Optimization of credit scorecard combinations based on quantum annealing algorithm

Abstract

With the development of the times, the usage of loan-related business is increasing. However, most of the credit score card portfolio methods used nowadays are manually designed and supplemented with computer calculations, so the bank yields are low. Therefore, it is of great interest to study the application of quantum computers in credit score card portfolio optimization for better rating of customer credit ratings. Based on the given credit score card data, a linear programming model is developed. Firstly, the data is processed into two sets of one-dimensional data by Python and the working range of the two sets of data is found. Secondly, after deriving the objective function expressions according to the given conditions, the data were visualized by building a 3D grid matrix with the pass rate and bad debt rate as the XOY plane and the final benefit to the bank as the Z axis. Subsequently, the decision variables in the linear programming model are converted into binary variables, and the original model is transformed into a QUBO model by converting the constraints into binary variables. After analyzing the QUBO model, the quantum annealing method is chosen to solve the optimal solution of the objective function. And based on this, the case of three selected credit score card combinations and any three credit score cards are considered.