. (2004), ''Early detection of insider trading in option markets'', In Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining, 420-429.
Drezewski, Rafal, Sepielak, Jan, Filipkowski, Wojciech (2012), ''System supporting money laundering detection'', Digital Investigation, 9(1), 8-21
Duman, Ekrem, Eliküçük, İlker (2013), ''Applying Migrating Birds Optimization to Credit Card Fraud Detection'',Trends and Applications in Knowledge Discovery and Data Mining Lecture Notes in Computer Science, 7867, 416-427.
Duman, E., Ozcelik, M.H. (2011), "Detecting credit card fraud by genetic algorithm and scatter search", Expert Systems with Applications, 38, 3057-13063.
Durtschi, C., Hillison, W., Pacini, C. (2004), ''The Effective Use of Benford's Law to Assist in detecting fraud in accounting data'', Journal of Forensic Accounting, 5,17¬34.
E. Turban, J.E. Aronson, T.P. Liang, R. Sharda (2010), Decision Support and Business Intelligence Systems, Prentice Hall.
Eining, M., Jones, D.R., Loebbecke, J.K. (1997), ''Reliance on decision aids: an examination of auditors' assessment of management fraud,'' Auditing: A Journal of Practice & Theory, 16(2), 1-19.
EMC2 (2014), "EMC2 Data Report'', http://www.emc.com, (Erişim: 21.04.2014).
EMC2 (2014), ''The digital Universe in 2020: Big Data, Bigger Digital Shadows, and Biggest Growth in the Far East'', http://www.emc.com, (Erişim: 16.03.2015).
European Commission (2015), ''Financial Crime'', (Çevrimiçi) http://ec.europa.eu/internal_market/company/financial-crime,
(Erişim:04.04.2015).
Federal Bureau of Investigation (2011), ''Financial Crimes Report to the Public of 2011'', http://www.fbi.gov/stats-services/publications, (Erişim: 04.04.2014).
Federal Reserve Bank (2013), ''The 2013 Federal Reserve Payments Study'', http://www.frbservices.org, (Erişim: 11.05.2015).
AĞUSTOS 2016
111
Federal Bureau of Investigation New York Divisio (2014), ''White-Collar Crime'', http://newyork.fbi.gov/dojpressrel/pressrel08/nyfo121108.htm (Erişim:
22.04.2014).
Financial
Conduc
t Authority (2013), "The changing face of financial crime: Report of 2013", http://www.fca.org.uk (Erişim: 03.05.2015).
Fraser, I. A. M., Hatherly, D. J., Lin, K. Z. (1997) ''An empirical investigation of the use of analytical review by external auditors'', The British Accounting Review, 29(1),
35-47.
Gao Z. (2009), ''Application of cluster-based local outlier factor algorithm in anti-money laundering'', Conference on Management and Service Science, 1-4.
Gao, Zengan, Ye, Mo (2007), "A framework for data mining based anti-money laundering research", Journal of Money Laundering Control, 10(2), 170 - 179.
Ghosh, S., Reilly, D.L. (1994), "Credit Card Fraud Detection with a Neural-Network," Proceedings on 27th Hawaii International Conference on System Sciences: Information Systems: Decision Support and Knowledge-Based Systems, 3, 621-630.
Giudici, P. (2003), Applied Data Mining Statistical Methods for Business and Industry, Wiley.
Glancy, F. H., Yadav, S. B. (2011), ''A computational model for financial reporting fraud detection: On quantitative methods for detection of financial fraud'', Decision Support Systems, 50(3), 595-601.
Goldberg, H. (2003), ''The NASD Securities Observation, News Analysis & Regulation System (SONAR)'', Conference Proceedings of Innovative Applications of Artificial Intelligence, 11-18.
Gonzalez, P.C., Velasquez, J.D. (2013), ''Characterization and detection of taxpayers with false invoices using data mining techniques'', Expert Systems with Applications, 40(5), 1427-1436.
Green, B. P., Choi, J. H. (1997), ''Assessing the risk of management fraud through neural network technology'', Auditing: A Journal of Practice & Theory, 16, 14-28.
Gupta, S., Hossain, L. (2011), ''Towards near-real-time detection of insider trading behaviour through social networks'', Computer Fraud & Security, 2011(1), 7-16.
Han, J., Kamber, M. (2001), Data Mining: Concepts and Techniques, USA: Morgan Kaufman Publishers, Academic Press.
Hand, D.J. (1992), Discrimination and Classification, Chichester: Wiley.
112
ESKİŞEHİR OSMANGAZİ ÜNİVERSİTESİ İİBF DERGİSİ
Hand, D.J. (1997), Construction and Assessment of Classification Rules, Chichester:
Wiley.
Holton, C. (2009), ''Identifying disgruntled employee systems fraud risk through text mining: a simple solution for a multi-billion dollar problem'', Decision Support Systems, 46(4), 853-864.
Huang, S.Y., Tsaih, R.H., Yu, F. (2014), "Topological pattern discovery and feature extraction for fraudulent financial reporting", Expert Systems with Applications,
41(9), 4360-4372.
Insurance Europe (2013), "The Impact of Insurance Fraud", http://www.insuranceeurope.eu/ (Erişim: 19.03.2015).
Jha, Sanjeev, Guillen, Montserrat, Westland, J.C. (2012), ''Employing transaction
aggregatio
n strategy to detect credit card fraud'', Expert Systems with Applications,
39(16), 12650-12657.
Jin, Yufei, R.M., Roderick, Little, B.B. (2005), ''Binary choice models for rare events data: a crop insurance fraud application'', Applied Economics, 37(7), 841-848.
Karaatlı, M., Senal, S., Öztürk, M.S. (2014), "Denetim Planlaması Aşamasında Analitik İnceleme Tekniği olarak Yapay Sinir Ağları Kullanımı: Bir Firma Uygulaması", Ege Academic Review, 14(4), 637-648.
Khoa Cao, Dang, Do, Phuc. (2012), ''Applying Data Mining in Money Laundering Detection for the Vietnamese Banking Industry'', Intelligent Information and Database Systems Lecture Notes in Computer Science, 7197, 207-216.
Kırlıoğlu, H., Ceyhan İ. (2014), "Mali Tablo Denetiminde Ön Analitik İnceleme Tekniği olarak Veri Madenciliğinin Kullanımı: Borsa İstanbul Uygulaması", Akademik Yaklaşımlar Dergisi, 5(1), 13-36.
Kirkland, J.D., Senator, T.E., Hayden, J.J., Dybala, T., Goldberg, H.G., Shyr, P. (1998), ''The NASD Regulation Advanced Detection System (ADS)'', Association for the Advancement of Artificial Intelligence, 20(1), 55-67.
Kirkos, E., Spathis, C., Manolopoulos, Y. (2007), ''Data Mining Techniques for the detection of fraudelent financial statements'', Expert Systems with Applications,
32(4), 995-1003.
Kirlidog, Melih, Asuk, Cuneyt (2012), ''A Fraud Detection Approach with Data Mining in Health Insurance'', Procedia - Social and Behavioral Sciences, 62, 989¬994.
AĞUSTOS 2016
113
Koskivaara, E. (2004), ''Artificial neural networks in analytical review procedures", Managerial Auditing Journal, 19(2), 191 - 223.
Kotsiantis, S., Koumanakos, E. D., Tampakas, Tzelepis, V. (2006), ''Forecasting Fraudulent Financial Statements Using Data Mining'', International Journal of Computational Intelligence, 2, 104-110.
Koyuncugil, S., Özgülbaş, N.
(2007)
, "Detecting Financial Early Warning Signs In Istanbul Stock Exchange by Data Mining", International Journal of Business Research, 7(3).
Küçükkocaoğlu,
G.
, Benli, Y.K., Küçüksözen, C. (1997), "Finansal Bilgi Manipülasyonunun Tespitinde Yapay Sinir Ağı Modelinin Kullanımı", İMKB Dergisi,
9 (36), 1-30.
Lisboa, P., Edisbury, B., Vellido, A. (2000), Business Applications of Neural Networks: The State-Of-The-Art of Real-World Applications, World Scientific Pub Co.Inc.
Liu, X., Zhang, P., Zeng, D. (2008), ''Sequence Matching for Suspicious Activity Detection in Anti-Money Laundering'', Intelligence and Security Informatics Lecture Notes in Computer Science, 5075, 50-61.
Major, J. A., Riedinger, D. R. (2002), ''EFD: A Hybrid Knowledge / Statistical Based System for the Detection of Fraud'', Journal of Risk and Insurance, 69(3), 309-324.
McLachlan, G.J. (1992), Discriminant Analysis and Statistical Pattern Recognition, New York: Wiley.
Mongkolnavin, J., Tirapat, S. (2009), ''Marking the Close analysis in Thai Bond Market Surveillance using association rules'', Expert Systems with Applications,
36(4), 8523- 8527.
Musal, R. M. (2010), ''Two models to investigate Medicare fraud within unsupervised databases'', Expert Systems with Applications, 37(12), 8628-8633.
Nigrini, M.J. (1999), ''I've got your number'', Journal of Accountancy, 187(5), 79¬83.
NYSE (2014), ''NYSE Market Data'', http://www.nyxdata.com/Data-
Products/Product-Summaries, (Erişim: 15.05.2014).
Olszewski, Dominik (2014), "Fraud detection using self-organizing map visualizing the user profiles", Knowledge-Based Systems, 70, 324-334.
Ögüt,
H.
, Aktaş, R., Alp, A., Doğanay, M.M. (2009), ''Prediction of financial information manipulation by using support vector machine and probabilistic neural network'', Expert Systems with Applications, 36(3), 5419-5423.
114
ESKİŞEHİR OSMANGAZİ ÜNİVERSİTESİ İİBF DERGİSİ
Pathak, J., Vidyarthi, N., Summers, S.L. (2005), ''A fuzzy-based algorithm for auditors to detect elements of fraud in settled insurance claims'', Managerial Auditing
Journal, 20(6), 632-644.
Phua, Clifton, Lee, Vincent, Smith, Kate, Gayler, R. (2014), ''A Comprehensive Survey ofData Mining-based Fraud Detection Research'', http://www.arxiv.org, (Erişim:
18.02.2015).
PricewaterhouseCoopers (2014), ''Global Economic Crime Survey of 2014'', http://www.pwc.com, (Erişim 23.06.2014).
PricewaterhouseCoopers (2014), ''Global Economic Crime Survey of 2014'', http://www.pwc.com, (Erişim: 23.06.2014).
Provost, F., Fawcett, T. (2001), ''Robust classification for imprecise environments'', Machine Learning, 42, 2001, 203-210.
Raza, Saleha, Haider, Sajjad (2011), ''Suspicious activity reporting using dynamic bayesian networks'', Procedia Computer Science, 3, 987-991.
Quah T. S, Sriganesh, M. (2008), ''Real-time credit card fraud using computational intelligence'', Expert Systems with Applications, 35(4), 1721-1732.
Rao, C.A. (2005), Handbook of Statistics, Elsevier.
Ravisankar, P., Ravi, V., Raghava, G., Bose, I. (2011) "Detection of financial statement fraud and feature selection using data mining techniques'', Decision Support Systems, 50(2), 491-500.
Rejesus, R.M., Little, B., Lovell, A. (2004), "Using Data Mining to Detect Crop Insurance Fraud: Is there a Role for Social Scientists?", Journal of Financial Crime,
12(1), 24-32.
Robert, Groth (2000), Data Mining: Building Competitive Advantage, USA: Prentice
Hall PTR.
Safer, Alan M. (2002), The application of neural networks to predict abnormal stock returns using insider trading data, Applied Stochastic Models in Business and
Industry, 18(4), 381-389.
Shin, H., Park, H., Lee, J., Jhee, W. C. (2012), ''A scoring model to detect abusive billing patterns in health insurance claims'', Expert Systems with Applications, 39(8),
7441-7450.
AĞUSTOS 2016
115
Shunnar, T., Barry, P.M., Shunnar, T. (2011), ''Tracking Fraudulent Activities in Real Estate Transactions'', International Federations of Surveyors, 1-15.
Singleton, T.W. (2010), Fraud Auditing and Forensic Accounting 4th edition, USA: John Wiley and Sons.
Sokol, L., Garcia, B., Rodriguez, J., West, M., Johnson, K. (2001), ''Using data mining to find fraud in HCFA health care claims'', Topics in Health Information Management, 22(1), 1-13.
Spathis, C., Doumpos, M., Zopounidis, C. (2002), ''Detecting falsified financial statements: a comparative study using multicriteria analysis and multivariate statistical techniques'', The European Accounting Review, 11(3), 509-535.
Stefano, B., Gisella, F. (2001), ''Insurance Fraud Evaluation: A Fuzzy Expert System'', Proc. of IEEE International Fuzzy Systems Conference, 1491-1494.
Subelj, L., Furlan, S., Bajec, M. (2011), ''An expert system for detecting automobile insurance fraud using social network analysis'', Expert Systems with Applications,
38(1), 1039-1052.
Tamersoy, A., Xie, B., Lenkey, S.L., Routledge, B.R., Chau, D.H., Navathe, S.B.(2013), ''Insider Trading: Patterns & Discoveries from a Large Scale Exploratory Analysis'', Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 797-804.
Terzi, S., Şen, İ.K. (2015), "Adli Muhasebede Hilelerin Tespitinde Yapay Sinir Ağı Modelinin Kullanımı", Uluslararası İktisadi ve İdari İncelemeler Dergisi, 14, 477-490.
The World Bank (2014), ''Financial Indicators'', http://data.worldbank.org/indicator, (Erişim: 13.05.2014).
Thornton, D., Mueller, R. M., Schoutsen, P., Van Hillegersberg, J. (2013), ''Predicting Healthcare Fraud in Medicaid: A Multidimensional Data Model and Analysis Techniques for Fraud Detection'', Procedia Technology, 9, 1252-1264.
Viaene, S., Derrig, R., Baesens, B., Dedene, G. (2002), ''A Comparison of State-of-the-Art Classification Techniques for Expert Automobile Insurance Claim Fraud Detection'', Journal of Risk and Insurance, 69(3), 373-421.
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.
Wallace, W.A. (2002), ''Assessing the quality of data used for benchmarking and decision-making'', The Journal of Government Financial Management', 51(3), 16¬22.
116
ESKİŞEHİR OSMANGAZİ ÜNİVERSİTESİ İİBF DERGİSİ
Wang, S. (2010), ''A Comprehensive Survey of Data Mining-Based Accounting-Fraud Detection Research'', International Conference on Intelligent Computation Technology and Automation, 1, 50-53.
Wang Richard Y., B. Kon, Henry, Madnick, Stuart E. (1993), "Data Quality Requirements Analysis and Modeling", Ninth International Conference of Data Engineering Proceedings Book, 1.
Welch, J., Reeves, T.E., Welch, S.T. (1998), "Using a genetic algorithm -based classifier system for modeling auditor decision behavior in a fraud setting", International Journal of Intelligent Systems in Accounting, Finance & Management,
7(3), 173-186.
Wheeler, R., Aitken, S. (2000), "Multiple Algorithms for Fraud Detection", Knowledge-Based Systems, 13(3), 93-99.
Whitrow, C., Hand, D.J., Juszczak, P., Weston, D., Adams, N.M. (2009), ''Transaction aggregation as a strategy for credit card fraud detection'', Data Mining and
Knowledge, 18(1), 30-55.
Wu, R.S., Ou, C.S., Lin, H.Y., Chang, S.I., Yen, D.C. (2012), ''Using data mining technique to enhance tax evasion detection performance'', Expert Systems with
Applications, 39(10), 8769-8777.
Xiong, Tengke, Wang, Shengrui, Mayers, E. Andre, Monga (2013), ''Personal bankruptcy prediction by mining credit card data'', Expert Systems with Applications, 40(2), 665-676.
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.
Yang, W. S., Hwang, S. Y. (2006), ''A process-mining framework for the detection of healthcare fraud and abuse'', Expert Systems with Applications, 31(1), 56-68.
Yoonseong, Kim, So Young, Sohn (2012), ''Stock fraud detection using peer group analysis'', Expert Systems with Applications, 39(10), 8986-8992.
Zaki, Mohamed, Theodoulidis, Babis, Diaz, David (2010), ''Stock-Touting Through Spam E-Mails: A Data Mining Case Study'', Journal of Manufacturing Technology Management, 22(6), 770-787.
AĞUSTOS 2016
117
Zhang M., Hua, Jingzhou, Zhang, Ruofei, Salerno, John J., Philip S. Yu (2003), "Applying data mining in investigating money laundering crimes", Association for Computing Machinery, 747-752.

Thank you for copying data from http://www.arastirmax.com