Dynamic framework for strategic forecasting of the bank consumer loan market: Evidence from Ukraine
-
DOIhttp://dx.doi.org/10.21511/bbs.18(3).2023.08
-
Article InfoVolume 18 2023, Issue #3, pp. 87-100
- Cited by
- 375 Views
-
152 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
Accurate forecasting of consumer loan market behavior gives banks a huge potential to optimize their credit strategies by proactively adapting to external changes. This study aims to analyze and predict consumer loan demand, supply, and profitability in the Ukrainian banking sector. Using a systemic dynamic approach, the interplay of five key factors is considered: central bank policies, GDP fluctuations, changing competitive landscape driven by FinTech companies, investment in government bonds as an alternative to loan granting, and severity of credit risk management.
The developed dynamic model for the bank consumer loan market in Ukraine offers predictive capabilities enhancing decision-making and strategic planning in the banking sector and can be adapted in open small economies. Within the proposed systemic dynamic model, five scenarios were explored. Compared to the base scenario, a 4 p.p. increase in the key policy rate results in UAH 4.7 billion decrease in demand for bank consumer loans and a UAH 0.55 billion reduction in lending profitability based on the year’s results. Fall in GDP by 6 p.p. leads to a decrease in the supply of bank consumer loans by UAH 6.9 billion and a decrease in lending income by UAH 1.3 billion based on the year’s results. Scenario with the decline of FinTech portfolio by 20 p.p. quarterly leads to an increase in demand for bank consumer loans of UAH 8 billion. A 4 p.p. rise in government bond yields leads to a UAH 17 billion reduction in the supply of consumer loans in the same quarter.
- Keywords
-
JEL Classification (Paper profile tab)G21, E44, E47
-
References53
-
Tables3
-
Figures10
-
- Figure 1. Causal loop diagram of MBCL
- Figure 2. Stock and flow SD diagram of the model
- Figure 3. Consumer loan portfolio outstanding: comparison of historical data and simulation within the SD model
- Figure 4. Portfolio of government bonds: comparison of historical data and simulation within the SD model
- Figure 5. Equity capital: comparison of historical data and simulation within the SD model
- Figure 6. Reserves/write-offs: comparison of historical data and simulation within the SD model
- Figure 7. Scenario analysis demand for new bank consumer loans
- Figure 8. Scenario analysis of supply of new consumer loans by banks
- Figure 9. Scenario analysis of consumer loan portfolio outstanding
- Figure 10. Scenario analysis of profit from bank consumer lending
-
- Table 1. The impact of external factors on model parameters: in total
- Table 2. Equations of the main variables of the model
- Table 3. Data for scenario analysis
-
- Abuka, C., Alinda, R. K., Minoiu, C., Peydró, J. L., & Presbitero, A. F. (2019). Monetary policy and bank lending in developing countries: Loan applications, rates, and real effects. Journal of Development Economics, 139, 185-202.
- Aikman, D., Bush, O., & Taylor, A. (2018). Monetary versus macroprudential policies: causal impacts of interest rates and credit controls in the era of the UK Radcliffe Report (NBER Working Paper No. 22380).
- Beck, R., Jakubik, P., & Piloiu, A. (2013). Non-performing loans What matters in addition to the economic cycle? (ECB Working Paper No. 1515). European Central Bank.
- Beck, T., Berrak, B., Rioja, K., & Valev, N. (2012). Who Gets the Credit? And Does It Matter? Household vs. Firm Lending across Countries. Journal of Macroeconomics, 12(1), 1-46.
- Board of Governors of the Federal Reserve System. (2023). Consumer credit.
- Bossel, H. (2007). Systems and Models: Complexity, Dynamics, Evolution, Sustainability. Books on Demand.
- Bouis, R. (2019). Banks’ Holdings of Government Securities and Credit to the Private Sector in Emerging Market and Developing Economies (IMF Working Paper. No WP/19/224).
- Brasliņš, G., Orlovs, A., Braukša, I., & Bulis, A. (2022). GDP and lending behaviour: empirical evidence for Baltic states economies. Regional Formation and Development Studies, 10(2), 31-45.
- Breeden, J., & Canals-Cerdá, J. (2018). Consumer risk appetite, the credit cycle and the housing bubble. The Journal of Credit Risk, 14(2), 45-74.
- Buchak, G., Matvos, G., Pikorski, T., & Seru, A. (2017). FinTech, regulatory arbitrage, and the rise of shadow banking (Research Paper No. 17-39). Columbia Business School.
- Calza, A., Gartner, C., & Sousa, J. M. (2001). Modelling the Demand for Loans to the Private Sector in the Euro Area. Applied Economics, 35(1), 107-117.
- Coletta, M., De Bonis, R., & Piermattei, S. (2014). The determinants of household debt: a cross-country analysis (Temi di Discussione Working Paper No. 989). Bank of Italy.
- Cornelli, G., Frost, J., Gambacorta, L., Rau, P. R., Wardrop, R., & Ziegler, T. (2023). Fintech and big tech credit: Drivers of the growth of digital lending. Journal of Banking & Finance, 148, 106742.
- Diette, M. D. (2000). How do lenders set interest rates on loans? Federal Reserve Bank of Minneapolis.
- Durkin, T., Elliehausen, G., Staten, M., & Zywicki, T. (Eds.). (2014). Introduction and Overview of Consumer Credit: Development, Uses, Kinds, and Policy Issues. In Consumer Credit and the American Economy (pp. 1-3). Oxford University Press.
- European Central Bank (ECB). (2023). Loans to households and non-financial corporations.
- FICO. (2019). Homecourt Disadvantage: Truncation Bias and the Art of Comparing Consumer Credit Scoring Models.
- Forrester, J. W. (2003). Economic theory for the new millennium. System Dynamics Review, 29(1), 26-41.
- Gennaioli, N., Martin, A., & Rossi, S. (2018). Banks, government Bonds, and Default: What do the data Say? Journal of Monetary Economics, 98, 98-113.
- Hahn, L. A., & Hagemann, H. (2015). Economic Theory of Bank Credit. Oxford University Press.
- Harvard Business Review Analytic Service. (2019). In the Game: Traditional Financial Institutions Embrace Fintech Disruption.
- Hill, J. (2018). Fintech and the Remaking of Financial Institutions. Academic Press.
- Holló, D. (2010). Estimating Price Elasticities on the Hungarian Consumer Lending and Deposit Markets: Demand Effects and Their Possible Consequences. Focus on European Economic Integration, 1, 73-89.
- Hong Kong Institute of Bankers (HKIB). (2012). Bank Lending. Wiley.
- Hughes, J. P., Jagtiani, J., & Moon, C. G. (2022). Consumer lending efficiency: commercial banks versus a fintech lender. Financial Innovation, 8, 38.
- Hunt, С. (2015). Economic implications of high and rising household indebtedness. Reserve Bank of New Zealand Bulletin, 78(1).
- IMF. (2017). Chapter 2: Household Debt and Financial Stability. In Global Financial Stability Report, October 2017: Is Growth at Risk? Monetary and Financial Systems Dept.
- Ishtiaq, M. (2015). Risk Management in Banks: Determination of Practices and Relationship with Performance (Ph.D. Thesis). University of Bedfordshire, Bedfordshire.
- Kaminskyi, A., & Petrovskyi, O. (2019). Consumer Lending in Banks: System Dynamics Modelling. Scientific Papers NaUKMA. Economics, 4(1), 48-53.
- Kaminskyi, A., & Versal, N. (2018). Risk Management of Dollarization in Banking: Case of Post-Soviet Countries. Montenegrin Journal of Economics, 14(2), 21-40.
- Kashif, M., Iftikhar, S. F., & Iftikhar, K. (2016). Loan growth and bank solvency: evidence from the Pakistani banking sector. Financial Innovation, 2(1), 22.
- Klein, N. (2013). Non-Performing Loans in CESEE: Determinants and Impact on Macroeconomic Performance (IMF Working Paper No. WP/13/72).
- Kondova, G. & Bandyopadhyay, T. (2019). The Impact of Non-Bank Lending on Bank Efficiency: Data Envelopment Analysis of European Banks. International Journal of Trade, Economics and Finance, 10(5), 108-112.
- Kupčinskas, K., & Paškevičius, A. (2017). Key factors of non-performing loans in Baltic and Scandinavian countries: lessons learned in the last decade. Ekonomika, 96(2), 43-55.
- Laryea, E., Ntow-Gyamfi, M., & Alu, A. A. (2016). Nonperforming loans and bank profitability: evidence from an emerging market. African Journal of Economic and Management Studies, 7(4), 462-481.
- Luhmann, N. (2012). Introduction to Systems Theory. Wiley.
- Lukianenko, I., & Faryna, O. (2016). Macrofinancial stability: models and assessment methods. (In Ukrainian).
- Michlitsch, K. (2020). Fintech Continues to Disrupt Consumer Lending. Morgan Stanley Investment Management.
- Minsky, H. (1986). Stabilizing an Unstable Economy. Yale University Press, New Haven, Connecticut.
- Mishra, P., Montiel, P., Pedroni, P., & Spilimbergo, A. (2014). Monetary policy and bank lending rates in low-income countries: Heterogeneous panel estimates. Journal of Development Economics, 111, 117-131.
- National Bank of Ukraine (NBU). (2023). Financial Sector Statistics.
- Onyiriuba, L. (2016). Emerging Market Bank Lending and Credit Risk Control: Evolving Strategies to Mitigate Credit Risk, Optimize Lending Portfolios, and Check Delinquent Loans. Academic Press.
- Phan, D. H. B., Narayan, P. K., Rahman, R. E., & Hutabarat, A. R. (2020). Do financial technology firms influence bank performance? Pacific-Basin Finance Journal, 62, 101210.
- Radzicki, M. J. (1990). Methodologia oeconomiae et systematis dynamis. System Dynamics Review, 6(2), 123-147.
- Radzicki, M. J. (2009). System Dynamics and Its Contribution to Economics and Economic Modeling. In Robert A. Meyer (Ed.), Complex Systems in Finance and Econometrics (pp. 727-737).
- Rinaldi, L., & Sanchis-Arellano, A. (2006). Household debt sustainability: what explains household non-performing loans? An empirical analysis (ECB Working Paper No. 570).
- Stein, R. M. (2005). The relationship between default prediction and lending profits: Integrating ROC analysis and loan pricing. Journal of Banking & Finance, 29(5), 1213-1236.
- Sterman, J. D. (2001). System Dynamics Modeling: Tools for Learning in a Complex World. California Management Review, 43(4), 8-25.
- Sterman, J. D. (2002). Business Dynamics: System Thinking and Modeling for a Complex World. Massachusetts Institute of Technology: Cambridge, MA, USA.
- Szarowska, I. (2018). Effect of macroeconomic determinants on non-performing loans in Central and Eastern European countries. International Journal of Monetary Economics and Finance, 11(1), 20.
- Versal, N., Erastov, E., Balytska, M., & Honchar, I. (2022). Digitalization Index: Case for Banking System. Statistika: Statistics and Economy Journal, 102(4), 426-443.
- Wheat, D. (2007). The feedback method. A system dynamics approach to teaching macroeconomics (Doctoral Thesis). University of Bergen, Bergen, Norway.
- Zheng, C., Bhowmik, P. K., & Sarker, N. (2019). Industry-Specific and Macroeconomic Determinants of Non-Performing Loans: A Comparative Analysis of ARDL and VECM. Sustainability, 12(1), 325.