Real-time Payment Parameter Prediction via ISO 20022 Data Decomposition
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Solution Overview
Problem
Current payment systems, particularly for small businesses, face delays in transaction processing, making it difficult to provide real-time indications of demand for services and comparing service offerings across geographic regions, as existing payment rails rely on batch processing and limited data transmission, which hinders dynamic pricing adjustments.
Innovation Solution
A system that decomposes structured messaging data from ISO 20022-compliant Request for Payment (RFP) messages to extract detailed elements, analyzes current pricing and demand, and generates notifications for small businesses to adjust their pricing in real-time based on comparative data across multiple locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If batch processing is used for payment transactions, then system complexity is reduced and ease of operation is improved, but transaction processing speed deteriorates and real-time data availability is lost
Solution Approach 1:
The patent segments the payment data stream into individual transaction messages that can be processed independently in real-time. Each message is decomposed into structured elements (payer, payee, amount, timestamp) that can be analyzed separately, enabling parallel processing without requiring complex batch coordination mechanisms.
Solution Approach 2:
The patent introduces an intermediary processing layer that sits between payment initiation and settlement. This layer includes message decomposition engines, data normalization services, and analytical processors that transform raw payment messages into structured analytical data, enabling real-time processing while maintaining compatibility with existing payment infrastructure.
2Loss of time
If real-time data transmission is implemented, then demand indication timeliness is improved and dynamic pricing capability is enhanced, but data transmission volume increases and network bandwidth consumption worsens
Solution Approach 1:
The patent extracts only the essential elements needed for demand analysis from complete payment messages. Instead of transmitting entire message payloads, the system decomposes messages and transmits only critical fields (transaction amount, timestamp, geographic location, counterparty identifiers), significantly reducing data volume while maintaining analytical utility.
Solution Approach 2:
The patent applies different levels of data processing and transmission based on local requirements. High-volume transaction data is processed locally to extract demand indicators, while aggregated analytical results are transmitted to central systems. This hierarchical approach reduces overall network bandwidth consumption while maintaining real-time responsiveness.
3Measurement precision
If detailed message data decomposition is performed, then analytical precision is improved and demand measurement accuracy is enhanced, but processing complexity increases and computational resources are consumed
Solution Approach 1:
The patent transforms unstructured or semi-structured payment messages into standardized parameters with consistent formats and data types. By normalizing message decomposition outputs into uniform parameter structures (amount, currency, timestamp, location coordinates), the system achieves high measurement precision while simplifying subsequent analytical processing through parameter standardization.
4Loss of information
If comparative analysis across multiple geographic regions is performed, then market insight quality is improved and pricing optimization is enhanced, but data processing time increases and analytical complexity worsens
Solution Approach 1:
The patent merges transaction data from multiple geographic regions into unified analytical structures that enable comparative analysis. By consolidating decomposed message data from different locations into standardized regional aggregates, the system maintains complete market information while enabling efficient cross-region comparisons through unified data formats and centralized processing.
Data Source
AI summary
The disclosed embodiments include computer-implemented apparatuses and processes that predict, in real-time modifications to parameters based on structured messaging data. For example, an apparatus obtains (i) first elements of decomposed message data that characterize real-time payments requested by a first counterparty and (ii) a second elements of decomposed message data that characterize real-time payments requested by one or more second counterparties associated with the first counterparty. The apparatus determines a first value of a parameter of a data exchange based on the first elements of decomposed message data, and determines a second value of the parameter based on the second elements of decomposed message data. Based on the first and second parameter values, the apparatus generates information characterizing a modification to at least the first parameter value during a temporal interval, and transmit notification data that includes the information characterizing the modification to a device operable by the first counterparty.