Intelligent claim auditing auxiliary method and system based on big data analysis and processing

Abstract

The invention relates to an intelligent claim auditing auxiliary method and system based on big data analysis and processing. The method comprises the following steps: 1, when an insurer makes a claimsettlement request for a specific item, obtaining the expenses incurred by the insurer on the specific item and predicting the future expenses; 2, summing up the cost incurred and the cost incurred in the future to obtain the total cost of the specific project; 3, based on the statistical data of the project, the cost interval of the total cost is obtained; 4, according to the expense interval ofthe total expense, the reasonable index of the total expense is obtained; 5, if that rationality index exceed a predetermined threshold, the claim settlement request is judged as a high-risk request,and background intervention is required. The present disclosure is based on big data processing and analysis, adopts advanced statistical model and analysis algorithm, provides cost rationality assessment and fraud risk assessment for case claim review, improves claim automation rate, and reduces unreasonable claim cost.

Description

technical field [0001] The present invention relates to the technical field of Internet services, and in particular to an intelligent claim settlement review assistance method and system based on big data analysis and processing. Background technique [0002] The current claim settlement system is mainly for reviewers to extract the claimant information from the materials of the claim case, and then calculate the payment amount and payment plan based on the claim settlement rules and the judgment of the reviewer. Therefore, the current claims review mainly relies on fixed claims rules and the professional competence of reviewers, and there is no national claims review conclusion and payment benchmark data for reference. [0003] At present, there are a considerable number of medical insurance frauds, and the existing technology lacks effective scientific identification methods for medical insurance fraud, which seriously affects the balance of income and expenditure of medic...

AI Technical Summary

Problems solved by technology

However, most of the existing medical insurance risk control schemes are based on setting threshold red lines to monitor violations such as fraud, waste, and abuse. Since violations often change with policies, payment methods, and supervision, simple threshold division is not suitable for many Scenarios, the composition of insured personnel, and the actual application environment of policy replacement

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