IDEAS home Printed from https://ideas.repec.org/a/pes/ieroec/v13y2022i2p407-438.html
   My bibliography  Save this article

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

Author

Listed:
  • Tomasz Korol

    (Gdañsk University of Technology, Poland)

  • Anestis K. Fotiadis

    (Zayed University, United Arab Emirates)

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.

Suggested Citation

  • Tomasz Korol & Anestis K. Fotiadis, 2022. "Implementing artificial intelligence in forecasting the risk of personal bankruptcies in Poland and Taiwan," Oeconomia Copernicana, Institute of Economic Research, vol. 13(2), pages 407-438, June.
    • Handle: RePEc:pes:ieroec:v:13:y:2022:i:2:p:407-438
    DOI: 10.24136/oc.2022.013
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.24136/oc.2022.013
    Download Restriction: no
    File URL: https://libkey.io/10.24136/oc.2022.013?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Luzzetti, Matthew N. & Neumuller, Seth, 2016. "Learning and the dynamics of consumer unsecured debt and bankruptcies," Journal of Economic Dynamics and Control, Elsevier, vol. 67(C), pages 22-39.
    2. Lei Li & Philip E. Strahan & Song Zhang, 2020. "Banks as Lenders of First Resort: Evidence from the COVID-19 Crisis," NBER Working Papers 27256, National Bureau of Economic Research, Inc.
    3. Heryati, Sharifah & Ismail, Shafinar & Wah, Bee, 2019. "Personal bankruptcy prediction using decision tree model," Journal of Economics, Finance and Administrative Science, Universidad ESAN, vol. 24(47), pages 157-170.
    4. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    5. Akkoç, Soner, 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 cred," European Journal of Operational Research, Elsevier, vol. 222(1), pages 168-178.
    6. Lei Li & Philip E Strahan & Song Zhang, 2020. "Banks as Lenders of First Resort: Evidence from the COVID-19 Crisis," The Review of Corporate Finance Studies, Society for Financial Studies, vol. 9(3), pages 472-500.
    7. Kukk, Merike, 2016. "How did household indebtedness hamper consumption during the recession? Evidence from micro data," Journal of Comparative Economics, Elsevier, vol. 44(3), pages 764-786.
    8. Gross, Tal & Notowidigdo, Matthew J., 2011. "Health insurance and the consumer bankruptcy decision: Evidence from expansions of Medicaid," Journal of Public Economics, Elsevier, vol. 95(7-8), pages 767-778, August.
    9. Tomasz Korol & Anestis Fotiadis, 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 (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(06), pages 1451-1468, November.
    10. Zurawicki, Leon & Braidot, Nestor, 2005. "Consumers during crisis: responses from the middle class in Argentina," Journal of Business Research, Elsevier, vol. 58(8), pages 1100-1109, August.
    11. Gross, Marco & Población, Javier, 2017. "Assessing the efficacy of borrower-based macroprudential policy using an integrated micro-macro model for European households," Economic Modelling, Elsevier, vol. 61(C), pages 510-528.
    12. Deborah Thorne, 2010. "Extreme Financial Strain: Emergent Chores, Gender Inequality and Emotional Distress," Journal of Family and Economic Issues, Springer, vol. 31(2), pages 185-197, June.
    13. Tomasz Korol, 2021. "Examining Statistical Methods in Forecasting Financial Energy of Households in Poland and Taiwan," Energies, MDPI, vol. 14(7), pages 1-14, March.
    14. Andra C. Ghent & Marianna Kudlyak, 2011. "Recourse and Residential Mortgage Default: Evidence from US States 1," Review of Financial Studies, Society for Financial Studies, vol. 24(9), pages 3139-3186.
    15. Rachel Croson & Uri Gneezy, 2009. "Gender Differences in Preferences," Journal of Economic Literature, American Economic Association, vol. 47(2), pages 448-474, June.
    16. Zhang, Jie & Thomas, Lyn C., 2012. "Comparisons of linear regression and survival analysis using single and mixture distributions approaches in modelling LGD," International Journal of Forecasting, Elsevier, vol. 28(1), pages 204-215.
    17. Diaz-Serrano, Luis, 2004. "Income Volatility and Residential Mortgage Delinquency: Evidence from 12 EU Countries," IZA Discussion Papers 1396, Institute of Labor Economics (IZA).
    18. Robert Kieschnick & Mark Laplante & Rabih Moussawi, 2013. "Working Capital Management and Shareholders' Wealth," Review of Finance, European Finance Association, vol. 17(5), pages 1827-1852.
    19. Jianakoplos, Nancy Ammon & Bernasek, Alexandra, 1998. "Are Women More Risk Averse?," Economic Inquiry, Western Economic Association International, vol. 36(4), pages 620-630, October.
    20. Ari, Anil & Chen, Sophia & Ratnovski, Lev, 2021. "The dynamics of non-performing loans during banking crises: A new database with post-COVID-19 implications," Journal of Banking & Finance, Elsevier, vol. 133(C).
    21. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    22. Tomasz Korol, 2018. "The Implementation of Fuzzy Logic in Forecasting Financial Ratios," Contemporary Economics, University of Economics and Human Sciences in Warsaw., vol. 12(2), June.
    23. French, Declan & Vigne, Samuel, 2019. "The causes and consequences of household financial strain: A systematic review," International Review of Financial Analysis, Elsevier, vol. 62(C), pages 150-156.
    24. Vicente García & Ana I. Marqués & J. Salvador Sánchez & Humberto J. Ochoa-Domínguez, 2019. "Dissimilarity-Based Linear Models for Corporate Bankruptcy Prediction," Computational Economics, Springer;Society for Computational Economics, vol. 53(3), pages 1019-1031, March.
    25. Bellotti, Tony & Crook, Jonathan, 2013. "Forecasting and stress testing credit card default using dynamic models," International Journal of Forecasting, Elsevier, vol. 29(4), pages 563-574.
    26. Diaz-Serrano, Luis, 2005. "Income volatility and residential mortgage delinquency across the EU," Journal of Housing Economics, Elsevier, vol. 14(3), pages 153-177, September.
    27. Aller, Carlos & Grant, Charles, 2018. "The effect of the financial crisis on default by Spanish households," Journal of Financial Stability, Elsevier, vol. 36(C), pages 39-52.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Khadija Ali Mohammed Abdulrahman Al Ameri, 2023. "Antecedents of Organizational Resilience after COVID-19: The Case of UAE," Sustainability, MDPI, vol. 15(7), pages 1-12, March.
    2. Piotrowski Dariusz, 2023. "Privacy frontiers in customers’ relations with banks," Economics and Business Review, Sciendo, vol. 9(1), pages 119-141, April.
    3. Beata Gavurova & Sylvia Jencova & Radovan Bacik & Marta Miskufova & Stanislav Letkovsky, 2022. "Artificial intelligence in predicting the bankruptcy of non-financial corporations," Oeconomia Copernicana, Institute of Economic Research, vol. 13(4), pages 1215-1251, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tomasz Korol, 2021. "Evaluation of the Macro- and Micro-Economic Factors Affecting the Financial Energy of Households," Energies, MDPI, vol. 14(12), pages 1-14, June.
    2. Tomasz Korol, 2021. "Examining Statistical Methods in Forecasting Financial Energy of Households in Poland and Taiwan," Energies, MDPI, vol. 14(7), pages 1-14, March.
    3. Hasan, Iftekhar & Politsidis, Panagiotis N. & Sharma, Zenu, 2021. "Global syndicated lending during the COVID-19 pandemic," Journal of Banking & Finance, Elsevier, vol. 133(C).
    4. Bill Francis & Iftekhar Hasan & Jong Chool Park & Qiang Wu, 2015. "Gender Differences in Financial Reporting Decision Making: Evidence from Accounting Conservatism," Contemporary Accounting Research, John Wiley & Sons, vol. 32(3), pages 1285-1318, September.
    5. repec:zbw:bofrdp:2014_001 is not listed on IDEAS
    6. Francis, Bill & Hasan, Iftekhar & Wu, Qiang & Park, Jong Chool, 2014. "Gender differences in financial reporting decision-making: Evidence from accounting conservatism," Bank of Finland Research Discussion Papers 1/2014, Bank of Finland.
    7. Enrico Supino & Nicola Piras, 2022. "Le performance dei modelli di credit scoring in contesti di forte instabilit? macroeconomica: il ruolo delle Reti Neurali Artificiali," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2022(2), pages 41-61.
    8. Ahsan Habib & Mabel D' Costa & Hedy Jiaying Huang & Md. Borhan Uddin Bhuiyan & Li Sun, 2020. "Determinants and consequences of financial distress: review of the empirical literature," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 60(S1), pages 1023-1075, April.
    9. Richard Chamboko & Jorge M. Bravo, 2016. "On the modelling of prognosis from delinquency to normal performance on retail consumer loans," Risk Management, Palgrave Macmillan, vol. 18(4), pages 264-287, December.
    10. Liang, Shangkun & Niu, Yuhao & Yang, Dan & Liu, Xuejuan, 2023. "Dividend payouts under a societal crisis: Financial constraints or signaling?," International Review of Financial Analysis, Elsevier, vol. 88(C).
    11. Yuan, Kunpeng & Chi, Guotai & Zhou, Ying & Yin, Hailei, 2022. "A novel two-stage hybrid default prediction model with k-means clustering and support vector domain description," Research in International Business and Finance, Elsevier, vol. 59(C).
    12. Tarek Ibrahim Eldomiaty & Mohamed Hashem Rashwan & Mohamed Bahaa El Din & Waleed Tayel, 2016. "Firm, industry and economic determinants of working capital at risk," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 3(04), pages 1-29, December.
    13. Salima Smiti & Makram Soui, 2020. "Bankruptcy Prediction Using Deep Learning Approach Based on Borderline SMOTE," Information Systems Frontiers, Springer, vol. 22(5), pages 1067-1083, October.
    14. Marco Locurcio & Francesco Tajani & Pierluigi Morano & Debora Anelli & Benedetto Manganelli, 2021. "Credit Risk Management of Property Investments through Multi-Criteria Indicators," Risks, MDPI, vol. 9(6), pages 1-23, June.
    15. Dumitrescu, Elena & Hué, Sullivan & Hurlin, Christophe & Tokpavi, Sessi, 2022. "Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects," European Journal of Operational Research, Elsevier, vol. 297(3), pages 1178-1192.
    16. Polina Popova, 2024. "COVID-19, Corporate Non-performing Loans, and Corporate Lending Dynamics: Evidence from Russian Regions," HSE Working papers WP BRP 93/FE/2024, National Research University Higher School of Economics.
    17. Angeliki Papana & Anastasia Spyridou, 2020. "Bankruptcy Prediction: The Case of the Greek Market," Forecasting, MDPI, vol. 2(4), pages 1-21, December.
    18. Bátiz-Zuk Enrique & Mohamed Abdulkadir & Sánchez-Cajal Fátima, 2021. "Exploring the sources of loan default clustering using survival analysis with frailty," Working Papers 2021-14, Banco de México.
    19. Douglas J. Cumming & Andrea Martinez-Salgueiro & Robert S. Reardon & Ahmed Sewaid, 2022. "COVID-19 bust, policy response, and rebound: equity crowdfunding and P2P versus banks," The Journal of Technology Transfer, Springer, vol. 47(6), pages 1825-1846, December.
    20. Abubakr Saeed & Syed Shafqat Mukarram & Yacine Belghitar, 2021. "Read between the lines: Board gender diversity, family ownership, and risk‐taking in Indian high‐tech firms," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 185-207, January.
    21. Elena Ivona DUMITRESCU & Sullivan HUE & Christophe HURLIN & Sessi TOKPAVI, 2020. "Machine Learning or Econometrics for Credit Scoring: Let’s Get the Best of Both Worlds," LEO Working Papers / DR LEO 2839, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.

    More about this item

    Keywords

    fuzzy logic; genetic algorithms; artificial neural networks; consumer bankruptcy; the financial crisis of households;
    All these keywords.

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G51 - Financial Economics - - Household Finance - - - Household Savings, Borrowing, Debt, and Wealth

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pes:ieroec:v:13:y:2022:i:2:p:407-438. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Adam P. Balcerzak (email available below). General contact details of provider: https://edirc.repec.org/data/ibgtopl.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.