Peer Benchmark Dataset Generation for Payment Card Analysis
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Solution Overview
Problem
Current card processing systems face challenges in efficiently analyzing and comparing transaction data across different payment cards due to high volumes of customer information, leading to inaccurate and inefficient product comparisons, and lack the capability to share proprietary information effectively.
Innovation Solution
A computer-implemented method and system that generates peer benchmark datasets by querying multiple databases to select electronic cards based on spend band value and transaction data, creating a benchmark dataset that can be shared securely, allowing for accurate and efficient comparisons of payment card performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional methods are used to analyze transaction data across different payment cards, then data from multiple databases can be collected, but the analysis becomes inefficient and inaccurate due to high volumes of customer information and inability to share proprietary data
Solution Approach 1:
The patent introduces a card processing computing system as an intermediary that receives anonymized transaction data from multiple card issuing computing systems. This intermediary system processes and analyzes the data without requiring direct sharing of proprietary information between competing card issuers, thereby enabling efficient analysis while preserving data confidentiality.
Solution Approach 2:
The system creates anonymized copies of transaction data that contain the necessary analytical information without including sensitive proprietary details. These copied datasets are processed for benchmarking and comparison purposes, allowing analysis efficiency to improve while the original proprietary information remains protected at its source.
2Measurement precision
If all transaction data from multiple databases is processed for benchmarking, then comprehensive comparisons can be made, but the processing time and computational resources increase significantly
Solution Approach 1:
The system extracts only the essential transaction attributes needed for benchmarking (such as transaction amounts, frequencies, and categories) while excluding unnecessary data. This extraction process creates a streamlined dataset that maintains comparison accuracy but reduces processing time and computational requirements.
Solution Approach 2:
The patent segments the transaction data processing into distinct stages: data collection from multiple sources, anonymization, attribute extraction, and benchmarking analysis. This segmentation allows parallel processing of different data streams and enables the system to handle large volumes of information efficiently without compromising comparison precision.
Data Source
AI summary
Methods and systems to generate a peer benchmark dataset comprises receiving a request to generate a deviation value between a payment card and other similar card in the market; querying a first database to receive a first dataset associated with the payment card; querying a second database to receive a second dataset comprising different cards and their respective transaction data; selecting a subset of the cards from the second database based on their spend band and a second characteristic; querying a third database to receive transaction data associated with the selected cards; generating a peer benchmark dataset based on the selected cards and their respective transaction data; and updating a graphical user interface on the client computing device to display the peer benchmark dataset and the first dataset.