Financial Transaction Social Graph for Targeted Offerings
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Solution Overview
Problem
Current social networking technologies lack the ability to effectively utilize financial transaction data to construct and analyze social graphs, which could provide insights for targeted product offerings and user profiling.
Innovation Solution
A method to generate a financial transaction-based social graph by analyzing user transaction data, identifying connections through co-occurrence and bill-splitting events, and assigning weights to these connections to determine relationship strengths, allowing for the creation of a social graph that can be used for collaborative filtering and user profiling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If financial transaction data is utilized to construct social graphs, then user relationship accuracy and product offering relevance are improved, but data processing complexity and computational resources increase
Solution Approach 1:
The patent segments financial transaction data into distinct event types (co-occurrence events, bill-splitting events) and processes them through separate analytical pathways. This segmentation allows the system to handle different data patterns with specialized algorithms, improving relationship detection accuracy while managing computational complexity through modular processing.
Solution Approach 2:
The patent introduces an intermediary processing layer that transforms raw financial transaction data into structured social graph relationships. This intermediary layer includes event detection modules, relationship strength calculation mechanisms, and graph construction algorithms that mediate between raw data and final social graph output, reducing overall system complexity.
2Measurement precision
If detailed financial transaction analysis is performed to identify co-occurrence and bill-splitting events, then connection strength determination is improved, but processing time and computational load increase
Solution Approach 1:
The patent performs preliminary actions by pre-defining relationship strength thresholds and connection criteria before processing transaction data. The system establishes weighted scoring systems for different event types in advance, allowing rapid evaluation of transaction patterns without real-time complex calculations, thus reducing processing time while maintaining connection strength determination accuracy.
Solution Approach 2:
The patent changes parameters by transforming detailed transaction data into standardized relationship strength scores through weighted parameter adjustment. Different event types (co-occurrence vs. bill-splitting) are assigned different weight parameters, allowing the system to efficiently calculate connection strengths by summing weighted events rather than performing detailed analysis of each transaction.
3Adaptability or versatility
If comprehensive user profiling is conducted using social graph connections, then product offering targeting is improved, but data privacy concerns and security risks increase
Solution Approach 1:
The patent extracts only the necessary relationship patterns from financial transaction data without exposing underlying personal information. The system extracts co-occurrence and bill-splitting event patterns to build social graphs, deliberately leaving out sensitive transaction details, account information, and personal identifiers, thus enabling targeted product offerings while mitigating privacy concerns.
Solution Approach 2:
The patent introduces an intermediary anonymization layer between financial data and user profiling processes. The system uses aggregated relationship metrics and normalized connection scores as intermediaries rather than raw personal data, allowing comprehensive user profiling for product targeting while maintaining a privacy-protecting barrier that prevents direct exposure of sensitive financial information.
Data Source
AI summary
Systems and methods that may be used to generate and use a social graph generated by user financial transaction data (i.e., a financial transaction-based social graph). Connections and other data within the financial transaction-based social graph can be used for targeted product offerings, other offerings, and or advertisements via e.g., collaborative filtering and user segmentation and profiling.