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Finansal Suçların Tespitinde Veri Madenciliği Yaklaşımı ve Literatüre Bakış

Yıl 2016, Cilt: 11 Sayı: 2, 93 - 118, 01.08.2016

Öz

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.

Kaynakça

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Data Mining Approach In Financial Fraud Detection and a Literature Review

Yıl 2016, Cilt: 11 Sayı: 2, 93 - 118, 01.08.2016

Öz

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.

Kaynakça

  • 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 NetworkBased 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”, Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi 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.
  • 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.
  • Bozdoğan, 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.
  • Dıáz, 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.
  • D.T. Larouse (2004), Discovering Knowledge in Data: An Introduction to Data Mining, John Wiley & Sons .
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Toplam 111 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Research Article
Yazarlar

M. Fevzi Esen

Yayımlanma Tarihi 1 Ağustos 2016
Yayımlandığı Sayı Yıl 2016 Cilt: 11 Sayı: 2

Kaynak Göster

APA Esen, M. F. (2016). Finansal Suçların Tespitinde Veri Madenciliği Yaklaşımı ve Literatüre Bakış. Eskişehir Osmangazi Üniversitesi İktisadi Ve İdari Bilimler Dergisi, 11(2), 93-118.