Model generation and health insurance claim settlement determination methods and devices, equipment and medium

Abstract

The embodiment of the invention discloses model generation and health insurance claim settlement determination methods and devices, equipment and a medium. The model generation method comprises the following steps: acquiring historical health insurance claim data of a historical health insurance claim case and classification data of whether the historical health insurance claim case is a historical health insurance claim fraud case, and taking the historical health insurance claim data and the classification data as a group of training samples, wherein the historical health insurance claim settlement data comprises historical insurance policy responsibility data, and the classification data comprises manual auditing data or actual claim refusing data for historical health insurance claim settlement cases; and training a to-be-trained machine learning model based on the multiple groups of training samples to obtain a classification model of health insurance claim settlement fraud cases. According to the technical scheme of the embodiment of the invention, the classification model for determining whether the health insurance claim settlement case is a health insurance claim settlement fraud case or not can be generated, namely, the classification model for automatically determining whether health insurance claim settlement is carried out or not.

Description

technical field [0001] The embodiments of the present invention relate to the field of data processing, and in particular to a method, device, device and medium for model generation and determination of health insurance claims. Background technique [0002] Health insurance can include the insurance that insurance companies pay insurance premiums for losses caused by health reasons through sickness insurance, medical insurance, disability income insurance, and nursing insurance. Finally, once the insured person causes economic losses due to health reasons, the insurance company needs to accept the claim application of the insured person and pay the insurance compensation. [0003] In current practice, it is mainly determined manually whether to make health insurance claims. Specifically, when the insured person submits a claim application, the insurance company will arrange a large number of claimants to review whether the claim application is normal (that is, whether it is...

AI Technical Summary

Problems solved by technology

Specifically, when the insured person submits a claim application, the insurance company will arrange a large number of claimants to review whether the claim application is normal (that is, whether it is fraudulent), this process is very cumbersome, and the accuracy of the review is very limited: up to 70% of claims are flagged as fraudulent, but only 10% of them are denied

Claims review takes up valuable manpower, time and other resources, but results are unsatisfactory

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