Dynamic Payment Card Insights Through Unified Data
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Solution Overview
Problem
Financial institutions face challenges in obtaining comprehensive and dynamic insights due to incomplete data, which affects their understanding of evolving market conditions and product offerings.
Innovation Solution
An intelligence platform integrates disparate databases and user preferences to compile parameter charts, including card comparison data and benefit data, and generates dynamic benchmarks through spider-web graphs to assess the competitiveness of payment card offerings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If financial institutions rely on incomplete data from various parties, then data compilation is simpler, but the quality and comprehensiveness of insights deteriorates
Solution Approach 1:
The patent combines multiple disparate databases from different parties (financial institutions, merchants, service providers) into a unified data structure that integrates transaction data, user profiles, and contextual information. This merging approach resolves the contradiction by consolidating incomplete data sources into a comprehensive view while managing integration complexity through standardized protocols.
Solution Approach 2:
The system creates a universal data structure that can accommodate multiple types of data sources and formats from different parties. This multi-functional framework allows the same integration mechanism to handle various data types (transactions, user preferences, contextual data) simultaneously, improving data completeness without proportionally increasing system complexity.
2Adaptability or versatility
If financial institutions use static data analysis, then analysis processing is simpler, but the ability to understand evolving market conditions deteriorates
Solution Approach 1:
The patent implements dynamic data structures that automatically update as new transactions and user interactions occur. The system transitions from static snapshots to continuously evolving data models that reflect current market conditions, enabling real-time insights while using incremental update mechanisms to manage computational complexity.
Solution Approach 2:
The system incorporates feedback loops where analysis results inform subsequent data collection and processing. User interactions with generated insights trigger additional data gathering and re-analysis, creating an adaptive cycle that improves market responsiveness. This feedback mechanism enables dynamic adaptation without requiring complete system reprocessing.
3Loss of information
If financial institutions analyze data at individual party level, then data privacy is better protected, but the comprehensiveness of insights deteriorates
Solution Approach 1:
The patent introduces an intermediary data structure that aggregates and anonymizes information from multiple parties before analysis. This intermediary layer combines data at a level that preserves individual privacy while enabling comprehensive market-level insights. The intermediary structure acts as a buffer that prevents direct exposure of sensitive individual data while still allowing holistic analysis.
4Measurement precision
If financial institutions compile comprehensive data from multiple sources, then insight quality improves, but data integration complexity increases
Solution Approach 1:
The patent segments the comprehensive data integration task into modular components: transaction data modules, user profile modules, contextual data modules, and analysis modules. Each segment handles specific data types independently using standardized interfaces, improving insight quality through comprehensive data collection while reducing overall integration complexity through modular architecture.
Data Source
AI summary
Systems and methods are provided for defining dynamic insights for a proposition. One example computer-implemented method includes in response to a request including a card value proposition (CVP) from a relying institution, accessing data from one or more databases, the data including card comparison data and benefit data; compiling a data structure, from the accessed data, the data structure including data for one or more segments including card peers for the CVP, a peers as defined by the geographic limitations, and the CVP, for each of multiple parameters; calculating representative values for the multiple parameters included in the data structure; generating a graphic having a spoke specific to each of the multiple parameters and a line based on the calculated values for each of the card peers, peers defined by the graphic limitation, and CVP; and presenting the graphic to the relying institution, in response to the request.