The problem of personal credit fault poses a severe challenge to the stability of the financial system and the quality of personal economic life. With the globalization of the economy and the complications of the financial market, the impact of personal credit fault has extended to the stability and sustainable development of the whole society. The influencing factors of personal credit fault include the borrower’s income level, professional status, family financial status, and personal consumption habits, while the macroeconomic situation, industry cycle, inflation rate and interest rate level also have an impact on repayment ability. Large-scale default may lead to the deterioration of the asset quality of financial institutions, increase financial risks, and curb economic investment and consumer demand. In addition, personal credit fault may aggravate the gap between the rich and the poor in society. It is very important for the traditional factor model to choose appropriate factors in personal credit fault prediction, but its interpretation is often challenged. Therefore, this paper improves the traditional logistic regression method and improves the fitting degree and generalization ability of the model by adding linear layers and applying the Dropout strategy. In this paper, a logical prediction method based on machine learning is proposed, and the problem of personal credit fault is deeply studied, which improves the accuracy and reliability of prediction. Through data analysis and experimental verification, the important patterns and potential relationships are revealed, and the characteristics that significantly affect the prediction are screened out, which improves the model performance and interpretation. The research results of this paper have practical guiding significance and show the superior performance of the proposed method in practical application.