Dynamic Payment Routing Using Downtime and Success Prediction
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Solution Overview
Problem
Digital payment systems face challenges with transaction failures due to system downtime and overcapacity, leading to declined transactions, which existing solutions like backup mechanisms and additional payment channels do not adequately address, and lack interoperability and real-time guidance for optimal routing.
Innovation Solution
A system using machine learning models to predict downtime and transaction success probabilities, dynamically routing transactions through the most optimal payment system based on real-time performance data, continuously updating with an adaptive feedback loop to enhance prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple payment channels are introduced to distribute transaction loads, then transaction success rate is improved, but implementation cost and operational complexity increase significantly
Solution Approach 1:
The system enables existing payment channels to serve multiple functions by dynamically routing transactions across different channels based on real-time conditions. A single payment application can leverage multiple existing channels (IMPS, UPI, AEPS, etc.) without requiring separate dedicated infrastructure for each channel, thus improving reliability while avoiding the complexity of managing multiple independent systems.
Solution Approach 2:
The patent introduces a routing module as an intermediary that sits between the user and multiple payment channels. This mediator intelligently selects the optimal channel for each transaction based on real-time performance data, reducing the complexity burden on users and operators while still providing access to multiple channels for improved transaction success rates.
2Reliability
If real-time performance monitoring and dynamic routing are implemented, then transaction failure mitigation is improved, but system complexity and computational resources increase
Solution Approach 1:
The system implements a feedback mechanism where transaction outcomes from each payment channel are continuously monitored and fed back to the routing module. This feedback loop enables the system to learn from past transactions and dynamically adjust routing decisions, improving failure mitigation while keeping the system architecture relatively simple through iterative learning rather than complex predictive modeling.
Solution Approach 2:
The routing module performs preliminary assessment of available payment channels before routing a transaction by checking real-time status indicators. This preliminary action filters out unavailable or poorly performing channels in advance, preventing unnecessary transaction attempts and reducing the computational burden during actual transaction processing.
3Productivity
If additional physical infrastructure and multiple third-party service providers are deployed, then load balancing capability is improved, but implementation cost and operational complexity increase
Solution Approach 1:
The system achieves load balancing by making existing payment channels multi-functional, allowing them to handle different types of transactions dynamically. Instead of deploying dedicated infrastructure for each channel, the same channels serve multiple purposes based on real-time conditions, improving productivity while avoiding the complexity of managing multiple specialized systems.
Data Source
AI summary
A system for dynamically routing digital transactions to mitigate failures caused by system downtime and overcapacity is provided. The system includes a processor and a memory, where the processor retrieves real-time performance data of multiple transaction systems upon receiving a transaction request. This data includes time-window-based features, event-based features, and transaction success metrics. A first machine learning model predicts system downtimes by analyzing past failure rates, response latency, and scheduled maintenance. A second machine learning model determines transaction success probabilities by dynamically weighting real-time features. The processor selects the optimal transaction system based on predicted success probabilities, ensuring a higher likelihood of transaction completion. An adaptive feedback loop refines predictions by continuously updating model parameters using an adaptive decay-rate technique. This approach enhances transaction reliability by intelligently routing payments through the most stable and efficient system, significantly reducing transaction failures in digital payment ecosystems.