USER CREDIT ASSESSMENT
The present disclosure describes techniques for assessing user credit based on credit and propensity information for multiple types of services. One example method includes identifying a plurality of services associated with a credit scoring model corresponding to a type of each service; for each particular service in the plurality of services: determining a credit score of a particular user for the particular service according to the credit scoring model corresponding to the type of the particular service; determining a propensity score of the particular user for the particular service; and after determining credit scores and propensity scores of the particular for each particular service in the plurality of services, determining a comprehensive credit score of the particular user according to the propensity scores of the particular user for various types of services and the credit scores of the particular user for the types of services.
1 . A computer-implemented method comprising:
identifying a plurality of services, each of the plurality of services associated with a credit scoring model corresponding to a type of each service;
for each particular service in the plurality of services:
determining a credit score of a particular user for the particular service according to the credit scoring model corresponding to the type of the particular service, wherein the credit score is determined based on credit information about the particular user required by the credit scoring model corresponding to the type of the particular service;
determining a propensity score of the particular user for the particular service, the propensity score representing a degree of preference of the particular user for the type of the particular service, wherein the propensity score is determined based on propensity information about the particular user associated with the type of the particular service; and
after determining credit scores and propensity scores of the particular for each particular service in the plurality of services, determining a comprehensive credit score of the particular user according to the propensity scores of the particular user for various types of services and the credit scores of the particular user for the types of services.
2 . The method of claim 1 , wherein i is a natural number not greater than N, and N is the number of service types, Si is the credit score for a service numbered i, and Pi is the propensity score for the service numbered i, and the comprehensive credit score CS is calculated according to the formula CS=Σ i=1 N Pi×Si.
3 . The method of claim 1 , wherein calculating a comprehensive credit score of the particular user includes:
performing a weighted summation on the credit scores of the particular user for the types of services according to the propensity scores of the target user for the types of services, and determining a result of the weighted summation as the comprehensive credit score of the particular user.
4 . The method of claim 3 , wherein:
i is a natural number not greater than N, and N is the number of service types,
Si is the credit score for a service numbered i,
Pi is the propensity score for the service numbered i,
determining a comprehensive credit score of the particular user includes calculating a scoring weight Wi of the particular user for the particular service according to the propensity score Pi, wherein Wi is positively correlated with Pi, and the comprehensive credit score CS is calculated according to the formula CS=Σ i=1 N Wi×Si.
5 . The method of claim 4 , wherein calculating the scoring weight Wi of the particular user includes:
counting the number of users whose propensity scores for the type of the particular service are all Pi as a total number;
counting the number of users who have used the service in the users whose propensity scores are all Pi as a usage number; and
obtaining a scoring weight Wi of the particular user in the service by dividing the usage number by the total number.
6 . The method of claim 1 , wherein the credit information includes at least one of age, occupation, usual place of residence, or historical business information.
7 . The method of claim 1 , wherein each type of service is associated with a different credit scoring model.
8 . A non-transitory, computer-readable medium storing one or more instructions executable by a computer system to perform operations comprising:
identifying a plurality of services, each of the plurality of services associated with a credit scoring model corresponding to a type of each service;
for each particular service in the plurality of services:
determining a credit score of a particular user for the particular service according to the credit scoring model corresponding to the type of the particular service, wherein the credit score is determined based on credit information about the particular user required by the credit scoring model corresponding to the type of the particular service;
determining a propensity score of the particular user for the particular service, the propensity score representing a degree of preference of the particular user for the type of the particular service, wherein the propensity score is determined based on propensity information about the particular user associated with the type of the particular service; and
after determining credit scores and propensity scores of the particular for each particular service in the plurality of services, determining a comprehensive credit score of the particular user according to the propensity scores of the particular user for various types of services and the credit scores of the particular user for the types of services.
9 . The non-transitory, computer-readable medium of claim 8 , wherein i is a natural number not greater than N, and N is the number of service types, Si is the credit score for a service numbered i, and Pi is the propensity score for the service numbered i, and the comprehensive credit score CS is calculated according to the formula CS=Σ i=1 N Pi×Si.
10 . The non-transitory, computer-readable medium of claim 8 , wherein calculating a comprehensive credit score of the particular user includes:
performing a weighted summation on the credit scores of the particular user for the types of services according to the propensity scores of the target user for the types of services, and determining a result of the weighted summation as the comprehensive credit score of the particular user.
11 . The non-transitory, computer-readable medium of claim 10 , wherein:
i is a natural number not greater than N, and N is the number of service types,
Si is the credit score for a service numbered i,
Pi is the propensity score for the service numbered i,
determining a comprehensive credit score of the particular user includes calculating a scoring weight Wi of the particular user for the particular service according to the propensity score Pi, wherein Wi is positively correlated with Pi, and the comprehensive credit score CS is calculated according to the formula CS=Σ i=1 N Wi×Si.
12 . The non-transitory, computer-readable medium of claim 11 , wherein calculating the scoring weight Wi of the particular user includes:
counting the number of users whose propensity scores for the type of the particular service are all Pi as a total number;
counting the number of users who have used the service in the users whose propensity scores are all Pi as a usage number; and
obtaining a scoring weight Wi of the particular user in the service by dividing the usage number by the total number.
13 . The non-transitory, computer-readable medium of claim 8 , wherein the credit information includes at least one of age, occupation, usual place of residence, or historical business information.
14 . The non-transitory, computer-readable medium of claim 8 , wherein each type of service is associated with a different credit scoring model.
15 . A computer-implemented system, comprising:
one or more computers; and
one or more computer memory devices interoperably coupled with the one or more computers and having tangible, non-transitory, machine-readable media storing one or more instructions that, when executed by the one or more computers, perform one or more operations comprising:
identifying a plurality of services, each of the plurality of services associated with a credit scoring model corresponding to a type of each service;
for each particular service in the plurality of services:
determining a credit score of a particular user for the particular service according to the credit scoring model corresponding to the type of the particular service, wherein the credit score is determined based on credit information about the particular user required by the credit scoring model corresponding to the type of the particular service;
determining a propensity score of the particular user for the particular service, the propensity score representing a degree of preference of the particular user for the type of the particular service, wherein the propensity score is determined based on propensity information about the particular user associated with the type of the particular service; and
after determining credit scores and propensity scores of the particular for each particular service in the plurality of services, determining a comprehensive credit score of the particular user according to the propensity scores of the particular user for various types of services and the credit scores of the particular user for the types of services.
16 . The computer-implemented system of claim 15 , wherein i is a natural number not greater than N, and N is the number of service types, Si is the credit score for a service numbered i, and Pi is the propensity score for the service numbered i, and the comprehensive credit score CS is calculated according to the formula CS=Σ i=1 N Pi×Si.
17 . The computer-implemented system of claim 15 , wherein calculating a comprehensive credit score of the particular user includes:
performing a weighted summation on the credit scores of the particular user for the types of services according to the propensity scores of the target user for the types of services, and determining a result of the weighted summation as the comprehensive credit score of the particular user.
18 . The computer-implemented system of claim 17 , wherein:
i is a natural number not greater than N, and N is the number of service types,
Si is the credit score for a service numbered i,
Pi is the propensity score for the service numbered i,
determining a comprehensive credit score of the particular user includes calculating a scoring weight Wi of the particular user for the particular service according to the propensity score Pi, wherein Wi is positively correlated with Pi, and the comprehensive credit score CS is calculated according to the formula CS=Σ i=1 N Wi×Si.
19 . The computer-implemented system of claim 18 , wherein calculating the scoring weight Wi of the particular user includes:
counting the number of users whose propensity scores for the type of the particular service are all Pi as a total number;
counting the number of users who have used the service in the users whose propensity scores are all Pi as a usage number; and
obtaining a scoring weight Wi of the particular user in the service by dividing the usage number by the total number.
20 . The computer-implemented system of claim 15 , wherein the credit information includes at least one of age, occupation, usual place of residence, or historical business information.