Behavioral Fraud Decisioning With Knowledge Graph Traversal
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Solution Overview
Problem
Conventional fraud detection methods for online transaction platforms struggle with real-time detection of complex and dynamic behavioral fraud patterns due to inefficiencies in data storage and processing, leading to latency and scalability issues.
Innovation Solution
A knowledge graph-based system that dynamically analyzes and synthesizes behavioral fraud patterns by converting user activity data into graph traversal logic, allowing for real-time detection and modification of user workflows to prevent fraud.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional fraud detection methods are used to analyze user behavior data, then fraud detection capability is provided, but real-time detection performance deteriorates due to data storage and processing inefficiencies
Solution Approach 1:
The patent segments user behavior data into discrete signatures representing specific activities at different workflow checkpoints. Each signature captures behavioral characteristics independently, allowing parallel processing and real-time analysis without analyzing entire data sets sequentially, thus resolving the contradiction between detection reliability and real-time performance.
Solution Approach 2:
The patent transforms traditional linear data processing into a graph-based multi-dimensional structure where user behaviors are nodes and relationships are edges. This dimensional transformation enables efficient traversal and pattern recognition algorithms that can detect fraud patterns in real-time while maintaining high detection accuracy through graph theory optimizations.
2Measurement precision
If complex behavioral fraud patterns are analyzed in real-time, then fraud detection accuracy is improved, but system complexity increases leading to scalability issues
Solution Approach 1:
The patent performs preliminary actions by pre-defining fraud patterns as graph traversal queries and pre-processing user behavior data into standardized signatures during normal operations. When fraud detection is needed, the system simply executes pre-prepared traversal logic against pre-processed data, achieving high detection accuracy without real-time complex computation, thus preventing scalability issues.
Solution Approach 2:
The patent introduces an intermediary layer (the graph structure and traversal logic) between raw user behavior data and fraud detection analysis. This intermediary transforms complex behavioral patterns into standardized graph traversal problems that can be solved efficiently using well-established algorithms, reducing system complexity while maintaining high detection accuracy.
3Quantity of substance
If traditional data storage methods are used for user activity data, then data retention is achieved, but processing efficiency deteriorates due to latency
Solution Approach 1:
The patent changes the fundamental parameters of data storage by organizing user activity data into graph structures with nodes representing activities and edges representing transitions. This structural parameter change enables efficient querying and traversal operations that can retrieve and analyze relevant data subsets rapidly, maintaining comprehensive data retention while achieving high processing efficiency for fraud detection.
Data Source
AI summary
Some aspects of the present technology relate to technologies for progressive decisioning of behavioral risk by dynamically analyzing and synthesizing behavioral fraud pattern. In accordance with some configurations, a signature comprising time series data corresponding to a user of an online transaction platform is received at various checkpoints in a user workflow. These signatures are stored in a knowledge graph. Upon receiving a search query from a business user for a combination of signatures indicative of fraud, the search query is converted, without human intervention, into graph traversal logic of the knowledge graph. The knowledge graph is traversed, in real-time, utilizing the graph traversal logic. Based on the traversing, the user workflow can be dynamically modified to prevent fraudulent activity.