Model Analysis and Application of Quantum Computers in Credit Scoring Card Combination Optimization

In the context of business such as bank credit cards or related loans, banks score customers through credit scoring cards. Loan interest income and bad debt losses determined by the combination of thresholds of different credit scoring cards are key factors that determine the bank's final income. This paper mainly studies the combination optimization of credit scoring cards by establishing an optimization model and transforming it into a Quadratic Unconstrained Binary Optimization (QUBO) problem. First, the optimization problem of a single credit scoring card is studied, the data is preprocessed and zero-one integer programming model is established, then convert the objective function into a Hamiltonian to obtain an optimization model in the form of QUBO, and obtain the maximum bank income and the threshold of a single credit scoring card through the Simulated Annealing. Then, the credit scoring card combination optimization problem is studied. On the basis of the above research, variable substitution is used to reduce function degree, and a penalty function is introduced to obtain an unconstrained QUBO model for the combination problem. The D-Wave quantum annealing algorithm is used to obtain the optimal combination solution.