Insurance fraud identification data processing method, device, equipment and server

Abstract

An embodiment of the invention discloses an insurance fraud identification data processing method, device, equipment and server. Multi-scale relationship network graph data of crowds are built based on multi-scale relationship association data of insurance applicants and the insured, a relationship network among people can be more deeply mined, identification efficiency is improved, and identification range is widened. Besides, a supervised learning model is jointly built according to characteristic data of insurance fraud people and used for learning relationship network characteristics and characteristics of the insurance fraud people. Ganged insurance fraud people have obvious multi-scale relationship characteristics on the relationship network, the characteristics of the insurance fraud people frequently show up similarity, so that the insurance fraud people can be more effectively and efficiently identified by the method, and identification processing efficiency is improved.

Description

technical field [0001] The embodiment solution of this specification belongs to the technical field of computer data processing for insurance anti-fraud identification, and in particular relates to a data processing method, device, processing equipment and server for insurance fraud. Background technique [0002] Insurance is the financial, personal and other protection that can be enjoyed by paying the prescribed premium. With the economic development of the society and the improvement of people's insurance awareness, the demand for insurance business is also increasing. [0003] However, due to the certain economic leverage effect of insurance, a large number of fraudulent insurance behaviors appear in the market. These fraudulent persons usually deliberately create insurance events and obtain compensation from insurance companies accordingly. The current fraudulent insurance behavior tends to develop into a professional and team-based trend, which will have a very advers...

AI Technical Summary

Problems solved by technology

[0003] However, due to the certain economic leverage effect of insurance, a large number of fraudulent insurance behaviors appear in the market. These fraudulent persons usually deliberately create insurance incidents and obtain compensation from insurance companies accordingly.

The current fraudulent insurance behavior tends to develop into a professional and team-based trend, which will have a very adverse impact on the healthy development of the insurance industry and damage the interests of insurance companies and the public

At present, the traditional way of identifying fraudulent insurance mainly relies on manual use of some simple rules to identify historical fraudulent persons, and to predict whether there is a risk of fraudulent insurance based on the behavior of historical fraudulent persons

Due to the increasing concealment of fraudsters and groups, the existing method is not easy to quickly detect group crimes, and the workload of manual review is relatively large, and the identification efficiency is relatively low

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