Machine Learning Settlement Delay Prediction
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Solution Overview
Problem
Financial service providers face challenges in accurately determining settlement delays for transactions, which affects cash flow management, liquidity, and the ability to make informed business decisions. Inaccurate settlement delays can disrupt operations and compliance with regulatory standards.
Innovation Solution
A predictive modeling system using machine learning to estimate settlement delays in transactions, considering factors like non-transfer days (holidays) that vary by country and year. The system trains on historical transaction data and adjusts predictions based on inferred non-transfer days to provide accurate settlement delay forecasts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to determine settlement delays, then the system is simple to operate, but the accuracy of settlement delay determination is poor
Solution Approach 1:
The system performs preliminary actions by training machine learning models on historical transaction data before actual settlement delay prediction is needed. This pre-training phase enables the system to make accurate predictions without complex real-time calculations, resolving the contradiction between accuracy and operational complexity.
Solution Approach 2:
Machine learning models serve as intermediaries between historical transaction data and settlement delay predictions. These models process and interpret complex patterns in the data, providing accurate predictions without requiring the downstream systems to directly handle the complexity of raw transaction data analysis.
2Productivity
If settlement delays are not accurately determined, then additional transactions and operations are required when settlement reports are received, but accurate determination requires complex predictive modeling
Solution Approach 1:
The system performs settlement delay prediction in advance before settlement reports are received. This preliminary action allows downstream processes to be properly configured and prepared, eliminating the need for additional corrective transactions and operations, thereby improving productivity without requiring complex real-time adjustments.
Solution Approach 2:
The system uses historical settlement data and actual settlement reports as feedback to continuously train and improve the machine learning models. This feedback mechanism enables the system to learn from past inaccuracies and improve prediction accuracy over time, reducing the need for additional operations while managing modeling complexity through iterative improvement.
3Measurement precision
If machine learning models are used to predict settlement delays, then prediction accuracy is improved, but the system requires training data and model maintenance
Solution Approach 1:
The system performs model training on historical transaction data in advance, before predictions are needed for actual transactions. This preliminary training phase consolidates the time investment for model development, allowing the system to then make rapid, accurate predictions without recurring training delays, thus improving prediction accuracy while managing time investment efficiently.
Data Source
AI summary
Aspects of the subject technology include obtaining a transfer request event associated with a transaction, the transfer request event indicating a request day of week and time of day, and, when the request time of day is past a pre-determined cutoff time, adjusting the request day of week to be a subsequent day. Aspects also include determining, using the transfer request event and based on a machine learning model trained on historical transaction data, a predicted transfer delay for the transaction, obtaining a set of relevant non-transfer days based on a comparison between the historical transaction data and a set of past non-transfer days, and, when one or more non-transfer days from the set of relevant non-transfer days occur within a time period from the request day of week and over the predicted transfer delay, adjusting the predicted transfer delay based on the one or more non-transfer days.