A knowledge graph anti-fraud feature extraction method based on BFS and LPA

A knowledge graph and feature extraction technology, which is applied in the field of consumer finance for asset management companies, and can solve problems such as not considering group nature and knowledge graphs.

Active Publication Date: 2019-06-21
华融融通(北京)科技有限公司
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Problems solved by technology

However, at present, the core groups targeted by anti-fraud are the intermediary and group fraud models. Traditional features are mostly based on

Method used

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  • A knowledge graph anti-fraud feature extraction method based on BFS and LPA
  • A knowledge graph anti-fraud feature extraction method based on BFS and LPA
  • A knowledge graph anti-fraud feature extraction method based on BFS and LPA

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Embodiment Construction

[0060] The technical solutions of the present invention will be further described below in combination with specific embodiments.

[0061] In order to illustrate the effectiveness of the present invention, we verify it based on the relationship data provided by China Huarong Consumer Finance Company.

[0062] 1. Data import

[0063] The data contains 3 tables, the customer application form, which mainly includes the user's name, contact number, ID number and rules triggered by the user during the application process; the contact table contains the content of the contact's communication contact; the call record table contains Contact's call behavior records.

[0064] 2. Data standardization

[0065] The original data may have problems such as irregular storage, inconsistent fields, mixed Chinese and English, missing data, and multi-category variables. To solve these problems, data cleaning is used to convert the original data into regular data. The specific technical solution...

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Abstract

The invention discloses a knowledge graph anti-fraud feature extraction method based on BFS and LPA, and the method comprises the steps: 1, carrying out the standardization of original data, converting the original data into labeled data under different dimensions, carrying out the cleaning and conversion, and forming data which conforms to the modeling of a knowledge graph; And step 2, constructing a knowledge graph model, including ontology construction, semantic annotation and information extraction. The method has the advantages that (1) a simple social relation is converted into a knowledge relation, different ontology knowledge is injected into a map, and a knowledge map representation method oriented to the consumer finance field is provided; (2) breadth-first search is introduced to find an entity touch black level, touch black information with different traversal lengths can be extracted after improvement, the feature level is enhanced, and the feature representation modes arediversified; And (3) for a fraudulent group problem in the anti-fraudulent field of consumer finance, entity subgroup information is mined by using an entity subgroup mining method based on label propagation, and a corresponding characteristic variable are extracted to show a relatively good distinguishing characteristic.

Description

technical field [0001] The present invention is based on BFS (Breadth-First Search, Breadth-First Search) and LPA (Label Propagation Algorithm, Label Propagation Algorithm-LPA) knowledge graph anti-fraud feature extraction method, relates to user fraud risk assessment technology in the financial field, specifically A knowledge map anti-fraud feature extraction method for asset management companies in the field of consumer finance. Background technique [0002] In recent years, Internet finance companies with P2P lending and consumer finance as their main businesses have emerged, establishing a huge emerging industry in areas that traditional financial industries cannot touch. But at the same time, all kinds of negative news came one after another, casting a shadow over the future of these Internet financial companies. Among them, the risk control capability has always been the lifeblood of these emerging technology financial companies. Only with good risk control technology...

Claims

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Application Information

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IPC IPC(8): G06F16/36G06Q30/00G06Q30/06G06Q40/02
Inventor 后其林李达钟丽莉万谊强仵伟强
Owner 华融融通(北京)科技有限公司
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