Bridged Transaction Settlement Using AI Clearing Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The bridged transaction message system faces challenges such as premature settlement without a clearing message, leading to complex and time-consuming processes and potential financial losses due to currency exchange rate fluctuations.
Innovation Solution
A server system uses AI/ML models to generate a clearing score based on transaction and historical data, determining whether to hold or initiate settlement, thereby optimizing transaction processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If settlement is performed in the bridged transaction message system without verifying clearing message receipt, then transaction processing speed is improved, but financial loss increases due to premature settlement of uncleared transactions
Solution Approach 1:
The system performs preliminary assessment of clearing probability using AI/ML models before executing settlement. The clearing prediction model evaluates transaction features and historical data to predict whether a clearing message will be received, and only settlements with high clearing probability are processed, preventing premature settlement losses while maintaining efficient processing for valid transactions
2Reliability
If the bridged transaction system implements strict clearing verification before settlement, then financial accuracy is improved, but processing time increases due to complex error handling
Solution Approach 1:
The system performs preliminary assessment of clearing probability using AI/ML models before executing settlement. The clearing prediction model evaluates transaction features and historical data to predict whether a clearing message will be received, and only settlements with high clearing probability are processed, preventing premature settlement losses while maintaining efficient processing for valid transactions
Solution Approach 2:
The system replaces traditional rule-based error handling mechanisms with AI/ML-based clearing prediction models. Instead of complex if-then logic and manual error processing, the system uses machine learning algorithms to automatically assess clearing probability and make intelligent settlement decisions, reducing processing time while maintaining high accuracy
3Measurement precision
If AI/ML clearing prediction models are implemented in the bridged transaction system, then settlement accuracy is improved, but system complexity increases
Solution Approach 1:
The system introduces an intermediary clearing prediction service layer between the transaction processing system and the settlement execution system. This intermediary service contains the AI/ML models that assess clearing probability, allowing the core transaction system to remain simple while gaining intelligent prediction capabilities through the mediator layer
Data Source
AI summary
Methods and systems for intelligently settling bridged transactions are disclosed. The method performed by a server system includes receiving a payment authorization message associated with an ongoing payment transaction from an issuer server. Here, the ongoing payment transaction is a bridged transaction with the issuer server operating in a single message system and an acquirer server operating in a dual message system. Method includes extracting a transaction feature set from the ongoing payment transaction and accessing a historical trend feature set from a database. Method includes generating, by one or more clearing prediction models associated with the server system, a clearing score for the ongoing payment transaction based, at least in part, on the transaction feature set and the historical trend feature set. Method includes transmitting a hold transaction message to the issuer server based, at least in part, on the clearing score being lower than a predefined threshold.