Claim settlement customer risk identification method and system

Abstract

The invention relates to a claim settlement customer risk identification method and system. The method comprises a data preparation stage, a model training stage and a model implementation stage. Thedata preparation stage comprises the following steps of obtaining data related to a claim settlement customer risk from medical claim settlement big data, wherein the data comprises personal information and doctor-seeing information of a claim settlement customer, and the doctor-seeing information includes doctor-seeing expense information; and extracting characteristics required for training a GBM model from the data to form a data set. The model training stage comprises the following steps of taking part of the data in the data set as a training set to be input to a trainer of the GBM modelfor performing training; and generating a trained GBM model. The model implementation stage comprises the following steps of collecting current claim settlement data of the claim settlement customer,and extracting the characteristics required by the GBM model; and inputting the characteristics to the GBM model to generate an identification result, wherein the identification result comprises a doctor-seeing expense possibly generated in the future by the claim settlement customer.

Description

technical field [0001] The invention relates to the technical field of insurance services based on Internet applications, in particular to a risk identification method and system for claims settlement customers. Background technique [0002] At present, the pricing method of health insurance products is relatively simple, mainly relying on industry benchmarking, reinsurance quotations, combined with simple macro risk analysis. This method cannot effectively reflect the risk characteristics of different groups of people and cannot support differentiated pricing. [0003] In addition, there are a considerable number of medical insurance frauds at present, and the existing technology lacks effective scientific identification methods for medical insurance fraud, which seriously affects the balance of income and expenditure of medical insurance funds, and violates the interests of the majority of insured people and even the public interest. [0004] Therefore, in order to achiev...

AI Technical Summary

Problems solved by technology

This method cannot effectively reflect the risk characteristics of different groups of people and cannot support differentiated pricing

[0003] In addition, there are currently a considerable amount of medical insurance fraud, and the existing technology lacks effective scientific identification methods for medical insurance fraud, which seriously affects the balance of income and expenditure of the medical insurance fund, and violates the interests of the majority of insured people and even the public interest

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