Bank Credit Score Card Selection and Threshold Determination Based on Quantum Annealing Algorithm and Genetic Algorithm

With the progress of the times, influenced by the rapid development of Internet finance and financial technology, the demand for lending is gradually increasing, and the lending services provided by banks are also gradually increasing, and the calculation of bank loan income is more cumbersome. Therefore, in practice, banks tend to assess the credit of their customers through credit scorecards. The assessment is divided into two types: single credit card assessment and combined credit card assessment. Today's society is a highly centralized information society, quantum computers reflect great potential in computing, storage space quantum bits is 2n times than classical bits, and quantum computing can simultaneously calculate multiple numbers at the same time, in various scenarios graph coloring, traveler problem, vehicle path optimization problem, all reflect the great advantages of quantum computing. To this end, this paper takes the selection and threshold determination of bank scorecards as the research object, establishes the QUBO model for scorecard selection and threshold determination considering single scorecard and combined score cards respectively, and solves the model based on quantum annealing algorithm and genetic algorithm respectively. The case validation shows that the model developed and the applied solution method can effectively find the optimal single and combination scorecards.