Payment Routing System Using Scaled Settlement Scores
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Solution Overview
Problem
Existing payment systems often result in suboptimal payment processing and higher costs due to predetermined merchant settings, leading to failed transactions and inefficient routing methods.
Innovation Solution
A system that uses processors and transceivers to receive payment transaction messages, generate scaled scores based on historical data, and apply contribution rules to optimize payment routing according to the likelihood of settlement, allowing for real-time adjustments and feedback-driven algorithm retraining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If predetermined merchant settings are used for payment routing, then the system is simple to operate, but payment processing efficiency deteriorates and settlement success rates decrease
Solution Approach 1:
The system automatically performs payment routing optimization without requiring merchant intervention. The payment processing system self-adjusts routing decisions based on real-time account data analysis, scaled score generation, and settlement likelihood predictions, eliminating the need for merchants to manually configure routing settings while maximizing processing efficiency
Solution Approach 2:
The system dynamically changes routing parameters based on real-time analysis of account data, transaction patterns, and settlement probabilities. Instead of using fixed predetermined settings, the system adjusts routing decisions by modifying parameters such as selected account, payment network, and processing timing based on calculated scaled scores and settlement likelihood
2Device complexity
If predetermined merchant settings are used for payment routing, then device complexity is reduced, but settlement success rates worsen
Solution Approach 1:
The system implements continuous feedback loops where settlement outcomes are monitored and used to refine future routing decisions. The payment processing system analyzes actual settlement results, updates account data and transaction patterns, and adjusts scaled score calculations accordingly, creating a self-improving mechanism that increases settlement success rates without increasing apparent system complexity
Solution Approach 2:
The system performs preliminary analysis of account data, transaction patterns, and settlement probabilities before making routing decisions. By pre-calculating scaled scores and predicting settlement likelihood for different routing options, the system ensures optimal routing choices are made in advance, maximizing settlement success rates while maintaining straightforward operation
3Productivity
If dynamic scaled score algorithms are implemented for payment routing, then payment processing efficiency is improved, but device complexity increases
Solution Approach 1:
The complex routing decision-making process is segmented into distinct functional modules: account data collection, transaction pattern analysis, scaled score calculation, settlement likelihood prediction, and routing decision execution. Each module handles a specific aspect of the process, making the overall complex system manageable and maintainable while achieving high processing efficiency through specialized optimization in each segment
Solution Approach 2:
The payment processing system acts as an intermediary layer between the merchant and the multiple payment accounts/networks. This intermediary automatically manages the complexity of analyzing account data across multiple financial institutions, calculating scaled scores, and determining optimal routing, shielding merchants from complexity while delivering efficient payment processing
4Reliability
If multiple accounts across multiple financial institutions are analyzed, then settlement success rates are improved, but loss of time increases due to data processing
Solution Approach 1:
The system performs preliminary collection and preliminary analysis of account data from multiple financial institutions before transactions occur. By pre-processing and storing relevant account information, transaction patterns, and settlement characteristics, the system reduces the time required during actual transaction processing while maintaining comprehensive analysis across multiple accounts for high settlement success rates
Data Source
AI summary
A system for payment routing programmed to: receive a payment transaction message relating to a putative payment transaction of an accountholder, the payment transaction message containing putative payment transaction data including a transaction amount corresponding to the putative payment transaction; identify a plurality of accounts corresponding to the accountholder based on the payment transaction message; implement a payment split among the accounts, the payment split assigning a proportion of the transaction amount to each of the accounts; and generate a scaled score representing the likelihood of settlement of the assigned proportion of the transaction amount via each of the accounts on a date.