Akkoc, S. (2012).An empirical comparison of conventional techniques, neural networks and the three stage hybrid adaptive neuro-fuzzy inference system (ANFIS) model for credit scoring analysis: the case of Turkish credit card data. European Journal of Operational Research, 222(1), 168–178.
Al-Kassar, T., Soileau, J. (2014).Financial performance evaluation and bankruptcy prediction (failure).ARAB ECONOMICS AND BUSINESS JOURNAL, 9(2), 147–155.
Altman, E.I., Marco, G., Varetto, F. (1994). Corporate distress diagnosis: comparisons using linear discriminant analysis and Neural Networks (the Italian experience). Journal of Banking and Finance, 18(3), 505-529.
Altman, I.E. (1968).Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609.
Altman, I.E. and Narayanan, P. (1997).An international survey of business failure classification models. Financial Markets, Institutions & Instruments, 6 (2), 1-57.
Aziz, M.A., Dar, H.A. (2006). Predicting corporate bankruptcy: where we stand?. Corporate Governance: The International Journal of Effective Board Performance, 6 (1), 18-33.
Beaver, W.H. (1966).Financial ratios as predictors of failure. Journal of Accounting Research, 4 (3), 71-111.
Bekhet, H., Eletter, S. (2014). Credit risk management for the Jordanian commercial banks: Neural scoring approach. Review of Development Finance, 4, 20–28.
Cao, Q., Parry, M. (2009). Neural Network earning per share forecasting models: a comparison of backward propagation and genetic algorithm. Decision Support Systems, 47, 32–41.
Casey, C.J. and Bartczak, N.J. (1984).Cash flow – it’s not the bottom line, Harvard Business Review, July-August, 61-66.
Chen, M., Huang, S. (2003). Credit scoring and rejected instances reassigning through evolutionary computation techniques. Expert Systems with Applications, 24, 433–441.
Chen, Y., Cheng, C. (2013).Hybrid models based on rough set classifiers for setting credit rating decision rules in the global banking industry. Knowledge-Based Systems, 39,
Courtis, J.K. (1978).Modeling a financial ratio categorical framework. Journal of Business Finance and Accounting, 5 (4), 371-86.
Curram, S., Mingers, J. (1994). Neural Networks, Decision tree induction and discriminant analysis: an empirical comparison. Operational Research Society, 45 (4), 440-450.
Dimitras, A.I., Zanakis, S.H. and Zopounidis, C. (1996).A survey of business failures with an emphasis on prediction methods and industrial applications. European Journal of Operational Research, 90 (3), 487-513.
Durand, D. (1941). Risk Elements in Consumer Instalment Financing. National Bureau of Economic Research, New York.
Efroymson, M.A. (1960).Multiple Regression Analysis. Mathematical Methods for Digital Computers, John Wiley, New York.191-203.
Emel, A., Oral, M., Reisman, A., Yolalan, R. (2003).A credit scoring approach for the commercial banking sector. Socio-Economic Planning Sciences, 37 (2),
Gestel, T.V., Baesens, B., Dijcke, P.V., Suykens, J.A.K., Garcia, J., Alderweireld, T. (2005). Linear and non-linear credit scoring by combining logistic regression and support vector machines. Journal of Credit Risk, 1 (4), 31-60.
Hand, D. J., and Henley, W. E. (1997). Statistical classification methods in consumer risk. Journal of the Royal Statistical Society, Series A, 160, 523–41.
Haykin, S., 1999. Neural Networks: A Comprehensive Foundation, second ed. Prentice Hall Int. Inc., New Jersey.
Heiat, A. (2012). Comparing performance of data mining models for computer credit scoring. Journal of International Finance & Economics, 12 (1), 78–83.
Hocking, R.R. (1976).The analysis and selection of variables in linear regression. Biometrics, 32, 1-49.
Jones, S., Hensher, D. (2004).Predicting Firm Financial Distress: A Mixed Logit Model. The Accounting Review, 79 (4), 1011-1038.
Kaminski, K.A., Wetzel, T.S. and Guan, L. (2004). Can financial ratios detect fraudulent financial reporting? Managerial Auditing Journal, 19 (1), 15-28.
Kankaanpää, M., Laitinen, T. (1999). Comparative analysis of failure prediction methods: the Finnish case. The European Accounting Review, 8 (1), 67-92.
Khandani, A., Kim, A., Lo, A. (2010). Consumer credit-risk models via machine learning algorithms. Journal of Banking and Finance, 34, 2767–2787.
Lahsasna, A., Ainon, R., Wah, T. (2010). Credit scoring models using soft computing methods: a survey. International Arab Journal of Information Technology, 7 (2), 115–123.
Lee, T., Chiu, C., Lu, C., Chen, I. (2002).Credit scoring using the hybrid neural discriminant technique. Expert Systems with Applications. 23 (3), 245–254.
Lee, T.S., Chen, I.F. (2005).A two-stage hybrid credit scoring model using artificial Neural Networks and multivariate adaptive regression splines. Expert Systems with Applications, 28, 743–752.
McKee, T.E., Lensberg, T. (2002).Genetic programming and rough sets: a hybrid approach to bankruptcy classification. European Journal of Operational Research, 138, 436-51.
Mossman, C. E., Bell, G. G., Turtle, H., Swartz, L. M. (1998). An empirical comparison of bankruptcy models. The Financial Review, 33, 35-54.
Paliwal, M., Kumar, U. (2009). Neural Networks and statistical techniques: a review of applications. Expert Systems with Applications, 36 (1), 2–17.
Persons, O.S. (1995).Using financial statement data to identify factors associated with fraudulent financial reporting. Journal of Applied Business Research, 11 (3), 38-46.
Piramuthu, S. (1999).Financial credit-risk evaluation with neural and neuro-fuzzy systems. European Journal of Operational Research, 112, 310–321.
Premachandra, I.M., Bhabra, G.S., Sueyoshi, T. (2009). DEA as a tool for bankruptcy assessment: A comparative study with logistic regression technique. European Journal of Operational Research, 193, 412–424.
Thomas, L. (2000). A survey of credit and behavioral scoring: forecasting financial risk of lending to consumers. International Journal of Forecasting, 16, 149–172.
Thomas, L., Edelman, D., Crook, J. (2002).Credit Scoring and its Applications. Philadelphia, PA: Society for Industrial and Application Mathematics.
Trippi, R.R., Turban, E. (1996). Neural Networks in Finance and Investing. Irwin Professional Pub., Chicago.
Tsai, C.F., Wu, J.W. (2008). Using Neural Network ensembles for bankruptcy prediction and credit scoring. Expert Systems with Applications, 34, 2639–2649.
Vuran, B. (2009). Prediction of business failure: a comparison of discriminant and logistic regression analyses. İstanbul Üniversitesi İşletmeFakültesiDergisi, 38 (1), 47-65.
West, D. (2000). Neural Network credit scoring models. Computers and Operations Research, 27, 1131–1152.
West, D., Dellana, S., Qian, J. (2005). Neural Network ensemble strategies for financial decision applications. Computers and Operations Research, 32, 2543–2559.
Wilcox, J.W. (1971). A simple theory of financial ratios as predictors of failure. Journal of Accounting Research, 9, 389-95.
Wu, C., Guo, Y., Zhang, X., Xia, H. (2010). Study of personal credit risk assessment based on support vector machine ensemble. International Journal of Innovative, 6 (5), 2353–2360.
Yalsin, N., Bayrakdaroglu, A., Kahraman, C. (2012). Application of fuzzy multi-criteria decision making methods for financial performance evaluation of Turkish manufacturing industries. Expert Systems with Applications, 39, 350–364.
Yap, P., Ong, S., Husain, N. (2011).Using data mining to improve assessment of credit worthiness via credit scoring models. Expert Systems with Applications, 38 (10), 1374–1383.