If the comprehensive score is not enough, you can go to the bank to apply for a loan, or you can apply for several online loans, but the pass rate is very low. You are not advised to try. Borrowing users should understand the reasons for insufficient comprehensive score and solve the problems before applying for loans. Generally, the reasons for insufficient comprehensive score are too many times of personal credit inquiry, too many debts exceeding the borrower's repayment ability, poor credit record of credit inquiry and untrue personal information. I suggest you find your own reasons to improve before applying for a loan, otherwise your comprehensive score will only be worse. After the improvement, we can understand the following formal platforms with lower requirements.

1. Personal comprehensive credit score refers to the comprehensive investigation of the internal and external subjective and objective environment that affects individuals and their families by using scientific and rigorous analysis methods, and the comprehensive judgment and evaluation of their ability to fulfill various economic commitments. According to different applications, credit score can be divided into risk score, income score, responsiveness score, customer churn (loyalty) score, collection score, credit card issuance audit score, mortgage loan issuance audit score, credit line approval score and so on.

2. Comprehensive credit risk score-Pengyuan 800. At the end of April 2005, Pengyuanzheng Letter Co., Ltd. independently developed a personal comprehensive credit risk score-"Pengyuan 800", which officially provided credit rating inquiry service for credit reporting institutions and individuals. "Pengyuan 800" makes a statistical analysis of personal credit information by establishing a mathematical model, predicts the possibility of future default risk, and comprehensively reflects personal credit status with a score. The credit scoring system has six grades, ranging from 320 to 800. Every 80 grades, the personal credit status is quantified in detail, and each score corresponds to a default probability. The higher the score, the lower the risk of default. Graphical representation of rating and default probability: minimum score 320 400 480 560 640 720 800 maximum score +F+E+D+C+B+A+.

3. The scoring model selects more than forty variables related to personal credit, which are divided into four categories: personal basic information, bank credit information, personal payment information and personal fund status. Among them, the weight of bank credit information is the largest, close to 50%, and the other three categories are roughly equal. At present, only 25% of the total population have bank credit records in the database of the credit information system. Because bank credit information is the most important variable affecting personal credit status, for customers without bank credit records, the model selects other variables related to bank credit instead. In the future, with the gradual improvement of data, we will add more variables to the model to continuously improve the accuracy, precision and universality of the model.