Implementing Artificial Intelligence in Forecasting the Risk of Personal Bankruptcies in Poland and Taiwan

• Autorzy: Tomasz Korol , Anestis K. Fotiadis
• Języki publikacji: EN

Abstrakty

EN

Research background: The global financial crisis from 2007 to 2012, the COVID-19 pandemic, and the current war in Ukraine have dramatically increased the risk of consumer bankruptcies worldwide. All three crises negatively impact the financial situation of households due to increased interest rates, inflation rates, volatile exchange rates, and other significant macroeconomic factors. Financial difficulties may arise when the private person is unable to maintain a habitual standard of living. This means that anyone can become financially vulnerable regardless of wealth or education level. Therefore, forecasting consumer bankruptcy risk has received increasing scientific and public attention.
Purpose of the article: This study proposes artificial intelligence solutions to address the increased importance of the personal bankruptcy phenomenon and the growing need for reliable forecasting models. The objective of this paper is to develop six models for forecasting personal bankruptcies in Poland and Taiwan with the use of three soft-computing techniques.
Methods: Six models were developed to forecast the risk of insolvency: three for Polish households and three for Taiwanese consumers, using fuzzy sets, genetic algorithms, and artificial neural networks. This research relied on four samples. Two were learning samples (one for each country), and two were testing samples, also one for each country separately. Both testing samples contain 500 bankrupt and 500 nonbankrupt households, while each learning sample consists of 100 insolvent and 100 solvent natural persons.
Findings & value added: This study presents a solution for effective bankruptcy risk forecasting by implementing both highly effective and usable methods and proposes a new type of ratios that combine the evaluated consumers' financial and demographic characteristics. The usage of such ratios also improves the versatility of the presented models, as they are not denominated in monetary value or strictly in demographic units. This would be limited to use in only one country but can be widely used in other regions of the world. (original abstract)

Słowa kluczowe

EN

Fuzzy logic; Genetic algorithms; Artificial neural networks (ANN); Consumer bankruptcy

PL

Logika rozmyta; Algorytmy genetyczne; Sztuczne sieci neuronowe (SSN); Upadłość konsumencka

• Czasopismo: Oeconomia Copernicana
• Rocznik: 2022
• Tom: 13
• Numer: nr 2
• Strony: 407--438

Twórcy

autor

Tomasz Korol

• Gdańsk University of Technology

autor

Anestis K. Fotiadis

• Zayed University, United Arab Emirates

Bibliografia

• Acosta-González, E., & Fernández-Rodríguez, F. (2014). Forecasting financial failure of firms via genetic algorithms. Computational Economics, 43, 133-157. doi: 10.1007/s10614-013-9392-9.
• 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, 168-178. doi: 10.1016/j.ejor.2012.04.009.
• Alaka, H. A., Oyedele, L. O., Owolabi, H. A., Kumar, V., Ajayi, S. O., Akinade, O. O., & Bilal, M. (2018). Systematic review of bankruptcy prediction models: towards a framework for tool selection. Expert Systems with Applications, 94, 164-184. doi: 10.1016/j.eswa.2017.10.040.
• Aller, C., & Grant, Ch. (2018). The effect of the financial crisis on default by Spanish households. Journal of Financial Stability, 36, 39-52. doi: 10.1016/j.jfs.2018.02.006.
• Altman, E. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23, 589-609. doi: 10.1111/j.1540-6261.1968.tb00843.x.
• Anastasiou, D., Louri, H., & Tsionas, M. (2016). Determinants of non-performing loans: evidence from Euro-area countries. Finance Research Letters, 18, 116-119. doi: 10.1016/j.frl.2016.04.008.
• Ari, A., Chen, S., & Ratnovski, L. (2021). The dynamics of non-performing loans during banking crises: a new database with post-COVID-19 implications. Journal of Banking and Finance, 133, 106-140. doi: 10.1016/j.jbankfin.2021.106 140.
• Aristei, D., & Gallo, M. (2016). The determinants of households' repayment difficulties on mortgage loans: evidence from Italian microdata. International Journal of Consumer Studies, 40, 453-465. doi: 10.1111/ijcs.12271
• Barba, A., & Pivetti, M. (2009). Rising household debt: its causes and macroeconomic implications-a long-period analysis. Cambridge Journal of Economics, 33(1), 113-137. doi: 10.1093/cje/ben030.
• Barboza, F., Kimura, H., & Altman, E. (2017). Machine learning models and bankruptcy prediction. Expert Systems with Applications, 83, 405-417. doi: 10.1016/j.eswa.2017.04.006.
• Bellotti, T., & Crook, J. (2013). Forecasting and stress testing credit card default using dynamic models. International Journal of Forecasting, 29, 563-574. doi: 10.1016/j.ijforecast.2013.04.003.
• Brygała, M. (2022). Consumer bankruptcy prediction using balanced and imbalanced data. Risks, 10(24), 1-13. doi:10.3390/risks10020024.
• Callejon, A. M., Casado, A. M., Fernandez, M. A., & Pelaez, J. I. (2013). A system of insolvency prediction for industrial companies using a financial alternative model with neural networks. International Journal of Computational Intelligence Systems, 6, 29-37. doi: 10.1080/18756891.2013.754167.
• Croson, R., & Gneezy, U. (2009). Gender differences in preferences. Journal of Economic Literature, 47(2), 448-474. doi: 10.1257/jel.47.2.448.
• Delen, D., Kuzey, C., & Uyar. A. (2013). Measuring firm performance using financial ratios: a decision tree approach. Expert Systems with Applications, 40, 3970-3983. doi: 10.1016/j.eswa.2013.01.012.
• Diaz-Serrano, L. (2005). Income volatility and residential mortgage delinquency across the EU. Journal of Housing Economics, 14, 153-177. doi: 10.1016/j.jhe.2005.07.003
• Dong, M. C., Tian, S., & Chen, C. W. S. (2018). Predicting failure risk using financial ratios: quantile hazard model approach. North American Journal of Economics and Finance, 44, 204-220. doi: 10.1016/j.najef.2018.01.005.
• French, D., & Vigne, S. (2019). The causes and consequences of household financial strain: a systematic review. International Review of Financial Analysis, 62, 150-156. doi:10.1016/j.irfa.2018.09.008.
• Garcia, V., Marques, A. I., Sanchez, J. S., & Ochoa-Dominguez, H. (2019). Dissimilarity-based linear models for corporate bankruptcy prediction. Computional Economics, 53, 1019-1031. doi: 10.1007/s10614-017-9783-4.
• Ghent, A. C., & Kudlyak, M. (2011). Recourse and residential mortgage default: evidence from US states. Review of Financial Studies, 24, 3139-3186. doi: 10.1093/rfs/hhr055.
• Giannopoulos, G., & Sigbjornsen, S. (2019). Prediction of bankruptcy using financial ratios in the Greek market. Theoretical Economics Letters, 9, 1114-1128. doi: 10.4236/tel.[phone].
• Gomes, F., Haliassos, M., & Ramadorai, T. (2021). Household finance. Journal of Economic Literature, 59(3), 919-1000. doi: 10.1257/jel.20201461.
• Gross, T., & Notowidigdo, M., J. (2011). Health insurance and the consumer bankruptcy decision: evidence from expansions of Medicaid. Journal of Public Economics, 95, 767-778. doi: 10.1016/j.jpubeco.2011.01.012.
• Gross, M., & Poblacion, H. (2017). Assessing the efficacy of borrower-based macroprudential policy using an integrated micro-macro model for European households. Economic Modelling, 61, 510-528. doi: 10.1016/j.econmod.2016.12.029.
• Guiso, L., Sapienza, P., & Zingales, L. (2013). The determinants of attitudes towards strategic default on mortgages. Journal of Finance, 68, 1473-1515. doi: 10.1111/jofi.12044.
• Guiso, L., & Sodini, P. (2013). Chapter 21 - household finance: an emerging field. In G. M. Constantinides, M. Harris & R. M. Stulz (Eds.). Handbook of the economics of finance, 2(b). Elsevier, 1397-1532. doi: 10.1016/B978-0-44-459406-8.00021-4.
• Hira, T. K. (2012). Promoting sustainable financial behaviour: implications for education and research. International Journal of Consumer Studies, 36, 502-507. doi: 10.1111/j.1470-6431.2012.01115.x
• Hosaka, T. (2019). Bankruptcy prediction using imaged financial ratios and convolutional neural networks. Expert Systems with Applications, 117, 287-299. doi: 10.1016/j.eswa.2018.09.039.
• I-Cheng, Y., & Che-Hui, L. (2009). The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients. Expert Systems with Applications, 36(2), 2473-2480. doi: 10.1016/j.eswa.2007.12.020.
• Jianakoplos, N. A., & Bernasek, A. (1998). Are women more risk averse? Economic Inquiry, 36(4),620-630. doi: 10.1111/j.1465-7295.1998.tb01740.x
• Jardin, P. (2018). Failure pattern-based ensembles applied to bankruptcy forecasting. Decision Support Systems, 107, 64-77. doi: 10.1016/j.dss.2018.01.003.
• Kieschnick, R., La Plante, M., & Moussawi, R. (2013). Working capital management and shareholders' wealth. Review of Finance, 17, 1827-1852. doi: 10.1093/rof/rfs043.
• Korol, T. (2021). Examining statistical methods in forecasting financial energy of households in Poland and Taiwan. Energies, 14, 1-14. doi: 10.3390/en14071821.
• Korol, T. (2020). Long-term risk class migrations of non-bankrupt and bankrupt enterprises. Journal of Business Economics and Management, 21(3), 783-804. doi: 10.3846/jbem.2020.12224.
• Korol, T. (2018). The implementation of Fuzzy Logic in forecasting financial ratios. Contemporary Economics, 12(2), 165-187. doi: 10.5709/ce.1897-9254.270.
• Korol, T., & Fotiadis, A. (2016). Applying Fuzzy Logic of expert knowledge for accurate predictive algorithms of customer traffic flows in theme parks. International Journal of Information Technology & Decision Making, 15(6), 1451-1468. doi: 10.1142/S0219622016500425.
• Kukk, M. (2016). How did household indebtedness hamper consumption during the recession? Evidence from micro data. Journal of Comparative Economics, 44, 764-785. doi: 10.1016/j.jce.2015.07.004.
• Li, L., Strahan, P., E., & Zhang, S. (2020). Banks as lenders of first resort: evidence from the COVID-19 crisis. Review of Corporate Finance Studies, 9(3), 472-500. doi: 10.1093/rcfs/cfaa009.
• Lin, F., Liang, D., Yeh, C. C., & Huang, J. C. (2014). Novel feature selection methods to financial distress prediction. Expert Systems with Applications, 41, 2472-2483. doi: 10.1016/j.eswa.2013.09.047.
• Louzada, F., Ara, A., & Fernandes, G. B. (2016). Classification methods applied to credit scoring: systematic review and overall comparison. Surveys in Operations Research and Management Science, 21, 117-135. doi: 10.1016/j.sorms. 2016.10.001.
• Lukason, O., & Hoffman, R.C. (2014). Firm bankruptcy probability and causes: an integrated study. International Journal of Business and Management, 9, 80-91. doi: 10.5539/ijbm.v9n11p80.
• Luzzetti, M., N., & Neumuller, S. (2016). Learning and the dynamics of consumer unsecured debt and bankruptcies. Journal of Economic Dynamics & Control, 67, 22-39. doi: 10.1016/j.jedc.2016.03.007.
• Mihalovic, M. (2016). Performance comparison of multiple discriminant analysis and Logit models in bankruptcy prediction. Economics and Sociology, 9, 101-118. doi: 10.14254/2071-789X.2016/9-4/6.
• Mitchell, M. (1999). An introduction to genetic algorithms. London: MIT Press.
• Nor, S. H. S, Ismail, S., & Yap, B. W. (2019). Personal bankruptcy prediction using decision tree model. Journal of Economics, Finance and Administrative Science, 24(47), 157-170. doi: 10.1108/JEFAS-08-2018-0076.
• Paskevicius, A., & Jurgaityte, N. (2015). Bankruptcy of natural persons in Lithuania: reasons and problems. Procedia - Social and Behavioral Sciences, 213, 521-526. doi: 10.1016/j.sbspro.2015.11.444.
• Patel, A., Balmer, N. J., & Pleasence, P. (2012). Debt and disadvantage: the experience of unmanageable debt and financial difficulty in England and Wales. International Journal of Consumer Studies, 36, 556-565. doi: 10.1111/j.1470-6431.2012.01121.x.
• Ptak-Chmielewska, A. (2019). Predicting micro-enterprise failures using data mining techniques. Journal of Risk and Financial Management, 12, 1-17. doi: 10.3390/jrfm12010030.
• Sun, J., Li, H., Huang, Q., & He, K. (2014). Predicting financial distress and corporate failure-a review from the state-of-the-art definitions, modeling, sampling, and featuring approaches. Knowledge-Based Systems, 57, 41-56. doi: 10.1016/j.knosys.2013.12.006.
• Thorne, D. (2010). Extreme financial strain: emergent chores, gender inequality and emotional distress. Journal of Family and Economic Issues, 31(2), 185-197. doi: 10.1007/s10834-010-9189-0.
• Tsai, C. (2014). Combining cluster analysis with classifier ensembles to predict financial distress. Information Fusion, 16, 46-58. doi: 10.1016/j.inffus.2011.12.001.
• Tufano, P. (2009). Consumer finance. Annual Review of Financial Economics, 1, 227-247. doi: 10.1146/annurev.financial.050808.114457.
• Worthington, A. C. (2006). Debt as a source of financial stress in Australian households. International Journal of Consumer Studies, 30, 2-15. doi: 10.1111/j.1470-6431.2005.00420.x.
• Wu, D. D., Zhang, Y., & Olson, D. L. (2010). Fuzzy multi-objective programming for supplier selection and risk modeling: a possibility approach. European Journal of Operational Research, 200, 774-787. doi: 10.1016/j.ejor.2009.01.026.
• Xiao, Z., Yang, X., Pang, Y., & Dang, X. (2012). The prediction for listed companies' financial distress by using multiple prediction methods with rough set and Dempster-Shafer evidence theory. Knowledge-Based Systems, 26, 196-206. doi: 10.1016/j.knosys.2011.08.001.
• Zadeh, L. (1965). Fuzzy sets. Information and Control, 8(3), 338-353. doi: 10.101 6/S0019-9958(65)90241-X.
• Zhang, J., & Thomas, L.C. (2012). Comparisons of linear regression and survival analysis using single and mixture distributions approaches in modelling LGD. International Journal of Forecasting, 28, 204-215. doi: 10.1016/j.ijforecast.2010.06.002.
• Zurawicki, L., & Braidot, N. (2005). Consumers during crisis: responses from the middle class in Argentina. Journal of Business Research, 58, 1100-1109. doi: 10.1016/j.jbusres.2004.03.005.

Identyfikatory

DOI

10.24136/oc.2022.013