Payment Routing System Using Scaled Scores for Settlement Optimization
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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 transaction costs increase
Solution Approach 1:
The system enables self-service by automatically analyzing transaction data and selecting optimal routing paths without requiring merchant intervention. The payment routing system autonomously evaluates multiple accounts and financial institutions, making routing decisions based on real-time data analysis while maintaining operational simplicity for merchants.
Solution Approach 2:
The system dynamically changes routing parameters based on transaction characteristics, account balances, and settlement likelihood. Instead of fixed predetermined settings, the system adjusts routing decisions in real-time by evaluating multiple variables including transaction amount, account type, and historical settlement patterns to optimize processing efficiency.
2Ease of manufacture
If predetermined merchant settings are used for payment routing, then configuration is simple, but transaction costs increase
Solution Approach 1:
The system performs self-optimization by automatically selecting cost-effective routing paths without requiring manual configuration changes. It continuously monitors transaction outcomes and adjusts routing strategies to minimize costs while maintaining simple merchant-facing interfaces.
Solution Approach 2:
The system implements feedback-driven cost optimization by analyzing transaction settlement outcomes and using this information to improve future routing decisions. Settlement data and transaction results are fed back into the routing algorithm to continuously reduce transaction costs based on actual performance patterns.
3Measurement precision
If real-time scaled score calculation is implemented, then settlement accuracy improves, but system complexity increases
Solution Approach 1:
The system introduces an intermediary scaled score mechanism that translates complex multi-factor analysis into a single probability metric. This intermediary layer simplifies the decision-making process by consolidating numerous input variables (account balances, transaction history, financial institution reliability) into one actionable settlement likelihood score.
Solution Approach 2:
The system transforms multiple complex parameters into a standardized scaled score parameter ranging from 0 to 1. This parameter transformation simplifies the evaluation process by converting diverse input variables into a unified metric that directly indicates settlement probability, making the system more manageable despite the complexity of underlying calculations.
4Adaptability or versatility
If multiple accounts across multiple financial institutions are evaluated, then routing options increase, but processing time increases
Solution Approach 1:
The system performs preliminary evaluation of multiple accounts and financial institutions by pre-calculating scaled scores and settlement probabilities before the actual routing decision is required. This advance preparation allows the system to quickly select from pre-evaluated options when a transaction needs to be routed, maintaining both versatility and speed.
Solution Approach 2:
The system segments the evaluation process into distinct phases: preliminary scoring of multiple accounts, filtering based on threshold criteria, and final selection. By dividing the complex evaluation of multiple financial institutions into manageable segments with clear decision checkpoints, the system can efficiently process numerous routing options without excessive processing time.
Data Source
AI summary
A system for payment routing programmed to: receive a payment transaction message relating to a putative transaction, the payment transaction message containing putative transaction data identifying an accountholder and a transaction amount corresponding to the putative transaction; input historical transaction data for the accountholder and at least a portion of the transaction amount to a scaled score algorithm to generate scaled scores, each of the scaled scores representing the likelihood of settlement of the putative transaction on a corresponding date and payment rail; output the scaled scores to a merchant in response to the payment transaction message; receive, from the merchant and in response to the output, feedback data for the putative transaction, the feedback data including a date of attempted payment processing for the putative transaction and an indicator of whether the attempted payment processing was completed; and retrain the scaled score algorithm using the feedback data.