Real-Time Financing via Structured Payment Message Decomposition
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Solution Overview
Problem
Existing real-time payment (RTP) technologies in the financial ecosystem often rely on limited and delayed message data, failing to predict and fund future transactions effectively, leading to inefficiencies in providing real-time financial products to customers.
Innovation Solution
A system that decomposes structured messaging data from ISO 20022-compliant Request for Payment (RFP) messages to predict future transactions and pre-approve financial products like loans or credits, offering them in real-time to customers through digital interfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If real-time payment technologies use limited and delayed message data, then system complexity is reduced, but the ability to predict and fund future transactions deteriorates
Solution Approach 1:
The system performs preliminary decomposition of structured messaging data to extract transaction patterns and predicts future transactions before they occur. By analyzing historical message data in advance, the system pre-identifies funding requirements for predicted transactions, allowing real-time provisioning of financial products without increasing operational complexity during transaction processing.
Solution Approach 2:
The system segments structured messaging data into distinct elements and fields, decomposing complex payment messages into analyzable components. This segmentation enables the extraction of specific transaction characteristics without processing the entire message structure, maintaining system simplicity while improving prediction accuracy through focused analysis of relevant data elements.
2Productivity
If the system analyzes message data in real-time to predict future transactions, then the ability to provide real-time financial products is improved, but processing time increases
Solution Approach 1:
The system performs message data decomposition and pattern extraction as preliminary actions before real-time transaction processing. By pre-analyzing structured messaging data and identifying transaction patterns in advance, the system reduces the computational burden during real-time operations, enabling fast provisioning decisions without excessive processing delays.
Solution Approach 2:
The system applies partial analysis to message data by focusing only on specific fields and elements that are most predictive of future transactions. Rather than processing entire message structures in full detail, the system selectively analyzes relevant data portions, reducing processing time while maintaining sufficient accuracy for real-time financial product provisioning.
3Measurement precision
If the system uses decomposed structured messaging data to predict transactions, then prediction accuracy is improved, but data processing complexity increases
Solution Approach 1:
The system segments structured messaging data into discrete elements and fields, decomposing complex ISO 20022-compliant messages into manageable components. This segmentation enables precise extraction of transaction characteristics from specific message fields while avoiding the complexity of processing entire message structures, thereby improving prediction accuracy through focused data analysis.
Solution Approach 2:
The system extracts only the essential and predictive elements from structured messaging data, taking out specific fields that characterize transactions without retaining the complete message structure. This extraction approach improves prediction accuracy by concentrating on relevant data while reducing processing complexity by eliminating unnecessary message components.
Data Source
AI summary
The disclosed embodiments include computer-implemented systems and processes that generate and provision, in real time, directed digital content based on decomposed structured messaging data. For example, an apparatus may receive a plurality of messages that characterize first data exchanges initiated between a first counterparty and second counterparties during a first temporal interval. Each of the messages includes elements of message data associated with a real-time payment requested from the first counterparty by a corresponding one of the second counterparties. Based on the elements of message data, that apparatus may predict an occurrence of a second exchange of data that involves the first counterparty during a second temporal interval, and may transmit notification data that includes product data characterizing an available product associated with the predicted occurrence of the second data exchange to a device operable by the first counterparty for presentation within a digital interface.