THE PROFITABILITY ANALYSIS OF FINTECH MICROLENDING: ADVANCED WHALE CURVE TOOLS APPLYING.

Fintech is actively expanding its activities in various directions in the modern financial system. One of these directions is the development of consumer lending, which forms an important competitive factor for banks and other traditional lenders. Lending models implemented by fintech companies have a number of fundamental differences from classic ones. The article is devoted to the study of the fintech microcredit model and the profitability analysis of this model based on the advanced Whale curve toolkit adapted to lending. In the article, the microcredit model is structured into three blocks, which include income generation, credit risk management systems, and borrower lead generation. Income generation is considered within the PDL (payday lending) approach. The methodological components of the application of the Whale curve toolkit for lending are justified. The first component outlines a holistic visualization of the relationship between risk and profitability of the credit portfolio of microcredit. The second component is the use of two approaches to the application of the Whale curve toolkit. The first approach is based on the choice of the basis of analysis of income from borrowers, and the second - on the choice of the basis of analysis of income from loans issued. The third component of the methodology was the segmentation of the loan portfolio into 4 segments: A, B, C, and D. It was done for both approaches. Segment A is characterized by the generation of high profitability for the creditor, segment B is close to a neutral level of profitability, and segments C and D are defined by a negative financial result of different levels. The analysis, based on the developed methodology, made it possible to identify a number of regularities between risk and profitability both in terms of segments A, B, C, and D and in terms of repeated loans. The analysis was conducted on the basis of data from several Ukrainian fintech companies for the 2nd and 3rd quarters of 2021. Within the methodological components, the analysis of income sensitivity based on the scenario approach was used in the work. A number of scenarios regarding changes in credit characteristics and risk management parameters were formed. On this basis, the sensitivity of income to these changes was modeled, and a comparative analysis of the results was carried out. The methodology proposed in the article makes it possible to implement an optimization analysis of fintech microcredit, to determine the relationship between credit risk and profitability, and to choose the optimal strategy for increasing the profitability of lending. [ABSTRACT FROM AUTHOR]