Method for constructing transaction association graph and carrying out fraud detection by using community discovery
By constructing a transaction association graph and using community detection methods, combined with coherent Ising machines for community detection, the problem of low efficiency in identifying fraudulent behavior in large-scale complex transaction associations in existing technologies is solved, achieving efficient and accurate fraud detection and real-time response under data-constrained conditions.
CN120822955APending Publication Date: 2025-10-21BEIJING QBOSON QUANTUM TECH CO LTD
0 Cites 0 Cited by
Patent Information
- Application Number
- CN202410438117.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-04-12
- Publication Date
- 2025-10-21
AI Technical Summary
Technical Problem
Existing fraud detection systems are inefficient when dealing with large-scale and complex transaction associations, and struggle to accurately identify fraudulent activities when data access is limited.
Method used
A transaction association graph is constructed and a community discovery method is used to identify high-risk communities by calculating AA similarity and optimizing the modularity function, combined with a coherent Ising machine.
Benefits of technology
It efficiently identifies potential fraudulent activities under data-constrained conditions, improves the accuracy and real-time response capability of fraud detection, and has flexibility and long-term adaptability, thereby enhancing the security of the transaction environment.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention belongs to the technical field of big data analysis, and relates to a method for constructing a transaction association graph and carrying out fraud detection by using community discovery, which comprises the following steps of: extracting corresponding transaction flow data according to a transaction behavior of a user; the transaction flow data is converted into a transaction association weight graph, each account is regarded as a node in the transaction association weight graph, and the similarity between the two accounts is regarded as an edge between the two nodes in the transaction association weight graph; filtering edges in the transaction association weight graph according to a similarity threshold value to obtain a transaction association graph; constructing a modularity function for measuring community discovery based on the transaction association graph; optimization is carried out by taking a maximized modularity function as a target, and an optimal solution is found through an iteration mode so as to obtain a community discovery result; and evaluating the fraud-related risk of each community according to a community discovery result so as to realize fraud detection. The method can meet the business requirements of quickly and accurately identifying and predicting the potential fraud risk in the transaction flow.
Need to check novelty before this filing date? Find Prior Art