A credit loan optimization scheme based on big data analysis under COVID-19

Zhu, X.

The sudden COVID-19 has forced many small and micro enterprises to obtain credit loans from banks to survive. The existing credit loan strategy research projects do not consider these sudden factors. From the perspective of banks, this article thoroughly considered the impact of COVID-19, credit loan risks, expected returns and obtained a credit loan optimization plan based on big data analysis. First, a credit risk indicator system, a Logistic credit risk quantification model, and a multi-target loan strategy model are established. Then based on the COVID-19 incident, the epidemic shock factor and the epidemic recovery factor were defined, and the degree of impact of different industries and enterprises of various sizes in the epidemic was quantified. Combined with the idea of random attacks, the model was closer to reality. We performed optimization analysis on actual lending strategies under different scenarios, such as pessimistic and optimistic assumptions about the impact of the epidemic. In particular, the scheme is easy to expand, and its ideas can be adapted to the optimization and adjustment of credit strategies under other emergent factors. © 2021 IEEE.