Logistic Regression or Neural Network? Which Provides Better Results for Retail Loans?





statistics, logistic regression, neural network, loan default


While there is a large literature on the prediction of corporate bankruptcies, there is little literature on the classification of retail borrowers. This is also true in our country, where there is not much scientific work on this topic. Recognising who is becoming a bad debtor is not easy. There are several ways to analyse the data, which may even show different results. In this paper, my aim is to predict the default of household loans using logistic regression and neural networks. The question is, which method produces the better results?

Author Biography

Kitti Fodor, University of Miskolc

Assistant Lecturer


Database: provided by Bisz Zrt.

Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609. https://doi.org/10.2307/2978933

Altman, E. I., Haldeman, R. G., & Narayanan, P. (1977). ZETATM analysis A new model to identify bankruptcy risk of corporations. Journal of Banking & Finance, 1(1), 29–54. https://doi.org/10.1016/0378-4266(77)90017-6

Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71-111. https://doi.org/10.2307/2490171

Blum, M. (1974). Failing company discriminant analysis. Journal of Accounting Research, 12(1), 1-25. https://doi.org/10.2307/2490525

Coats, P. K., & Fant, L. F. (1993). Recognizing financial distress patterns using a neural network tool. Financial Management, 22(3), 142-155. https://doi.org/10.2307/3665934

Deakin, E. B. (1972). A discriminant analysis of predictors of business failure. Journal of Accounting Research, 10(1), 167-179. https://doi.org/10.2307/2490225

Frydman, H., Altman, E. I., & Kao, D.-L. (1985). Introducing recursive partitioning for financial classification: The case of financial distress. The Journal of Finance, 40(1), 269–291. https://doi.org/10.1111/j.1540-6261.1985.tb04949.x

Hajdu, O. (2003): Többváltozós statisztikai számítások [Multivariate statistical calculations]. Budapest: Központi Statisztikai Hivatal.

Hajdu O. (2018). Többváltozós statisztikai R Open alkalmazások [Multivariate Statistical R Open Applications]. Statisztikai Szemle, 96(10), 1021-1047. https://doi.org/10.20311/stat2018.10.hu1021

KHR Annul Information (2021). https://www.bisz.hu/dokumentumtar (May 2023)

Ketskeméty, L., Izsó, L. & Könyves Tóth, E. (2011). Bevezetés az IBM SPSS Statistics programrendszerbe [Introduction to IBM SPSS Statistics]. Budapest: Artéria Stúdió Kft.

Kristóf, T. (2002). A mesterséges neurális hálók a jövőkutatás szolgálatában [Artificial neural networks in Futures Studies]. Futures Studies Department, Corvinus University of Budapest. https://doi.org/10.13140/RG.2.1.2835.6562

Malhotra, N. K. (2008): Marketingkutatás [Marketing research]. Budapest: Akadémiai Kiadó

Odom, M. D., & Sharda, R. (1990). A neural network model for bankruptcy prediction. 1990 IJCNN International Joint Conference on Neural Networks, 163-168. https://doi.org/10.1109/IJCNN.1990.137710

Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109-131. https://doi.org/10.2307/2490395

Olmeda, I., & Fernández, E. (1997). Hybrid Classifiers for Financial Multicriteria Decision Making: The Case of Bankruptcy Prediction. Computational Economics, 10(4), 317–335. https://doi.org/10.1023/a:1008668718837

Sajtos, L. & Mitev, A. (2007): SPSS kutatási és adatelemzési kézikönyv [SPSS research and data analysis handbook]. Alinea Kiadó.

Varga, B. & Szilágyi, R. (2011). Quantitative Information Forming Methods. Nemzeti Tankönyvkiadó. https://www.tankonyvtar.hu/hu/tartalom/tamop425/0049_08_quantitative_information_forming_methods/6127/index.html

Virág, M. (2004): A csődmodellek jellegzetességei és története [Characteristics and history of bankruptcy models]. Vezetéstudomány, 35(10), 24-32.

Virág, M., & Kristóf, T. (2005): Az első hazai csődmodell újraszámítása neurális hálók segítségével [Recalculation of the first domestic bankruptcy model using neural networks]. Közgazdasági Szemle, 52(2), 144–162.

Zhang, G., Hu, M. & Patuwo, B. (1999): Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis. European Journal of Operational Research, 116(1), 16–32. https://doi.org/10.1016/S0377-2217(98)00051-4




How to Cite

Fodor, K. (2023). Logistic Regression or Neural Network? Which Provides Better Results for Retail Loans?. Theory, Methodology, Practice - Review of Business and Management, 19(01), 53–62. https://doi.org/10.18096/TMP.2023.01.05