Anti-fraud recognition method, storage medium and server with safe computer

An identification method and decision-making model technology, applied in the medical field, can solve the problems of insufficient anti-fraud capability, difficult to control fraudulent behavior, waste of medical resources and other problems in the medical field, and achieve the effect of helping rapid processing, improving decision-making efficiency and reducing impact.

Inactive Publication Date: 2018-03-09
PING AN TECH (SHENZHEN) CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0002] In the medical field, there are often many fraudulent behaviors, such as drug rat behavior, claims fraud, illegal credit card reimbursement, etc. The existence of these fraudulent behaviors will waste medical resources and intensify social conflicts
[0003] However, there is currently no perfect method to identify these fraudulent behaviors, resulting in insufficient anti-fraud capabilities in the medical field, and it is difficult to control fraudulent behaviors

Method used

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  • Anti-fraud recognition method, storage medium and server with safe computer
  • Anti-fraud recognition method, storage medium and server with safe computer
  • Anti-fraud recognition method, storage medium and server with safe computer

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0065] see figure 2 , an embodiment of a method for constructing a decision model in an embodiment of the present invention includes:

[0066] Step S110, acquiring rule template data, and extracting each variable object and each template sample in the rule template data.

[0067] Specifically, the rule template refers to a set of standards used to help determine the review results. The review of a document or project may correspond to one or more rule templates. There are rule templates such as "a branch for loan", "which institution the lender has had a bad record with". Each different rule template has its corresponding rule template data, wherein, the rule template data can include each variable object, each template sample, and the matching relationship between the variable object and the template sample, and the variable object is a variable of qualitative type , and each variable object corresponds to a different category in the rule template. For example, the rule te...

Embodiment 2

[0126] Such as Figure 6As shown, a device for constructing a decision model includes an extraction module 510 , a clustering module 520 , a first feature module 530 , a second feature module 540 and a construction module 550 .

[0127] The extraction module 510 is configured to acquire rule template data, and extract each variable object and each template sample in the rule template data.

[0128] Clustering module 520, configured to perform cluster analysis on variable objects to obtain clustering results.

[0129] The first feature module 530 is configured to match the clustering result with each template sample according to the rule template data, and use the matched clustering result as the first feature.

[0130] The second feature module 540 calculates the black sample probability of each variable object respectively, and uses the black sample probability of each variable object as a second feature.

[0131] A construction module 550, configured to construct a decisio...

Embodiment 3

[0133] see Figure 7 , an embodiment of a method for identifying fraudulent data in an embodiment of the present invention includes:

[0134] Step K10, using a preset continuous model training method to train the preset training data set to establish a continuous anti-fraud model;

[0135] In this embodiment, firstly, the preset continuous model training method is adopted, combined with data analysis theories such as decision tree and random forest, and data analysis tools such as R and SAS, to train the preset training data set to establish a continuous anti-fraud Model. For example, the preset training data set can be divided into multiple groups for training and intermediate testing respectively to establish a continuous anti-fraud model. When using the preset continuous model training method for training, in one embodiment, the preset training data set can be divided into multiple groups, and model training and testing are performed in each group, and each The training ...

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PUM

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Abstract

The invention discloses an anti-fraud recognition method used for solving the problem that the anti-fraud capability is insufficient in the medical field. The method provided by the invention comprises the following steps of determining a target event; extracting target data related to the target event; and processing target data by adopting at least two methods of a decision-making model constructing method, a fraud data recognition method and a fraud behavior recognition method. The invention further provides a storage medium and a server with a safe computer.

Description

technical field [0001] The invention relates to the medical field, in particular to an anti-fraud identification method, a storage medium and a server carrying Ping An Brain. Background technique [0002] In the medical field, there are often many fraudulent behaviors, such as drug rat behavior, claims fraud, illegal card swiping and reimbursement, etc. The existence of these fraudulent behaviors will waste medical resources and intensify social conflicts. [0003] However, currently there is no perfect method to identify these fraudulent behaviors, resulting in insufficient anti-fraud capabilities in the medical field, and it is difficult to control fraudulent behaviors. Therefore, it is an urgent problem for those skilled in the art to find an anti-fraud method to further improve the anti-fraud capability in the medical field. Contents of the invention [0004] Embodiments of the present invention provide an anti-fraud identification method, a storage medium, and a serv...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H10/60G06F17/30G06F17/24
CPCG06F16/24564G06F16/285G06F40/186
Inventor 肖京王健宗王建明徐亮汪伟周宝李想
Owner PING AN TECH (SHENZHEN) CO LTD
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