Payment Transaction Cost Optimization via Dynamic Card Routing
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Solution Overview
Problem
Merchants face high costs from interchange fees, card network fees, and processor fees when processing card transactions, with varying rates for different card types and networks, making it challenging to minimize overall transaction costs.
Innovation Solution
A system and method that calculates and suggests the most cost-effective card type and convenience fee by accessing BIN and rate information, using algorithms to determine lower-cost card types and convenience fees, allowing merchants to process transactions through the cheapest available network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If merchants process card transactions through multiple card networks, then transaction coverage and customer convenience are improved, but transaction costs increase due to varying interchange fees and network fees
Solution Approach 1:
The system dynamically changes the processing parameters (card network selection, card type routing) based on transaction characteristics such as card category, merchant type, and amount. By adjusting these parameters in real-time, the system achieves both broad transaction coverage and cost optimization, resolving the contradiction between versatility and cost.
2Ease of operation
If merchants accept multiple card types with different features, then customer convenience is improved, but processing complexity and cost management difficulty increase
Solution Approach 1:
The system performs preliminary classification and routing decisions based on pre-configured rules and real-time analysis of card characteristics. By making these decisions upfront in the authorization phase, the system simplifies subsequent processing while maintaining support for multiple card types, thus improving customer convenience without proportionally increasing complexity.
Solution Approach 2:
The system implements feedback mechanisms that monitor transaction outcomes, costs, and patterns to continuously optimize routing decisions. This feedback loop enables the system to manage increasing card type diversity without linearly increasing processing complexity, as learned patterns automate decision-making.
3Loss of energy
If merchants manually optimize card processing costs, then cost reduction is achieved, but time consumption and operational burden increase
Solution Approach 1:
The system implements automated self-service capabilities that independently analyze transaction data, select optimal card networks, and execute routing decisions without human intervention. This automation achieves cost optimization while eliminating the time consumption and operational burden associated with manual processes.
Solution Approach 2:
The system replaces manual mechanical optimization processes with automated electronic algorithms and software-based decision-making. This substitution maintains cost reduction benefits while dramatically reducing time consumption by eliminating human review and manual configuration steps.
Data Source
AI summary
Disclosed herein is a method for managing a payment transaction wherein a processor receives a transaction information input corresponding to a particular card type. The processor then accesses a BIN table and a rate table to calculate a fee based on the transaction information input and compares the calculated fee with alternate fees corresponding to alternate card types. A card type corresponding to a determined lowest fee between the calculated fee and the alternate fees is then outputted. The processor then completes the payment transaction by accessing transaction information corresponding to a user selected card type.