ML Payment Prediction for Clearing Timing and Amount
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Solution Overview
Problem
In electronic payment networks, there is a lack of real-time prediction for clearing message timing and parameters, leading to confusion for account holders and liquidity issues for account issuers, as well as difficulties in determining when to release pending charges due to latency and non-receipt of clearing messages.
Innovation Solution
A machine learning-based platform that processes authorization request messages to generate confidence predictions for clearing amounts and timing predictions, which are transmitted in real-time to issuer devices, enabling accurate display of available balances and optimal fund management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time prediction of clearing message timing and parameters is implemented using machine learning, then account balance accuracy and liquidity management improve, but system complexity and computational resources increase
Solution Approach 1:
The system performs preliminary actions by training machine learning models in advance using historical authorization and clearing message data. The trained models are then deployed to predict clearing message timing and parameters in real-time without requiring complex runtime computations, thus improving prediction accuracy while managing system complexity.
Solution Approach 2:
The machine learning model acts as an intermediary between historical transaction data and real-time clearing message predictions. It processes and interprets patterns from authorization request messages to generate timing predictions, serving as a mediator that translates raw data into actionable insights without requiring direct complex analysis of every transaction parameter.
2Measurement precision
If machine learning models are trained on historical authorization and clearing messages, then prediction accuracy improves, but data processing time and computational resources increase
Solution Approach 1:
The system performs model training as a preliminary action during off-peak periods or in advance, separating the computationally intensive training phase from the real-time prediction phase. This allows accurate models to be developed without impacting real-time transaction processing speeds.
Solution Approach 2:
The system uses historical authorization request messages and clearing messages as training data to create copies of past transaction patterns. These copied patterns are analyzed during training to build models that can predict future clearing behavior without requiring real-time access to actual transaction data during the training process.
3Reliability
If funds are held in reserve for expected clearing messages, then transaction authorization reliability improves, but account issuer liquidity decreases
Solution Approach 1:
The system implements feedback by continuously monitoring predicted clearing message timing and actual clearing outcomes. This feedback loop allows the system to learn from prediction accuracy and adjust fund reservation strategies, releasing funds earlier when predictions are highly accurate and maintaining longer reserves when uncertainty is higher, thus optimizing both reliability and liquidity.
Solution Approach 2:
The system dynamically adjusts the amount of funds held in reserve based on real-time predictions from the machine learning model. Instead of static reserve requirements, the system varies reserve levels according to predicted clearing timing and confidence levels, allowing flexibility in liquidity management while maintaining sufficient reserves for authorized transactions.
Data Source
AI summary
A modelling platform comprising at least one processor in communication with at least one memory device and a payment processor is disclosed. The at least one processor is programmed to apply one or more data fields of an authorization request message received from a payment processor as one or more inputs to at least one trained machine learning model to generate a first output and a second output. The at least one processor is further programmed to transmit, to an issuer computing device, in real-time as part of an enhanced authorization request message, the first output and the second output. The enhanced authorization request message instructs the issuer computing device to cause a value to be displayed that is viewable by the account holder based upon the first output for a period of time that is based upon the second output.