Financial anti-fraud method and device based on heterogeneous graph deconstruction

Abstract

The invention provides a financial anti-fraud method and device based on heterogeneous graph deconstruction, and relates to the technical field of financial anti-fraud, and the method comprises the following steps: extracting entities needing to be associated in sample data, constructing a relationship between the entities, and converting the entities into a heterogeneous graph; constructing at least one meta-path based on the entity, carrying out node walk sampling on the heterogeneous graph according to the meta-path, and deconstructing the heterogeneous graph to obtain a sampling sample of the node, the meta-path being composed of symmetrical entities, and the entities in the meta-path corresponding to nodes of the meta-path; constructing at least one isomorphic composition based on the sampled sample; extracting graph features of the same composition, and constructing an anti-fraud model based on the graph features; and inputting the to-be-processed data into the anti-fraud model to obtain an anti-fraud analysis result output by the anti-fraud model. The entity information contained in the heterogeneous graph is fully utilized, expert experience is effectively replaced to make specification rules, and a universal self-adaptive financial anti-fraud solution is constructed.

Description

technical field [0001] The invention relates to the technical field of financial anti-fraud, in particular to a financial anti-fraud method and device based on heterogeneous graph deconstruction. Background technique [0002] In the field of Internet financial anti-fraud, different from traditional anti-fraud, all kinds of transactions on the Internet are virtual, so for Internet financial anti-fraud, data processing is particularly critical. Especially in anti-financial fraud fields such as financial loans and transaction fraud, it is usually necessary to check user information for fraud risk management and control. For example, anti-fraud financial fields such as financial loans and transaction fraud usually need to verify, analyze and mine user information in order to control fraud risks. Existing loan business verification models usually use artificial intelligence combined with expert systems or artificial intelligence models. The above two models have some disadvantag...

AI Technical Summary

Problems solved by technology

In recent years, there have been many studies on heterogeneous graphs. The utilization of heterogeneous graphs is mainly divided into two ways. One is to use graph representation algorithms to directly calculate the relevant characteristics of nodes in heterogeneous graphs. The problems involved are: It is generally difficult for the representation algorithm to achieve real-time update and real-time calculation of the features on the entire graph, and the graph representation algorithm generally consumes more resources, and there are many difficulties in the application perspective; the second is to use expert experience to formulate some rules for heterogeneous The relationship or nodes in the graph are reduced to convert it into an isomorphic graph for the next calculation, but expert experience is not universal in different products and scenarios, and the rules that may be known for different products are not applicable. Involving the development of new rules requires a lot of manpower and material resources, and the expert experience generally only focuses on existing entities. For the addition of unknown entities, the rules need to be redeveloped. The process is cumbersome and there may be errors. Most of the reductions use only one The entity-related isomorphic graph ignores the information that may be implied by other entity isomorphic graphs. Most of the reduction rules are also regarded as commercial secrets, which are difficult to implement in practice.

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