ML-Based Retry Timing for Multi-Tenant Payment Systems
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Solution Overview
Problem
In multi-tenant computing environments, conventional techniques for retrying failed payment transactions are inefficient, as they lack a data-driven approach to determine optimal retry timing, leading to increased costs and customer churn due to unpaid invoices and failed payment retries.
Innovation Solution
A machine learning-based approach is employed to optimize retry timing schedules for failed payment transactions by training a model using multilevel data such as subscriber, account, and tenant-level information, allowing for tailored retry strategies based on historical success rates and error types, thereby improving the likelihood of successful retries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional retry techniques are used for failed payment transactions, then the system maintains simplicity in operation, but the success rate of payment retries decreases and costs increase
Solution Approach 1:
The system performs preliminary analysis of historical payment data, error types, and contextual information before executing retry attempts. Machine learning models are trained in advance on multilevel data (subscriber, account, tenant levels) to predict optimal retry timing and strategies, allowing the system to make informed decisions rather than using generic retry approaches.
Solution Approach 2:
The system dynamically adjusts retry parameters such as timing, frequency, and strategy based on analyzed historical data and error characteristics. Different tenants and subscribers receive customized retry schedules generated by machine learning models, optimizing parameters like retry intervals and attempt numbers based on predicted success probabilities rather than using fixed conventional parameters.
2Reliability
If conventional retry techniques are used for failed payment transactions, then the system maintains ease of operation, but customer churn increases due to unpaid invoices
Solution Approach 1:
The system automatically generates and executes optimized retry strategies without requiring manual intervention. Machine learning models autonomously analyze historical data, predict optimal retry approaches, and implement customized schedules for different tenants and subscribers, eliminating the need for manual configuration while improving payment collection reliability.
Solution Approach 2:
The system continuously monitors retry outcomes and uses this feedback to refine and update machine learning models. Historical success rates and error patterns are fed back into the training process, allowing the system to adapt and improve its retry strategies over time, thereby enhancing payment collection reliability through data-driven optimization.
3Reliability
If conventional retry techniques are used, then the system avoids complexity in data processing, but the likelihood of successful retries decreases
Solution Approach 1:
The system segments data processing into distinct hierarchical levels: subscriber-level data (payment history, error types), account-level data (billing information, subscription details), and tenant-level data (organization-wide patterns). Machine learning models process each segment separately and integrate results to generate comprehensive retry strategies, making complex data processing manageable and effective.
Solution Approach 2:
The system combines multiple data sources and analysis methods into a composite approach. It integrates structured transaction data, unstructured error logs, contextual information, and machine learning predictions to create a holistic view for determining optimal retry strategies, leveraging the strengths of different data types and methods to improve success rates.
Data Source
AI summary
Machine learning-based techniques are described that enable modifying an event timing schedule in a multi-tenant computing environment. The multi-tenant computing environment stores tenant data of multiple tenants. Each tenant of the multiple tenants offers subscription services to subscribers. Multiple events involving multiple subscribers of a particular tenant are attempted. The multiple events include a first subset of successfully executed events and a second subset of unsuccessfully executed events. One or more training datasets are generated based on the first subset and the second subset. The one or more training datasets include contextual information corresponding to each of the multiple events. The contextual information includes multilevel data. A machine learning model is trained to output a timing schedule for retrying a particular unsuccessfully executed event of a particular subscriber.