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In the credit loan practices of lending platforms, there is a mismatch problem between borrowers’ credit scoring and the probability of default (PD), which cannot provide a basis for the credit loan decision. Firstly, the  Wald  test is used to select the single indicator with a strong default identification ability, and Lasso-Logistic regression is used to determine the optimal combination of indicators with the overall default identification ability to construct the credit scoring indicator system. Secondly, by randomly dividing non-default samples, this article forms multiple groups of balanced samples with default samples to perform Lasso-Logistic regression, and multiple groups of Lasso-Logistic regression coefficients and expert opinion are used to determine the optimal indicator weight and calculate the credit score. The empirical work is developed through 43,471 and 24,153 samples of Lending Club. The results show that the proposed credit scoring method is effective. The credit scoring method proposed in this paper solves the mismatch problem between credit scoring and PD in credit loan decision-making practice. The findings of this paper provide references for assisting credit loan decision-making, reducing bankers’ investment risks, and alleviating borrowers’ financing problems.

Research on credit scoring method matching the probability of default: evidence from Lending Club