User credit line prediction method and device and related equipment

Abstract

The invention provides a user credit line prediction method and device, and related equipment. The method comprises the steps of obtaining user attribute data of a target user and multiple pieces of asset data in a preset time period, wherein each piece of asset data corresponds to a timestamp in a preset time period, and the asset data represents manageable assets of the target user in the corresponding timestamp; using the asset data to construct an asset function reflecting a function relationship between manageable assets and time of the target user, and using a line prediction model to process the asset function of the target user and the user attribute data to obtain a credit limit prediction value serving as a basis for handling a credit service of the target user, wherein the lineprediction model is a semi-function type partial linear regression model constructed based on sample data of a plurality of historical users. The line prediction model can directly process an asset function of the user, so that the change trend of the asset condition of the user is combined to predict the credit limit, and therefore, the scheme can obtain a more accurate credit limit prediction value.

Description

technical field [0001] The invention relates to the technical field of intelligent decision-making, in particular to a method, device and related equipment for predicting user credit line. Background technique [0002] Credit business to individual users is an important business carried out by commercial banks, such as credit card business, personal loan business, etc. can be considered as a kind of credit business. When a user requests a credit business, the bank needs to determine the user's credit limit based on the user's relevant data. For example, if the user applies for a credit card, the bank needs to determine the consumption limit of the credit card issued to the user. [0003] At present, the method of determining the credit line of the user is mainly to evaluate the user's current asset situation (for example, the user's income) and credit records, and determine the credit line according to the evaluation result. For example, configure a lower credit line for us...

AI Technical Summary

Problems solved by technology

[0004] However, the user's asset situation will change over time, and only evaluating the user's current asset situation cannot accurately reflect the user's real repayment ability and consumption needs within a period of time after obtaining the corresponding credit line. That is to say, the existing definite credit line The accuracy of the method is low, and it cannot accurately meet the needs of users and banks

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