Dynamic framework for strategic forecasting of the bank consumer loan market: Evidence from Ukraine

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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.

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    • 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
    • Conceptualization
      Andrii Kaminskyi, Nataliia Versal, Nataliia Prykaziuk
    • Data curation
      Andrii Kaminskyi, Oleksii Petrovskyi
    • Formal Analysis
      Andrii Kaminskyi, Oleksii Petrovskyi
    • Investigation
      Andrii Kaminskyi, Oleksii Petrovskyi
    • Methodology
      Andrii Kaminskyi, Oleksii Petrovskyi, Nataliia Prykaziuk
    • Supervision
      Andrii Kaminskyi, Nataliia Versal
    • Validation
      Andrii Kaminskyi, Oleksii Petrovskyi
    • Visualization
      Andrii Kaminskyi, Nataliia Versal, Oleksii Petrovskyi, Nataliia Prykaziuk
    • Writing – original draft
      Andrii Kaminskyi, Nataliia Versal, Oleksii Petrovskyi, Nataliia Prykaziuk
    • Writing – review & editing
      Andrii Kaminskyi, Nataliia Versal, Oleksii Petrovskyi, Nataliia Prykaziuk
    • Resources
      Oleksii Petrovskyi
    • Software
      Oleksii Petrovskyi