Graph Database Fraud Detection with Versioned Nodes
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Solution Overview
Problem
Current fraud detection systems in banking face challenges in efficiently and accurately identifying fraudulent activities, particularly in account takeovers and credential reuse, due to limitations in data storage and processing capabilities.
Innovation Solution
The implementation of graph databases that utilize account nodes and attribute nodes, where nodes of the same type are not directly linked, allowing for the creation of updated nodes with higher version numbers and immutable linking, enabling the calculation of fraud proximity scores and maintaining historical records of fraudulent activity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data storage and processing methods are used in fraud detection systems, then the system structure is simple, but the accuracy and speed of fraud detection deteriorates
Solution Approach 1:
The patent segments fraud detection data into distinct graph database components: account nodes representing customer accounts, attribute nodes representing account attributes (addresses, phone numbers, devices), and transaction nodes representing financial transactions. This segmentation allows the system to model complex relationships between different entities while maintaining manageable data structures that can be efficiently queried for fraud patterns.
Solution Approach 2:
The patent transitions from traditional flat or relational data storage to a graph-based dimensional structure, where relationships between accounts, attributes, and transactions are explicitly modeled as edges in a multi-dimensional graph space. This dimensional change enables the system to capture complex fraud patterns across multiple dimensions (temporal, relational, behavioral) simultaneously, improving detection accuracy without proportionally increasing processing complexity.
2Reliability
If graph databases with version control are implemented to track fraudulent activity, then fraud detection quality improves, but data storage complexity increases
Solution Approach 1:
The patent implements version control mechanisms in advance within the graph database structure, where each node (account, attribute, transaction) maintains a version history from creation. This preliminary action allows the system to automatically track changes over time, maintain immutable audit trails of fraudulent activity, and revert to previous states if needed, thereby improving fraud detection reliability without requiring complex external version management systems.
Solution Approach 2:
The patent creates updated versions of nodes when attributes change, rather than modifying existing nodes in place. Each updated node is a copy with an incremented version number, preserving the historical state. This copying approach simplifies the storage structure by using standard graph database node replication capabilities, avoiding the need for complex version control mechanisms while maintaining reliable fraud detection through historical node versions.
3Loss of information
If immutable linking is used to preserve historical node versions, then fraud history tracking improves, but data processing speed deteriorates
Solution Approach 1:
The patent extracts only the essential fraud-relevant information from historical node versions during fraud detection queries, rather than processing entire version histories. The graph database structure allows selective traversal of relevant relationship paths and filtering of nodes based on fraud indicators, extracting only the necessary historical information needed for detection decisions. This extraction approach preserves complete fraud history for audit purposes while maintaining fast processing speeds by avoiding unnecessary data traversal.
4Productivity
If nodes of the same type are not directly linked to improve data organization, then data storage efficiency improves, but query complexity increases
Solution Approach 1:
The patent uses attribute nodes as intermediary elements between account nodes and transaction nodes in the graph structure. Instead of allowing direct links between accounts and transactions, all relationships pass through attribute nodes that represent account properties. This intermediary structure improves storage efficiency by enforcing a consistent data model and reducing redundant relationships, while query complexity is managed through the natural semantic meaning of attribute nodes that guide the query structure.
Data Source
AI summary
Aspects discussed herein relate to the storage of data in graph databases and detecting fraudulent behavior in the stored data. Fraud detection systems may use graph databases to store data, allowing for querying the graph database to obtain data using a variety of graph semantics such as nodes, edges, and properties. Graph databases in accordance with embodiments of the invention may include account nodes and attribute nodes, where nodes of the same type are not directly linked to each other. When a particular node is updated, an updated node may be created with a higher version number than the existing node. Each node may include an indication of the node being associated with fraudulent activity. Fraud indicators may be calculated based on the relationships between the nodes and fraud indicators for the nodes.