Refund Prediction-Based Payment Processing Delay
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Solution Overview
Problem
Card-based payment processing systems incur significant fees for transactions and refunds, with existing methods failing to efficiently determine when to delay processing requests to minimize these costs.
Innovation Solution
A system that uses data modeling and machine learning to predict the likelihood of a transaction being associated with a refund request, allowing for strategic delays in sending authorization or capture requests to avoid additional fees.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If authorization or capture requests are sent immediately for all transactions, then transaction processing speed is improved, but fees increase due to processing both successful transactions and transactions that will later be refunded
Solution Approach 1:
The system performs preliminary analysis of transaction data using machine learning models before sending authorization or capture requests. By predicting the likelihood of refund in advance, the system determines which transactions should be delayed, avoiding unnecessary processing fees while maintaining speed for transactions unlikely to be refunded.
Solution Approach 2:
The system dynamically changes the timing parameter of transaction processing based on predicted refund probability. For transactions with high refund likelihood, the authorization or capture request timing is delayed until after the refund prediction window expires, transforming the fixed immediate-processing approach into a flexible timing strategy that reduces fees.
2Loss of energy
If authorization or capture requests are delayed for potential refund transactions, then fee costs are reduced, but transaction processing time increases
Solution Approach 1:
The system applies different processing strategies to different transactions based on their individual refund probability predictions. High-risk transactions receive delayed processing while low-risk transactions proceed immediately, creating localized quality differences in processing timing that optimize overall fee reduction without uniformly increasing processing time for all transactions.
Solution Approach 2:
The system delays processing only for the subset of transactions predicted to be refunded, rather than delaying all transactions. This partial action approach ensures that fee-reducing delays are applied selectively to minimize their impact on overall processing time while still achieving cost savings on the problematic transactions.
3Measurement precision
If machine learning models analyze all transaction data, then refund prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
The system segments transactions into different risk categories based on preliminary analysis, then applies appropriate processing delays only to high-risk segments. This segmentation allows the machine learning models to focus computational resources on predicting refunds for the most problematic transactions rather than uniformly processing all transactions, reducing overall computational complexity while maintaining accuracy for critical cases.
Data Source
AI summary
Techniques for leveraging a likelihood that a transaction (or a portion thereof) will be associated with a refund request to determine whether and/or how long to delay sending, to a payment service, a request for processing the transaction (or portion thereof) are described herein. In an example, a payment processing service may receive, from a point of sale device operated by a merchant, transaction data associated with a transaction between the merchant and a customer. The payment processing service may determine a likelihood that the customer will request a refund for a cost of an item of the transaction at a time after the transaction. Based at least partly on the likelihood, the payment processing service may determine whether to wait until at least the time before sending, to a payment service, a request to process the transaction data for at least the cost of the item.