Chargeback Analytics Device Segmentation for Merchant Experience
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Solution Overview
Problem
Existing chargeback systems fail to differentiate between fraud-related and non-fraud-related chargebacks, leading to inaccurate merchant ratings and inefficient resource allocation, as they process all chargeback data without filtering, obscuring the source of chargebacks and consuming significant time and resources.
Innovation Solution
A chargeback analytics computing device that retrieves transaction data, extracts and parses chargeback data to isolate fraud-related and non-fraud-related portions, specifically identifying service chargebacks initiated by unsatisfied customers, and calculates a merchant experience score based on the service chargeback data to provide a more precise assessment of merchant performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all chargeback data is processed without filtering, then comprehensive chargeback analysis is achieved, but processing time and resource consumption increase significantly
Solution Approach 1:
The patent segments chargeback data into distinct categories: fraud-related chargebacks and non-fraud-related chargebacks. This segmentation allows the system to process and analyze different types of chargebacks separately, reducing the time and resources needed to handle all chargeback data comprehensively while maintaining analytical precision.
Solution Approach 2:
The patent extracts and isolates fraud-related chargeback data from the overall chargeback dataset. By removing fraud-related chargebacks from the general processing stream and handling them separately, the system reduces the volume of data requiring comprehensive analysis, thereby decreasing processing time and resource consumption while preserving the ability to analyze non-fraud chargebacks in detail.
2Loss of information
If all chargeback data is used for merchant rating, then complete chargeback picture is obtained, but merchant performance assessment accuracy decreases due to fraud-related chargebacks obscuring service-related issues
Solution Approach 1:
The patent segments chargeback data into fraud-related and non-fraud-related categories, allowing merchants to be assessed on service-related chargebacks separately from fraud-related chargebacks. This segmentation ensures that merchant performance assessments reflect actual service quality rather than being obscured by fraudulent transactions.
Solution Approach 2:
The patent extracts fraud-related chargeback data from the overall chargeback dataset used for merchant rating. By removing fraud-related chargebacks from the merchant assessment calculation, the system maintains a complete picture of chargeback data while ensuring that merchant performance metrics accurately reflect service quality.
3Quantity of substance
If fraud-related chargebacks are included in merchant scoring, then all chargeback incidents are accounted for, but the scoring system fails to identify merchants with poor service quality
Solution Approach 1:
The patent segments the chargeback dataset into fraud-related and non-fraud-related portions, calculating merchant scores based exclusively on non-fraud chargebacks. This segmentation allows the system to maintain comprehensive data records while generating service-quality-specific metrics that accurately identify merchants with poor service quality regardless of their fraud rates.
Solution Approach 2:
The patent extracts non-fraud-related chargeback data from the complete chargeback dataset to create a specialized scoring mechanism. By removing fraud-related chargebacks from the service quality assessment, the system maintains full chargeback data volume for other purposes while achieving precise service quality identification.
Data Source
AI summary
A chargeback analytics computing device for generating chargeback analytics associated with service chargebacks is provided. The chargeback analytics computing device is configured to retrieve transaction data for a merchant, the transaction data associated with transactions initiated at the merchant, and extract chargeback data from the transaction data, the chargeback data associated with chargeback transactions. The chargeback analytics computing device also parses the chargeback data to identify a chargeback reason code associated with each chargeback transaction. Based upon the chargeback reason codes, the chargeback analytics computing device isolates a fraud-related portion and a non-fraud-related portion of the chargeback data, and extracts a service subset of the non-fraud-related portion associated with service chargeback transactions, wherein a service chargeback transaction is initiated by a cardholder having an unsatisfactory experience with the merchant. The chargeback analytics computing device is configured to calculate and transmit a merchant experience score based upon the service subset.