Author(s)

Yiyang Chen

Corresponding Author

Yiyang Chen

ABSTRACT

After the 21st century, China's small and micro enterprises have sprung up like mushrooms. Due to their small size, they often do not have fixed assets to mortgage loans, so they can only apply for credit loans with high risks for banks. Based on the risk assessment score of enterprises, this paper puts forward an objective function based on the expected return of credit business of commercial banks. Because the number of independent variables is too large, this paper proposes a solution method based on genetic algorithm, which converts a large number of data into binary in order and connects them head to tail to obtain gene sequence. When the population size is 10 and the number of iterations is 1000, the convergence effect is good. By decomposing the optimized solution and reversing code according to the rules, the optimal lending strategy for 123 enterprises is obtained. Considering that the total amount of capital is fixed, if the optimal lending strategy exceeds the total amount, it will be deleted from the enterprises whose adaptive function value is the smallest and the credit rating is d until the total amount requirement is met.

KEYWORDS

Credit decision, genetic algorithm, iteration