Machine Learning Transaction Retry Decisions with Cost-Based Failure Analysis
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Solution Overview
Problem
Online transaction processors face challenges in determining whether to retry failed data processing operations, as excessive retries waste resources while abandoning retries can lead to declined transactions due to system unavailability or incomplete data.
Innovation Solution
Implementing a machine learning model that predicts the likelihood of success for retrying failed transaction processing requests, using a cost function to determine whether the retry cost is less than the cost of abandoning the transaction, thereby optimizing resource usage and transaction success.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all failed data processing requests are retried, then transaction success rate is improved, but computing and network resources are wasted
Solution Approach 1:
The system performs preliminary analysis of failure reasons and predicts retry success probability before executing retry operations. By evaluating failure categories (transient vs. permanent failures) and associated risk scores in advance, the system determines whether retrying is worthwhile, preventing resource waste on futile retry attempts while capturing successful ones.
Solution Approach 2:
The system continuously monitors retry outcomes and uses this feedback to refine failure analysis and prediction models. By learning from actual retry results, the system improves its ability to distinguish between transient failures that warrant retry and permanent failures that should be abandoned, optimizing the balance between success rate and resource consumption over time.
2Loss of energy
If no retry is performed for failed requests, then resource wastage is minimized, but transactions are declined due to system unavailability or incomplete data
Solution Approach 1:
The system performs preliminary classification of failure reasons into transient and permanent categories before making retry decisions. By analyzing failure characteristics in advance and assigning risk scores, the system identifies candidates for retry that could succeed on subsequent attempts while avoiding unnecessary retries for permanent failures, thus balancing resource conservation with transaction completion.
3Device complexity
If retry decisions are made manually or with simple rules, then system complexity is low, but retry effectiveness is insufficient
Solution Approach 1:
The system replaces simple mechanical retry rules with an intelligent decision-making framework that incorporates failure reason analysis, risk scoring, and machine learning models. This substitution enables nuanced, context-aware retry decisions that consider multiple factors such as failure category, risk level, and historical patterns, significantly improving retry effectiveness without requiring overly complex manual control mechanisms.
Data Source
AI summary
There are provided systems and methods for identifying transaction processing retry attempts based on machine learning models for transaction success. A service provider, such as an electronic transaction processor for digital transactions, may detect a failure of data processing for a transaction when processed with a separate data processing system, such as a card processing system for payment cards. In order to minimize cost and wasted resources for retrying transactions that are likely to further fail, a machine learning model may be implements that generates a predictive score for whether a failed transaction is likely to be successful if retried with the data processing system. The predictive score may be used to predict a probability of success, which may then be used with a cost function to determine a cost to retry the failed transaction and a cost to stop a retry of the failed transaction.