Aggregating Merchant Chargeback Data Across Payment Platforms
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Solution Overview
Problem
Current methods for monitoring chargebacks are narrowly focused within a single proprietary payment platform, failing to provide a comprehensive view of a merchant's transaction activity across multiple platforms, and often require long periods to identify merchants with excessive chargebacks, which can lead to delayed detection of problematic merchants.
Innovation Solution
A system and method that aggregates merchant transaction data from multiple payment processors, including credit, debit, and electronic payment services, to analyze chargeback activity across platforms, flagging merchants with excessive or high-velocity chargebacks, and enabling real-time or near-real-time identification of suspect merchants, with notifications sent to relevant parties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If chargeback monitoring is performed within a single proprietary payment platform, then the monitoring process is simple and manageable, but the detection is incomplete and delayed because it fails to capture chargeback activity across multiple platforms
Solution Approach 1:
The patent combines chargeback monitoring across multiple proprietary payment platforms into a unified monitoring system. The system aggregates chargeback data from different platforms (Visa, MasterCard, American Express, Discover) and analyzes them together, enabling comprehensive detection of problematic merchants that would be invisible when monitoring each platform separately.
Solution Approach 2:
The monitoring system is designed to handle multiple payment platforms simultaneously with a single unified approach. It processes chargeback data from various sources using common analysis methods and thresholds, making the system universally applicable across different payment networks while maintaining platform-specific nuances.
2Speed
If monthly chargeback thresholds are used for identifying problematic merchants, then the monitoring process is straightforward, but the detection time is too long allowing problematic merchants to continue operating
Solution Approach 1:
The system implements dynamic monitoring that can operate at multiple time frequencies. While maintaining monthly threshold analysis for straightforward identification, it also incorporates real-time or near-real-time monitoring capabilities that can detect and alert about problematic merchants immediately when chargeback patterns emerge, allowing for faster intervention.
Solution Approach 2:
The system establishes predetermined chargeback thresholds and velocity metrics in advance across multiple platforms. By pre-configuring these detection criteria, the system can automatically identify problematic merchants as soon as they cross the thresholds, eliminating delays associated with manual analysis and enabling rapid response.
3Measurement precision
If aggregated data from multiple payment processors is collected and analyzed, then the identification of problematic merchants becomes more accurate and comprehensive, but the data processing complexity increases
Solution Approach 1:
The system segments the aggregation process by maintaining separate data collection streams for each payment platform while using a unified analysis engine. This allows the system to handle the complexity of multiple data sources through modular processing, where each platform's data is collected and pre-processed independently before being combined for comprehensive analysis.
Data Source
AI summary
A computer-implemented method of detecting a merchant with chargeback activity includes a computing device receiving merchant financial data from a plurality of different financial service providers, wherein the merchant financial data comprises chargebacks. The computing device aggregates the received merchant financial data into, for example, a database. The computing device analyzes the aggregated merchant financial data of a single merchant for a chargeback characteristic and the computing device flags the merchant when the chargeback characteristic exceed preset threshold levels. A flagged merchant may trigger an optional notification to one or more of the financial service provider (e.g., card issuer), the acquiring bank, the merchant, or the customer.