Implementing artificial intelligence in forecasting the risk of personal bankruptcies in Poland and Taiwan

Authors

DOI:

https://doi.org/10.24136/oc.2022.013

Keywords:

fuzzy logic, genetic algorithms, artificial neural networks, consumer bankruptcy, the financial crisis of households

Abstract

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.

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References

Acosta-Gonza?lez, E., & Ferna?ndez-Rodri?guez, F. (2014). Forecasting financial failure of firms via genetic algorithms. Computational Economics, 43, 133?157. doi: 10.1007/s10614-013-9392-9.

DOI: https://doi.org/10.1007/s10614-013-9392-9
View in Google Scholar

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 da-ta. European Journal of Operational Research, 222, 168?178. doi: 10.1016/j.ejor. 2012.04.009.

DOI: https://doi.org/10.1016/j.ejor.2012.04.009
View in Google Scholar

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.

DOI: https://doi.org/10.1016/j.eswa.2017.10.040
View in Google Scholar

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.j fs.2018.02.006.

DOI: https://doi.org/10.1016/j.jfs.2018.02.006
View in Google Scholar

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.

DOI: https://doi.org/10.1111/j.1540-6261.1968.tb00843.x
View in Google Scholar

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.

DOI: https://doi.org/10.1016/j.frl.2016.04.008
View in Google Scholar

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.

DOI: https://doi.org/10.1016/j.jbankfin.2021.106140
View in Google Scholar

Aristei, D., & Gallo, M. (2016). The determinants of households? repayment dif-ficulties on mortgage loans: evidence from Italian microdata. International Journal of Consumer Studies, 40, 453?465. doi: 10.1111/ijcs.12271

DOI: https://doi.org/10.1111/ijcs.12271
View in Google Scholar

Barba, A., & Pivetti, M. (2009). Rising household debt: its causes and macroeco-nomic implications?a long-period analysis. Cambridge Journal of Econom-ics, 33(1), 113?137. doi: 10.1093/cje/ben030.

DOI: https://doi.org/10.1093/cje/ben030
View in Google Scholar

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.

DOI: https://doi.org/10.1016/j.eswa.2017.04.006
View in Google Scholar

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.

DOI: https://doi.org/10.1016/j.ijforecast.2013.04.003
View in Google Scholar

Brygała, M. (2022). Consumer bankruptcy prediction using balanced and imbal-anced data. Risks, 10(24), 1?13. doi:10.3390/risks10020024.

DOI: https://doi.org/10.3390/risks10020024
View in Google Scholar

Callejon, A. M., Casado, A. M., Fernandez, M. A., & Pelaez, J. I. (2013). A sys-tem of insolvency prediction for industrial companies using a financial alter-native model with neural networks. International Journal of Computational Intelligence Systems, 6, 29?37. doi: 10.1080/18756891.2013.754167.

DOI: https://doi.org/10.1080/18756891.2013.754167
View in Google Scholar

Croson, R., & Gneezy, U. (2009). Gender differences in preferences. Journal of Economic Literature, 47(2), 448?474. doi: 10.1257/jel.47.2.448.

DOI: https://doi.org/10.1257/jel.47.2.448
View in Google Scholar

Delen, D., Kuzey, C., & Uyar. A. (2013). Measuring firm performance using fi-nancial ratios: a decision tree approach. Expert Systems with Applications, 40, 3970?3983. doi: 10.1016/j.eswa.2013.01.012.

DOI: https://doi.org/10.1016/j.eswa.2013.01.012
View in Google Scholar

Diaz-Serrano, L. (2005). Income volatility and residential mortgage delinquency across the EU. Journal of Housing Economics, 14, 153?177. doi: 10.1016/j.jh e.2005.07.003

DOI: https://doi.org/10.1016/j.jhe.2005.07.003
View in Google Scholar

Dong, M. C., Tian, S., & Chen, C. W. S. (2018). Predicting failure risk using fi-nancial ratios: quantile hazard model approach. North American Journal of Economics and Finance, 44, 204?220. doi: 10.1016/j.najef.2018.01.005.

DOI: https://doi.org/10.1016/j.najef.2018.01.005
View in Google Scholar

French, D., & Vigne, S. (2019). The causes and consequences of household finan-cial strain: a systematic review. International Review of Financial Analysis, 62, 150?156. doi:10.1016/j.irfa.2018.09.008.

DOI: https://doi.org/10.1016/j.irfa.2018.09.008
View in Google Scholar

Garcia, V., Marques, A. I., Sanchez, J. S., & Ochoa-Dominguez, H. (2019). Dis-similarity-based linear models for corporate bankruptcy prediction. Computional Economics, 53, 1019?1031. doi: 10.1007/s10614-017-9783-4.

DOI: https://doi.org/10.1007/s10614-017-9783-4
View in Google Scholar

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.

DOI: https://doi.org/10.1093/rfs/hhr055
View in Google Scholar

Giannopoulos, G., & Sigbjornsen, S. (2019). Prediction of bankruptcy using fi-nancial ratios in the Greek market. Theoretical Economics Letters, 9, 1114?1128. doi: 10.4236/tel.2019.94072.

DOI: https://doi.org/10.4236/tel.2019.94072
View in Google Scholar

Gomes, F., Haliassos, M., & Ramadorai, T. (2021). Household finance. Journal of Economic Literature, 59(3), 919?1000. doi: 10.1257/jel.20201461.

DOI: https://doi.org/10.1257/jel.20201461
View in Google Scholar

Gross, T., & Notowidigdo, M., J. (2011). Health insurance and the consumer bankruptcy decision: evidence from expansions of Medicaid. Journal of Pub-lic Economics, 95, 767?778. doi: 10.1016/j.jpubeco.2011.01.012.

DOI: https://doi.org/10.1016/j.jpubeco.2011.01.012
View in Google Scholar

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.2016.1 2.029.

DOI: https://doi.org/10.1016/j.econmod.2016.12.029
View in Google Scholar

Guiso, L., Sapienza, P., & Zingales, L. (2013). The determinants of attitudes to-wards strategic default on mortgages. Journal of Finance, 68, 1473?1515. doi: 10.1111/jofi.12044.

DOI: https://doi.org/10.1111/jofi.12044
View in Google Scholar

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.

DOI: https://doi.org/10.1016/B978-0-44-459406-8.00021-4
View in Google Scholar

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

DOI: https://doi.org/10.1111/j.1470-6431.2012.01115.x
View in Google Scholar

Hosaka, T. (2019). Bankruptcy prediction using imaged financial ratios and con-volutional neural networks. Expert Systems with Applications, 117, 287?299. doi: 10.1016/j.eswa.2018.09.039.

DOI: https://doi.org/10.1016/j.eswa.2018.09.039
View in Google Scholar

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. Ex-pert Systems with Applications, 36(2), 2473?2480. doi: 10.1016/j.eswa.2007.12.020.

DOI: https://doi.org/10.1016/j.eswa.2007.12.020
View in Google Scholar

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

DOI: https://doi.org/10.1111/j.1465-7295.1998.tb01740.x
View in Google Scholar

Jardin, P. (2018). Failure pattern-based ensembles applied to bankruptcy forecast-ing. Decision Support Systems, 107, 64?77. doi: 10.1016/j.dss.2018.01.003.

DOI: https://doi.org/10.1016/j.dss.2018.01.003
View in Google Scholar

Kieschnick, R., La Plante, M., & Moussawi, R. (2013). Working capital manage-ment and shareholders? wealth. Review of Finance, 17, 1827?1852. doi: 10.109 3/rof/rfs043.

DOI: https://doi.org/10.1093/rof/rfs043
View in Google Scholar

Korol, T. (2021). Examining statistical methods in forecasting financial energy of households in Poland and Taiwan. Energies, 14, 1?14. doi: 10.3390/en14071 821.

DOI: https://doi.org/10.3390/en14071821
View in Google Scholar

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.

DOI: https://doi.org/10.3846/jbem.2020.12224
View in Google Scholar

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.
View in Google Scholar

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.

DOI: https://doi.org/10.1142/S0219622016500425
View in Google Scholar

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.

DOI: https://doi.org/10.1016/j.jce.2015.07.004
View in Google Scholar

Li, L., Strahan, P., E., & Zhang, S. (2020). Banks as lenders of first resort: evi-dence from the COVID-19 crisis. Review of Corporate Finance Studies, 9(3), 472?500. doi: 10.1093/rcfs/cfaa009.

DOI: https://doi.org/10.1093/rcfs/cfaa009
View in Google Scholar

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.

DOI: https://doi.org/10.1016/j.eswa.2013.09.047
View in Google Scholar

Louzada, F., Ara, A., & Fernandes, G. B. (2016). Classification methods applied to credit scoring: systematic review and overall comparison. Surveys in Oper-ations Research and Management Science, 21, 117?135. doi: 10.1016/j.sorms. 2016.10.001.

DOI: https://doi.org/10.1016/j.sorms.2016.10.001
View in Google Scholar

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.

DOI: https://doi.org/10.5539/ijbm.v9n11p80
View in Google Scholar

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.

DOI: https://doi.org/10.1016/j.jedc.2016.03.007
View in Google Scholar

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.

DOI: https://doi.org/10.14254/2071-789X.2016/9-4/6
View in Google Scholar

Mitchell, M. (1999). An introduction to genetic algorithms. London: MIT Press.

DOI: https://doi.org/10.7551/mitpress/3927.001.0001
View in Google Scholar

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.

DOI: https://doi.org/10.1108/JEFAS-08-2018-0076
View in Google Scholar

Paskevicius, A., & Jurgaityte, N. (2015). Bankruptcy of natural persons in Lithua-nia: reasons and problems. Procedia - Social and Behavioral Sciences, 213, 521?526. doi: 10.1016/j.sbspro.2015.11.444.

DOI: https://doi.org/10.1016/j.sbspro.2015.11.444
View in Google Scholar

Patel, A., Balmer, N. J., & Pleasence, P. (2012). Debt and disadvantage: the expe-rience 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.

DOI: https://doi.org/10.1111/j.1470-6431.2012.01121.x
View in Google Scholar

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.

DOI: https://doi.org/10.3390/jrfm12010030
View in Google Scholar

Sun, J., Li, H., Huang, Q., & He, K. (2014). Predicting financial distress and cor-porate failure?a review from the state-of-the-art definitions, modeling, sam-pling, and featuring approaches. Knowledge-Based Systems, 57, 41?56. doi: 10.1016/j .knosys.2013.12.006.

DOI: https://doi.org/10.1016/j.knosys.2013.12.006
View in Google Scholar

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.

DOI: https://doi.org/10.1007/s10834-010-9189-0
View in Google Scholar

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.

DOI: https://doi.org/10.1016/j.inffus.2011.12.001
View in Google Scholar

Tufano, P. (2009). Consumer finance. Annual Review of Financial Economics, 1, 227?247. doi: 10.1146/annurev.financial.050808.114457.

DOI: https://doi.org/10.1146/annurev.financial.050808.114457
View in Google Scholar

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.

DOI: https://doi.org/10.1111/j.1470-6431.2005.00420.x
View in Google Scholar

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.2009.0 1.026.

DOI: https://doi.org/10.1016/j.ejor.2009.01.026
View in Google Scholar

Xiao, Z., Yang, X., Pang, Y., & Dang, X. (2012). The prediction for listed compa-nies? 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.

DOI: https://doi.org/10.1016/j.knosys.2011.08.001
View in Google Scholar

Zadeh, L. (1965). Fuzzy sets. Information and Control, 8(3), 338?353. doi: 10.101 6/S0019-9958(65)90241-X.

DOI: https://doi.org/10.1016/S0019-9958(65)90241-X
View in Google Scholar

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.ijforecas t.2010.06.002.

DOI: https://doi.org/10.1016/j.ijforecast.2010.06.002
View in Google Scholar

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.

DOI: https://doi.org/10.1016/j.jbusres.2004.03.005
View in Google Scholar

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Published

2022-06-30

How to Cite

Korol, T., & Fotiadis, A. K. (2022). Implementing artificial intelligence in forecasting the risk of personal bankruptcies in Poland and Taiwan. Oeconomia Copernicana, 13(2), 407–438. https://doi.org/10.24136/oc.2022.013

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