A method and device for identifying insurance fraud

By analyzing vehicle status data from multiple dimensions, an insurance fraud identification model is constructed, which solves the problems of misjudgment caused by single data and manual review in existing technologies, and realizes accurate identification and automated processing of vehicle insurance fraud.

CN122390881APending Publication Date: 2026-07-14GREAT WALL MOTOR CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
GREAT WALL MOTOR CO LTD
Filing Date
2026-04-27
Publication Date
2026-07-14

AI Technical Summary

Technical Problem

When dealing with vehicle insurance fraud, current technologies mainly rely on manual review of repair materials and on-site inspections, resulting in a single data source, insufficient verification dimensions, inconsistent judgment standards, and a high rate of missed or false judgments.

Method used

By acquiring vehicle status data, including repair work orders, vehicle network operation data, and customer feedback data, and analyzing engine status parameters such as repeat repair rate, fuel economy degradation index, idle stability deterioration index, and stall frequency increase, a multi-dimensional insurance fraud identification model is constructed to generate a comprehensive fraud risk score.

Benefits of technology

It enables accurate identification of insurance fraud involving "repair instead of replacement" of engines, improves the efficiency and accuracy of claims risk control, reduces the error of subjective human judgment, and supports automated identification and rapid screening of batch claims.

✦ Generated by Eureka AI based on patent content.

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Abstract

The application discloses a method and device for identifying insurance fraud. One or more vehicle state data related to the reported target engine replacement is obtained, the real running state of the vehicle is collected, and the completeness and objectivity of the identification basis are improved. Then, the state parameters are quantified based on the vehicle state data, the objective identification dimension is constructed, the artificial subjective judgment error is reduced, and the fraud identification accuracy is improved. Based on the objective state parameters, the insurance fraud identification result can be automatically generated without manual item-by-item verification, which greatly improves the claim case screening efficiency. With real running data as the core basis, it can accurately determine whether the engine has "repair instead of replacement" behavior, effectively strengthen the claim risk control ability, and protect the legal rights and interests of both the insurance company and the vehicle owner.
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