Dynamic Merchant Community Graph for Financial Data Analysis
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Solution Overview
Problem
Analyzing financial transaction data is inefficient due to its density and the limitations of traditional static data formats, which take hours or days to assemble and provide a limited perspective on business operations and performance.
Innovation Solution
Constructing and using merchant communities formed from financial transaction data, where each node represents a merchant and edges represent transactions, allowing for clustering and visualization in a dynamic graph format to facilitate faster and more meaningful analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional static data formats (spreadsheets, tables) are used to represent financial transaction data, then data assembly is possible, but the process takes hours or days and provides only a limited perspective
Solution Approach 1:
The patent transforms static data formats into dynamic visual representations where merchants, transactions, and relationships are displayed in real-time through interactive graphs and networks. This allows the data structure to adapt and respond to user interactions, enabling rapid exploration of business operations without time-consuming assembly processes
Solution Approach 2:
The patent adds visual dimensions to traditional tabular data by representing merchants as nodes and transactions as edges in a network graph. This dimensional transformation from 2D spreadsheets to multi-dimensional visual networks enables simultaneous display of numerous relationships and patterns that would be impossible to perceive in traditional formats
2Quantity of substance
If large sets of financial transaction data (millions of merchants, billions of transactions) are assembled using traditional methods, then complete data coverage is achieved, but the analysis process becomes extremely time-consuming
Solution Approach 1:
The patent extracts and highlights the most relevant relationships and patterns from billions of transactions by applying filtering, aggregation, and pattern recognition algorithms. This allows the system to present a manageable visual representation of key business relationships without requiring manual processing of the entire dataset
Solution Approach 2:
The patent merges multiple data dimensions (merchant attributes, transaction patterns, relationship networks) into a unified visual model. This integration allows simultaneous analysis of diverse data types without requiring separate assembly processes for each data category
3Loss of information
If detailed financial transaction data is presented in traditional formats, then data completeness is maintained, but the data becomes dense and difficult to analyze
Solution Approach 1:
The patent uses color coding to represent different merchant categories, transaction types, risk levels, and relationship strengths. This visual encoding transforms dense numerical data into easily distinguishable visual patterns, allowing rapid identification of trends and anomalies while preserving underlying data details
Solution Approach 2:
The patent segments the overwhelming volume of transaction data into meaningful clusters and communities based on merchant relationships and transaction patterns. This segmentation organizes dense data into manageable groups that can be individually analyzed while maintaining context of the overall network
Data Source
AI summary
Systems and methods may be used to generate and use a merchant community graph generated based on merchant financial transaction data. Connections between merchants and other data within the merchant community graph can be used to detect fraud, target product offerings and or other advertisements, detect similar communities, generate dynamic attributes that may be used to develop machine learning models, and develop new user interfaces (UIs) and other features of an information service.