Dynamic Payment Transaction Routing via Pseudo-Network Simulation
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Solution Overview
Problem
Merchants face difficulties in routing electronic payment transactions efficiently due to complex factors influencing interchange categories and rates, including regulatory status, card types, and network preferences, making it challenging to predict and minimize transaction costs.
Innovation Solution
A system and method using payment pseudo-networks and electronic transaction simulation to dynamically identify eligible networks and route transactions to the least cost PIN-less network by extracting routing criteria and determining breakeven transaction amounts, optimizing transaction routing based on merchant categories, regulatory status, and transaction amounts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a static table of networks or issuers is used to determine routing, then initial insights into costs are provided, but real costs cannot be accurately predicted because costs depend on regulatory status and negotiated thresholds that vary by merchant and issuer
Solution Approach 1:
The patent implements dynamic routing that adapts to each transaction based on multiple variables including merchant category code, issuer regulatory status, transaction amount, and negotiated thresholds. The system calculates optimal routing in real-time rather than using static predefined tables, allowing costs to be accurately predicted for each specific transaction context.
Solution Approach 2:
The system changes multiple parameters simultaneously to determine optimal routing: merchant category code, issuer regulatory status (Durbin exempt/regulated), transaction amount thresholds, and negotiated markup rates. By dynamically adjusting these parameters based on current transaction characteristics, the system achieves precise cost prediction without requiring overly complex manual configuration.
2Loss of energy
If transaction routing is optimized for large volume merchants to maximize savings, then hundreds of thousands of dollars per month could be saved, but routing decisions become more complicated by multiple locations and business lines with different MCCs
Solution Approach 1:
The patent segments transactions by merchant category code (MCC) and location, allowing each segment to be routed independently to the optimal network. This enables multi-location merchants with diverse business lines to receive tailored routing decisions for each transaction type, maximizing savings while maintaining manageable system complexity through automated segmentation.
Solution Approach 2:
The system automatically performs routing optimization without requiring manual intervention for each transaction. By self-sequencing through multiple variables (MCC, regulatory status, transaction amount, negotiated rates) and calculating the optimal path automatically, the system handles complex multi-location scenarios without increasing operational complexity for the merchant.
3Measurement precision
If dynamic routing considering multiple variables is implemented, then cost prediction accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary sequencing and evaluation of routing variables before final transaction processing. By pre-establishing the decision hierarchy (MCC → regulatory status → transaction amount → negotiated rates) and caching relevant merchant-issuer agreements, the system reduces real-time computational burden while maintaining high prediction accuracy.
Solution Approach 2:
The system efficiently processes multiple parameters by implementing conditional logic that evaluates variables in a predetermined sequence, stopping when the optimal route is determined. This parameter-based decision framework achieves high accuracy without requiring exhaustive analysis of all possible routing combinations for each transaction.
Data Source
AI summary
Systems and methods are for routing electronic payment transactions to PIN-less networks using payment pseudo-networks and electronic transaction simulation. One method comprises: receiving transaction-related information from a merchant, the transaction-related information including a bank identification number (“BIN”), one or more available network IDs, one or more merchant categories, an issuer regulatory status, a transaction amount, and a preferred status; extracting routing criteria from the received transaction-related information; dynamically identifying one or more eligible networks based on extracted routing criteria; dynamically identifying one or more breakeven transaction amounts for each identified eligible network, each breakeven transaction amount defining a point at which two or more eligible networks have the same expenses for a given transaction amount; and routing signature debit transactions from the merchant to a least cost PIN-less network selected from the eligible networks based on identification of a desired breakeven transaction amount for the PIN-less network.