Analysis of Challenges and Strategies to Develop More Effective Early Warning System of Bank Bankruptcy

Document Type : Research Article

Authors

1 Department of Management & Economic, Rasht Branch, Islamic Azad University, Rasht, Iran

2 International Center for Development, Education, and Entrepreneurship, Amsterdam, Netherland

3 Department of Accounting, Rasht Branch, Islamic Azad University, Rasht, Iran

Abstract

Banks play an important role in the country's macroeconomics, they have a special importance in the economic pillars of the country, instability in macroeconomic policies, both in the supervision sector and in the real and financial sectors, makes banks as the last buffer against these shocks. Therefore, it is necessary to have an efficient warning system by benefiting from special economic forecasting tools in order to prevent their bankruptcy; This research tries to analyze the challenges and strategies of more effective development of the rapid warning system of banks' bankruptcy so that it can identify the existing obstacles and adopt appropriate approaches. The current research follows the post-positivist paradigm and in terms of its purpose, it is applied research, the results of which have been analyzed based on grounded theory. In the category of Early Warning System (EWS) development for bankruptcy, 11 main criteria and their various considerations were extracted; 10 key challenges and current problems and limitations were introduced and about 80 approaches and strategies were introduced for these 10 challenges, finally 13 main approaches were proposed to make the development of the banking bankruptcy warning system more effective.

Keywords

Main Subjects


Acharya, V. V., & Richardson, M. (2009). Causes of the financial crisis. Critical review, 21(2-3), 195-210.
Agarwal, S., Chomsisengphet, S., & Liu, C. (2011). Consumer bankruptcy and default: The role of individual social capital. Journal of Economic Psychology, 32(4), 632-650.
Ahmadian, A. and Heydari, H. (2016). Designing a quick warning system in the country's banking network, Policy notes report, Monetary and Banking Research Institute of the Central Bank of the Islamic Republic of Iran (MBRI-PN-95014), pp. 1-7.
Ahn, J. J., Oh, K. J., Kim, T. Y., & Kim, D. H. (2011). Usefulness of support vector machine to develop an early warning system for financial crisis. Expert Systems with Applications, 38(4), 2966-2973.
Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The journal of finance, 23(4), 589-609.
Altman, E. I., Iwanicz‐Drozdowska, M., Laitinen, E. K., & Suvas, A. (2017). Financial distress prediction in an international context: A review and empirical analysis of Altman's Z‐score model. Journal of International Financial Management & Accounting, 28(2), 131-171.
Altman, E., & Vosk, R. (2015). Universal dynamics and renormalization in many-body-localized systems. Annu. Rev. Condens. Matter Phys., 6(1), 383-409.
Backman, K., & Kyngäs, H. A. (1999). Challenges of the grounded theory approach to a novice researcher. Nursing & health sciences, 1(3), 147-153.
Barrell, R., Davis, E. P., Karim, D., & Liadze, I. (2010). Bank regulation, property prices and early warning systems for banking crises in OECD countries. Journal of Banking & Finance, 34(9), 2255-2264.
Basel Committee. (2013). Principles for effective risk data aggregation and risk reporting. Bank for International Settlements, 8.
Ben Lahouel, B., Taleb, L., Ben Zaied, Y., & Managi, S. (2022). Financial stability, liquidity risk and income diversification: evidence from European banks using the CAMELS–DEA approach. Annals of Operations Research, 1-32.
Borisov, N., & Diaz, C. (Eds.). (2021). Financial Cryptography and Data Security: 25th International Conference, FC 2021, Virtual Event, March 1–5, 2021, Revised Selected Papers, Part II (Vol. 12675). Springer Nature.
Casabianca, E. J., Catalano, M., Forni, L., Giarda, E., & Passeri, S. (2019). An early warning system for banking crises: From regression-based analysis to machine learning techniques. EconPapers. Orebro: Orebro University.
Chen, T. K., Liao, H. H., Chen, G. D., Kang, W. H., & Lin, Y. C. (2023). Bankruptcy Prediction Using Machine Learning Models with the Text-based Communicative Value of Annual Reports. Expert Systems with Applications, 120714.
Christofides, C., Eicher, T. S., & Papageorgiou, C. (2016). Did established Early Warning Signals predict the 2008 crises?. European Economic Review, 81, 103-114.
Claessens, M. S., & Kose, M. A. (2013). Financial crises explanations, types, and implications.
Cooper, R., Fleischer, A., & Cotton, F. A. (2012). Building Connections: An Interpretative Phenomenological Analysis of Qualitative Research Students' Learning Experiences. Qualitative Report, 17, 1.
Durango‐Gutiérrez, M. P., Lara‐Rubio, J., & Navarro‐Galera, A. (2023). Analysis of default risk in microfinance institutions under the Basel III framework. International Journal of Finance & Economics, 28(2), 1261-1278.
Fan, J., & Yao, Q. (2017). The elements of financial econometrics. Cambridge University Press.
Filippopoulou, C., Galariotis, E., & Spyrou, S. (2020). An early warning system for predicting systemic banking crises in the Eurozone: A logit regression approach. Journal of Economic Behavior & Organization, 172, 344-363.
Frankel, J., & Saravelos, G. (2012). Can leading indicators assess country vulnerability? Evidence from the 2008–09 global financial crisis. Journal of International Economics, 87(2), 216-231.
Ghosh, A., & Kapil, S. (2022). Is Altman’s Model efficient in predicting bankruptcy?–A comparison among the Altman Z-score, DEA, and ANN models. Journal of Information and Optimization Sciences, 43(6), 1191-1207.
Green, E. C. (2001). Can qualitative research produce reliable quantitative findings?. Field Methods, 13(1), 3-19.
Guerra, P., & Castelli, M. (2021). Machine learning applied to banking supervision a literature review. Risks, 9(7), 136.
Hastie, T., Tibshirani, R., Friedman, J. H., & Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction (Vol. 2, pp. 1-758). New York: springer.
Hull, J. (2012). Risk management and financial institutions,+ Web Site (Vol. 733). John Wiley & Sons.
Iturriaga, F. J. L., & Sanz, I. P. (2015). Bankruptcy visualization and prediction using neural networks: A study of US commercial banks. Expert Systems with applications, 42(6), 2857-2869.
Kaminsky, G., Lizondo, S., & Reinhart, C. M. (1998). Leading Indicators of Currency Crises. IMF Staff Papers, Palgrave Macmillan, 45(1), 1-48.
Kelman, I., & Glantz, M. H. (2014). Early warning systems defined. Reducing disaster: Early warning systems for climate change, 89-108.
Keshavarz Haddadha, H., Alipour, H., & Kheradyar, S. (2023). Designing an Optimal Model Regarding Early Warning System of Bankruptcy of Banks in Iran Application of Grounded Theory and Econometric Models. Environmental Energy and Economic Research, 7(2), 1-20.
Khan, S. N. (2014). Qualitative research method: Grounded theory. International journal of business and management, 9(11), 224-233.
Khoshnoud, Z., & Bultez, P. A. (2014). Low Statutory Power of the Central Bank of Islamic Republic of Iran. Journal of Money and Economy, 9(2), 23-61.
Kokabisaghi, F. (2018). Assessment of the effects of economic sanctions on Iranians’ right to health by using human rights impact assessment tool: a systematic review. International journal of health policy and management, 7(5), 374.
Laitinen, E. K., & Laitinen, T. (2000). Bankruptcy prediction: Application of the Taylor's expansion in logistic regression. International review of financial analysis, 9(4), 327-349.
Li, L., & Faff, R. (2019). Predicting corporate bankruptcy: What matters?. International Review of Economics & Finance, 62, 1-19.
Liu, L. X., Liu, S., & Sathye, M. (2021). Predicting bank failures: a synthesis of literature and directions for future research. Journal of Risk and Financial Management, 14(10), 474.
Lohmann, C., Möllenhoff, S., & Ohliger, T. (2022). Nonlinear relationships in bankruptcy prediction and their effect on the profitability of bankruptcy prediction models. Journal of Business Economics, 1-30.
McIntosh, M. J., & Morse, J. M. (2015). Situating and constructing diversity in semi-structured interviews. Global qualitative nursing research, 2, 2333393615597674.
Merton, R. C. (1977). An analytic derivation of the cost of deposit insurance and loan guarantees an application of modern option pricing theory. Journal of banking & finance, 1(1), 3-11.
Nori, H., Jenkins, S., Koch, P., & Caruana, R. (2019). Interpretml: A unified framework for machine learning interpretability. arXiv preprint arXiv:1909.09223.
Nyitrai, T., & Virág, M. (2019). The effects of handling outliers on the performance of bankruptcy prediction models. Socio-Economic Planning Sciences, 67, 34-42.
Onwuegbuzie, A. J., & Collins, K. M. (2007). A typology of mixed methods sampling designs in social science research. Qualitative report, 12(2), 281-316.
Padhan, R., & Prabheesh, K. P. (2019). Effectiveness of early warning models: A critical review and new agenda for future direction. Buletin Ekonomi Moneter Dan Perbankan, 22(4), 457-484.
Penman, S. H. (2010). Financial statement analysis and security valuation. New York: McGraw-Hill/Irwin.
Reinhart, C. M. (2022). From health crisis to financial distress. IMF Economic Review, 70(1), 4-31.
Saadatmand, M., & Daim, T. U. (2021). Technology Intelligence Map: Finance Machine Learning. Roadmapping Future: Technologies, Products and Services, 337-356.
Sahajwala, R., & Van den Bergh, P. (2000). Supervisory risk assessment and early warning systems. Basle Committee on Banking Supervision.
Sahiq, A. N. M., Ismail, S., Nor, S. H. S., Ul-Saufie, A. Z., & Yaacob, W. F. W. (2022). Application of Logistic Regression Model on Imbalanced Data in Personal Bankruptcy Prediction. In 2022 3rd International Conference on Artificial Intelligence and Data Sciences (AiDAS) (pp. 120-125). IEEE.
Salehi-Isfahani, D. (2011). Iranian youth in times of economic crisis. Iranian Studies, 44(6), 789-806.
Sami, B. J. (2014). Financial distress and bankruptcy costs. In Global Strategies in Banking and Finance. IGI Global.
Samitas, A., Kampouris, E., & Kenourgios, D. (2020). Machine learning as an early warning system to predict financial crisis. International Review of Financial Analysis, 71, 101507.
Schwarcz, S. L. (2019). Systematic Regulation of Systemic Risk. Wis. L. REv., 1.
Thanh, V. H., Ha, N. M., & Mcaleer, M. (2021). Asset investment diversification, bankruptcy risk and the mediating role of business diversification. Annals of Financial Economics, 16(01), 2150001.
Wang, P., Zong, L., & Ma, Y. (2020). An integrated early warning system for stock market turbulence. Expert Systems with Applications, 153, 113463.
White, M. J. (2007). Bankruptcy reform and credit cards. Journal of Economic Perspectives, 21(4), 175-199.