A Comparative Study of Machine Learning Algorithms As an Audit Tool in Financial Failure Prediction

Yıl 2019, Cilt: 1 Sayı: 1, 11 - 32, 01.07.2019

Şafak Ağdeniz , Birol Yıldız

Öz

The main aim of this study is to show usage of machine learning as an
audit tool. Within this main aim, the object of this study is to compare the
classification performances of machine learning algorithms in financial failure
and to determine the best algorithm. Financial failure has been one of the
major research topic in accounting and finance. Financial
failure is an important task for internal auditors too. As an assurance
activity internal auditors should give an assurance about the company
continuity Early studies used traditional statistical techniques. With
the development of computer science and technology, artificial intelligence and
machine learning have been used in order to increase the accuracy. The output
that has been used in this study is classification accuracy. Our data set
consist of 216 companies’ financial data between the period 1983-2012. As a
result of the study it was seen that rule based classification algorithms’ are
more successful than the others. The decision table algorithm from this rule
based classification algorithms has reached the highest classification
performance with a ratio of 91.8%.

Anahtar Kelimeler

Financial Failure Prediction, Machine Learning, Classification, Data Mining, Audit Tool

Kaynakça

• Ahn, H. & Kim, K. J. (2009). Bankruptcy prediction modeling with hybrid case-based reasoning and genetic algorithms approach. Applied Soft Computing, 9, 599-607.
• Ali, S. & Smith, K. A. (2006). On learning algorithm selection for classification. Applied Soft Computing, 6, 119-138.
• Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, XXXI(4), 589-609.
• Altman, E. I. & Eisenbeis, R. A. (1978). Financial applications of disriminant analysis: a clarification. Journal of Financial and Quantitative Analysis, March, 185-188.
• Atiya, A. F. (2001). Bankruptcy prediction for credit risk using neural networks: a survey and new results. IEEE Transactions on Neural Networks, 12(4), 929-935.
• Barboza, F., Kimura, H. & Altman, E. (2017). Machine learning models and bankruptcy prediction. Expert Systems with Applications, 83, 405-417.
• Barniv, R., Agarwal, A. & Leach, R. (1997). Predicting the outcome following bankruptcy filing: a three-state classification using neural networks. Intelligent Systems in Accounting, Finance and Management, 6, 177-194.
• Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71-111.
• Beaver, W. H. (1968). Market prices, financial ratios and the prediction of failure. Journal of Accounting Research, Autumn, 179-192.
• Bell T. (1997). Neural nets or the logit model? a comparison of each model's ability to predict commercial bank failures. Intelligent Systems in Accounting, Finance and Management, 6, 249-264.
• Berenson, M. L., David S., Kathryn, A. &Krehbiel, T. C. (2013). Basic business statistics: concepts and applications, Australia:Pearson.
• Booth, P. J. (1983). Decomposition measures and the prediction of financial failure. Journal of Business Finance and Accounting, 10(1), 67-82.
• Boritz, J. & Kennedy, D. (1995). Effectiveness of neural networks types for prediction of business failure. Expert Systems with Applications, 9, 503-512.
• Eisenbeis, R. A. (1977). Pitfalls in the application of discriminant analysis in business, finance and economics. The Journal of Finance, XXXII(3), 875-900.
• Etheridge, H. & Sriram, R. (1997). A comparison of the relative costs of financial distress models: artificial neural networks, logit and multivariate discriminant analysis. Intelligent Systems in Accounting, Finance and Management, 6, 235-248.
• Fletcher, D. & Goss, E. (1993). Forecasting with neural networks: an application using bankruptcy data. Information and Management, 24(3), 159-167.
• Gestel T.V., Baesens, B., Suykens, J.A.K., Van Den Poel D., Baestaens D.E. & Willekens, M. (2006). Bayesian kernel based classification for financial distress detection. European Journal of Operational Research, 172, 979-1003.
• Gupta, A., Syed, A. , Mohammad, A. & Halgamuge, M.N. (2016). A comparative study of classification algorithms using data mining: crime and accidents in denver city USA. International Journal of Advanced Computer Science and Applications, 7(7), 374-381.
• Huang Z., Chen, H. , Hsu, C.J. , Chen, W. H. & Wu, S. (2004). Credit rating analysis with support vector machines and neural networks: a market comparative study. Decision Support Systems, 37, 543-558.
• Karels, G. V. & Prakash, A. J., (1987). Multivariate normality and forecasting of business bankruptcy. Journal of Business Finace and Accounting, 14(4), 573-593.
• Kim, M. J. & Han, I. (2003). The discovery of experts’ decision rules from qualitative bankruptcy data using genetic algorithms. Expert Systems with Applications, 25, 637-646.
• Kim, M. J. & Kang, D. K. (2010). Ensemble with neural networks for bankruptcy prediction. Expert Systems with Applications, 37, 3373-3379.
• Kotsiantis, S., Tzelepis, D., Koumanakos, E., &Tampakas, V. (2005). Efficiency of machine learning techniques in bankruptcy prediction. 2nd International Conference on Enterprise Systems and Accounting, July, Thessaloniki, Greece.
• Meyer, P. A. & Pifer, H. W. (1970). Prediction of bank failures. The Journal of Finance, September, 853-858.
• McKee, T. E. & Lensberg, T. (2002). Genetic programming and rough sets: a hybrid approach to bankruptcy classification. European Journal of Operational Research, 138, 436-451.
• Min, J. H., & Lee, Y.C. (2005). Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert Systems with Applications, 28, 603-614.
• Odom, M. D. and Sharda, R. (1990). A neural network model for bankruptcy prediction. Proceedings of the International Joint Conference on Neural Networks, San Diego, 163-169.
• Ohlson, J. A. (1980). Financial ratios and probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109-131.
• Pompe, P. P. & Feelders, A.J. (1997). Using machine learning, neural networks, and statistics to predict corporate bankruptcy. Microcomputers in Civil Engineering, 12, 267-276.
• Shin, K. S. & Lee, Y.J. (2002). A genetic algorithm application in bankruptcy prediction modeling. Expert Systems with Applications, 23, 321-328.
• Shin K. S., Lee, T.S. & Kim, H. J. (2005). An application of support vector machines in bankruptcy prediction model. Expert Systems with Applications, 28, 127-135.
• Tam, K.Y. & Kiang, M.Y. (1992). Managerial applications of neural networks: the case of bank failure predictions. Management Science, 38(7), 926-947.
• Tamari, M. (1966). Les ratios, moyen de prévision des faillities. Management International Review, 4, 22-27.
• Tsai, C. F. & Wu, J.W. (2008). Using neural network ensembles for bankruptcy prediction and credit scoring. Expert Systems with Applications, 34(4), 2639-2649.
• Yıldız, B. (2001). Finansal başarısızlığın öngörülmesinde yapay sinir ağı kullanımı ve halka açık şirketlerde ampirik bir uygulama. İMKB Dergisi, 17, 51-67.
• Yu, Q., Miche, Y., Severin, E. & Lendasse, A. (2014). Bankruptcy prediction using extreme learning machine and financial expertise. Neurocomputing, 128, 296-302.
• Wang, N. (2017). Bankruptcy prediction using machine learning. Journal of Mathematical Finance, 7, 908-918.
• Wilson, R. & Sharda, R. (1994). Bankruptcy prediction using neural networks. Decision Support Systems, 11(5), 545-557.
• Witten, I. H., Frank, . H. & Mark, A. (2011) Data mining, 3rd Edition:Elsevier.
• Zhang, G., Hu, M. Y., Patuwo, B. E. & Indro, D. C. (1997). Artificial neural networks in bankruptcy prediction: general framework and cross-validation analysis. European Journal of Operational Research, 116,16-32.
• Zmijewski, M. E. (1984). Methodologixal ıssues related to the estimation of financial distress prediction models” Journal of Accounting Research, 22, 59-82.
• https://na.theiia.org/standards-guidance/mandatory-guidance/Pages/Definition-of-Internal-Auditing.aspx, 10.12.2018