Network Graph Hotspot Confidence Scoring
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Solution Overview
Problem
Detecting fraudulent or criminal activity in financial transactions is challenging due to the large volume of data and complexity of transactions, making it difficult to identify potential hotspots and reducing the efficiency of fraud detection systems.
Innovation Solution
A computer-implemented method generates a hotspot confidence score by creating a network graph from transaction data, identifying hotspots, compiling characteristics, receiving user feedback, and using a learning model to update the score, thereby improving the accuracy of fraudulent activity detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional fraud detection methods are used to analyze large volumes of transaction data, then comprehensive monitoring is achieved, but detection efficiency and accuracy deteriorate due to data complexity and volume
Solution Approach 1:
The patent segments the large volume of transaction data into network graphs that represent relationships between entities. By dividing the data into structured graphical representations with nodes and edges, the system can analyze relationships and patterns more efficiently while maintaining comprehensive monitoring coverage.
Solution Approach 2:
The patent implements feedback mechanisms where user interactions with identified hotspots (such as confirming fraud or false positives) are used to retrain and improve the machine learning models. This continuous feedback loop enhances detection accuracy over time while maintaining efficient processing of new data.
2Measurement precision
If manual review of all transactions is performed to ensure accurate fraud detection, then detection precision improves, but processing time and operational complexity increase significantly
Solution Approach 1:
The patent performs preliminary analysis by automatically generating network graphs and identifying potential hotspots using machine learning models before human review. This preliminary action filters out obvious cases and prepares structured information for analysts, reducing the time required for manual verification while maintaining high accuracy.
Solution Approach 2:
The patent creates simplified representations (copies) of complex transaction data in the form of network graphs and hotspot summaries. These copied representations capture essential relationship patterns without requiring analysts to examine raw transaction volumes, enabling quick yet accurate assessment.
3Difficulty of detecting and measuring
If advanced pattern detection algorithms are implemented to identify fraud patterns, then detection capability improves, but system complexity and computational requirements increase
Solution Approach 1:
The patent introduces network graphs as an intermediary representation between raw transaction data and fraud detection analysis. These graphs serve as a mediator that structures complex data into manageable visual representations, enabling sophisticated pattern detection without proportionally increasing system complexity.
Solution Approach 2:
The patent replaces manual mechanical analysis methods with automated machine learning models and algorithms. This substitution enables advanced pattern detection capabilities to process data at scale without requiring proportional increases in human operational complexity.
4Reliability
If comprehensive transaction monitoring is performed on all accounts and transactions, then fraud detection coverage improves, but false positive rates increase
Solution Approach 1:
The patent applies local quality by analyzing specific local patterns and relationships within network graphs rather than applying uniform rules to all transactions. By examining contextual relationships between specific entities and their interactions, the system maintains high monitoring coverage while reducing false positives through localized, context-aware analysis.
Data Source
AI summary
A computer-implemented method to generate a hotspot confidence score for a hotspot in a network graph includes, receiving input data, wherein the input data includes a plurality of messages, each message containing a set of message data. The method further includes generating, based on the plurality of messages, a network graph. The method also includes identifying, in the network graph, a first hotspot. The method includes compiling a set of hotspot characteristics for the first hotspot. The method further includes receiving, in response to identifying the first hotspot, a first user feedback. The method also includes, generating, by a learning model, a hotspot confidence score for the first hotspot; and outputting the hotspot confidence score.