Continuous Merchant Risk Scoring via Collective Intelligence
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Solution Overview
Problem
Merchants, especially small ones with low transaction volume, face challenges in detecting and mitigating payment fraud and chargebacks due to lack of resources and access to comprehensive fraud detection solutions, which can lead to being placed on watchlists like MATCH, resulting in loss of payment processing capabilities.
Innovation Solution
A dynamic system that evaluates merchant risk scores in near real-time, using a continuous risk assessment model based on collective transaction behavior across the payment network, enabling early warning notifications and more accurate risk evaluation, including cross-border transactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional fraud detection solutions are used by small merchants with low transaction volume, then resource requirements and system complexity increase, but fraud detection capability remains insufficient
Solution Approach 1:
The patent combines fraud detection capabilities across multiple merchants into a unified system. By pooling transaction data from numerous merchants, the system achieves comprehensive fraud detection that would be impossible for individual small merchants to implement alone, resolving the contradiction between detection capability and system complexity for resource-constrained merchants
Solution Approach 2:
The system creates a universal fraud detection platform that serves multiple merchants simultaneously. This multi-functional approach allows small merchants to access enterprise-level fraud detection capabilities through a shared system, eliminating the need for each merchant to maintain separate complex detection infrastructure
2Speed
If merchants are monitored in real-time for fraud detection, then detection speed improves, but information processing requirements and system resource consumption increase
Solution Approach 1:
The system performs preliminary fraud risk assessment by analyzing historical transaction patterns and establishing baseline behavior profiles before actual fraud occurs. This advance preparation enables faster real-time detection decisions without requiring intensive processing during transaction moments, balancing speed with resource consumption
Solution Approach 2:
The system applies partial monitoring intensity based on risk levels. Low-risk transactions receive minimal processing while high-risk transactions trigger more intensive analysis. This differentiated approach enables near-real-time detection for critical cases while reducing overall information processing requirements across the entire merchant network
3Ease of operation
If binary risk categorization (low-risk/high-risk) is used, then decision-making simplicity improves, but risk assessment precision decreases due to inability to capture intermediate risk levels
Solution Approach 1:
The system transforms risk assessment from binary categorization to a continuous spectrum using risk scores. By changing the parameter from discrete categories (low/high risk) to continuous values, the system maintains ease of operation through score-based decisions while dramatically improving measurement precision to capture subtle risk variations and intermediate states
4Measurement precision
If continuous risk scoring is implemented instead of binary categorization, then risk assessment precision improves, but computational complexity and processing time increase
Solution Approach 1:
The system pre-computes risk factors and establishes scoring models in advance based on historical data. This preliminary action enables continuous risk scoring to be performed efficiently during transactions, improving precision without proportionally increasing processing time as the computational heavy lifting is done beforehand
Solution Approach 2:
The continuous risk scoring system is segmented into discrete computational components that can be processed independently and efficiently. By breaking down the complex scoring algorithm into manageable segments, the system achieves high precision risk assessment while maintaining acceptable processing speeds through modular computation
Data Source
AI summary
The system and method may assess the merchant risk level on a more continuous scale rather than a binary categorization. It may produce a continuous risk score proportional to the likelihood of a merchant being risky, effectively addressing the issue of shades of gray encountered by the traditional blacklisting approach. The continuous risk score feature provides greater flexibility as it allows the payment network to make dynamic pricing decisions (known as interchange optimization) based on the merchant risk level. Using collective intelligence from transactions across the payment network, the system and method may be able to assess the merchant risk level with high accuracy. The system and method may be particularly beneficial to small merchants with low transaction volume as even a few fraudulent transactions can easily put them in the high-risk merchant category. Further, the system and method may help payment processing networks make better decision on cross-border transactions.