Clearing Delay Estimation Using Machine Learning
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Solution Overview
Problem
In the four-party card ecosystem, issuers face difficulties in forecasting settlement liabilities due to variable clearing times, leading to challenges in optimizing funding and managing working capital, as the timing of settlement is often uncertain and can exceed 30 days.
Innovation Solution
A system and method that employs machine learning and artificial intelligence to estimate clearing delay times by analyzing historical data on transactions, transaction types, merchant types, and other factors, allowing for more accurate forecasting and optimization of settlement accounts by inserting clearing delay estimates into the payment transaction process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If issuers adopt a conservative transaction authorization profile to ensure sufficient funds for unpredictable settlement totals, then settlement fund availability is improved, but transaction approval rate deteriorates
Solution Approach 1:
The system performs preliminary estimation of clearing delay and settlement liability before transaction authorization. By using machine learning models to predict when clearing messages will be submitted and settlement liabilities will arise, issuers can proactively allocate funds and adjust authorization profiles in advance, rather than reacting to unpredictable settlement totals after transactions are approved
Solution Approach 2:
The system implements feedback loops where actual clearing and settlement data are continuously fed back into the machine learning models. This allows the system to learn from historical patterns, improve prediction accuracy over time, and dynamically adjust authorization profiles based on real-time fund availability forecasts, balancing reliability and productivity
2Measurement precision
If issuers forecast settlement liabilities with higher accuracy, then working capital management is improved, but system complexity deteriorates
Solution Approach 1:
The patent introduces machine learning models as intermediary components between the clearing message submission and settlement liability calculation. These models act as predictive intermediaries that process historical clearing patterns, transaction data, and settlement information to generate forecasts, thereby simplifying the overall forecasting system while improving accuracy through pattern recognition rather than complex manual calculations
Solution Approach 2:
The system implements self-service through automated machine learning models that continuously learn from and adapt to clearing and settlement patterns without requiring manual intervention. The models automatically update their predictions based on new data, reducing the need for complex human-managed forecasting processes while maintaining high accuracy
Data Source
AI summary
A clearing delay estimate may be employed that provides issuers with an accurate estimate for when a transaction will be cleared, thus providing more transparency to issuers to more efficiently manage funds. An algorithm may be employed to estimate clearance timing information and insert the timing information into communications with the issuer or other parties to a transaction involving a payment device. The estimated clearance timing information may be determined based on machine learning and other artificial intelligence techniques using historical data related to clearance timing for the particular entities involved in each transaction.