Abstract
In the field of digital payment processing, transaction routing systems can operate reactively by identifying performance degradation within a payment processor's network after user transactions have failed. A predictive payment orchestration framework can utilize a machine learning inference engine to generate a real-time authorization probability score for a transaction across multiple available payment processors. This score can be calculated using real-time telemetry signals from the processors and the specific metadata of the pending transaction. Based on the calculated scores, an execution engine can dynamically route the payment to a selected processor or signal a user-facing interface (e.g., a web application, a mobile application, a point-of-sale terminal) to adapt its presentation of payment options. This approach may reduce failed payments and decrease user exposure to backend instability within a payment ecosystem.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Moguluru, Sree Sitamahalakshmi, "The Predictive Scoring for Dynamic Payment Routing and User Interface Adaptation", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/11823