Modeling the dynamic patterns of banking and non-banking financial intermediaries’ performance


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Nowadays, there are many preconditions and circumstances for conducting shadow schemes in the financial market. Therefore, the level of risk of participation of bank and non-bank financial intermediaries in such schemes is assessed as high. The lack of a practical methodology for assessing the development trajectory of financial intermediaries raises the question of the need for preventive control and quality modeling of their growth dynamics. The study aims to identify and formalize the patterns of development paths of banking and non-banking financial intermediaries based on the Harrington desirability function, which will be used to identify risk patterns as indicative patterns of financial intermediaries’ participation in shadow schemes. The sample includes 13 banking institutions, 3 credit unions, 3 pawnshops, 3 insurance companies, and 3 financial companies. The obtained results showed the relationship between the financial intermediary risk level in terms of its participation in shadow schemes and the phases of the economic cycle as a catalyst for the economic dynamics of the formal and informal economy. Thus, in 2012–2015, most financial intermediaries were in the zone of most significant risk, especially banks, characterized by economic, social, and political instability. Today, banks are in the group with a controlled level of risk of participation in scheme operations. Over the years analyzed, a stable neutral level of risk of participation in shadow schemes was inherent in most non-bank financial institutions. They were less sensitive than banks to the phases of the economic cycle.

Alina Bukhtiarova and Yevgeniya Mordan gratefully acknowledge financial support from the Ministry of Education and Science of Ukraine (0120U100473, 0121U100469).

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    • Figure 1. Kohonen maps obtained
    • Figure 2. General Kohonen map
    • Figure 3. New Kohonen map with conditional financial intermediaries
    • Figure 4. Obtained Kohonen maps by groups of indicators, considering conditional financial intermediaries
    • Figure A1. Development patterns of financial intermediaries’ trajectories
    • Table 1. List of financial intermediaries included in the model as of January 1, 2021
    • Table 2. Description of input model variables
    • Table 3. Description of intermediate model variables
    • Table 4. Description of Pivdennyi Bank’s indicators as of January 1, 2021
    • Table 5. Synthesis function G for each group of indicators as of January 1, 2021
    • Table 6. Financial intermediaries included in pattern C1
    • Table 7. Distribution of points for cluster evaluation
    • Table 8. Cluster rank formation
    • Table 9. Cluster rating
    • Table 10. Assessment of financial intermediaries by groups within clusters
    • Table 11. Cluster rank formation
    • Table 12. Cluster ranking
    • Table 13. Assessment of conditional financial intermediaries by new groups within clusters
    • Table 14. Financial intermediaries of the newly formed pattern C7
    • Table 15. Financial intermediaries of the newly formed pattern C5
    • Table 16. A set of development patterns of financial intermediaries’ trajectories according to the probability of participation in shadow operations
    • Conceptualization
      Alina Bukhtiarova, Yevgeniya Mordan
    • Formal Analysis
      Alina Bukhtiarova, Viktoriia Kremen
    • Methodology
      Alina Bukhtiarova, Yevgeniya Mordan
    • Project administration
      Alina Bukhtiarova, Yevgen Balatskyi
    • Data curation
      Andrii Semenog, Viktoriia Kremen
    • Validation
      Andrii Semenog, Yevgen Balatskyi
    • Visualization
      Andrii Semenog, Viktoriia Kremen
    • Writing – original draft
      Andrii Semenog, Yevgeniya Mordan
    • Writing – review & editing
      Yevgeniya Mordan, Yevgen Balatskyi
    • Supervision
      Viktoriia Kremen, Yevgen Balatskyi