User characteristic data generation method and device and electronic equipment

Abstract

The invention relates to a user characteristic data generation method and device, electronic equipment and a computer readable medium. The method comprises the steps of obtaining a terminal application list of a user, wherein the terminal application list comprises installed application information; respectively inputting the application information in the terminal application list into a first word vector model and a second word vector model to generate a plurality of first word vectors and a plurality of second word vectors; performing information fusion on the plurality of first word vectors and the plurality of second word vectors to generate word vector information of the user; and generating feature data of the user through the word vector information. According to the user characteristic data generation method and device, the electronic equipment and the computer readable medium, the characteristics of the user can be accurately analyzed from multiple dimensions, the data for accurately describing the characteristics of the user is generated, and more comprehensive risk analysis can be carried out on the user through the user characteristic data.

Description

technical field [0001] The present disclosure relates to the field of computer information processing, and in particular, to a method and device for generating user characteristic data, electronic equipment, and a computer-readable medium. Background technique [0002] Individual users or corporate users often carry out borrowing activities by financial service institutions. For financial service institutions, users' borrowing activities are likely to bring risks to financial service companies. At present, the identification of financial risks is often obtained by analyzing the user's basic information and behavior information. For example, the basic information may include the user's age, gender, occupation, region, etc., and the behavior information may include the user's loan information, Repayment information, default information, etc. How to dig out more information that can reflect a certain aspect of the user's characteristics, so as to conduct a more comprehensive a...

AI Technical Summary

Problems solved by technology

[0003] Existing technology’s risk perception of app information is mostly focused on client’s app classification information and manual experience. Every time a new case occurs, it needs to be checked by the reviewing colleagues after demarcating the scope

The result of this is excessive use of manpower, and manpower may be overtired and cause errors

The use of traditional statistical models relies on manual experience, requiring detailed analysis of user app information and expression of abnormal information, which is time-consuming and labor-intensive

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