Trade Settlement Failure Prediction With ML Risk Reasoning
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Solution Overview
Problem
Capital markets firms face significant challenges in monitoring and tracking high-risk trades due to potential settlement failures, which is both time-intensive and costly, often leading to unnecessary resource expenditure on trades that do not settle or require unnecessary monitoring of trades that will settle.
Innovation Solution
A machine learning model is employed to predict the probability of trade settlement failure and the most likely reason for such failure, providing alerts and reducing resource expenditure by automating monitoring and mitigation processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual monitoring and tracking of all high-risk trades is conducted, then settlement failure detection capability is improved, but resource expenditure (time and cost) increases significantly
Solution Approach 1:
The system enables self-service by automatically monitoring and tracking trades using machine learning models that predict settlement failures without requiring continuous manual intervention. The model independently analyzes trade data, identifies at-risk trades, and generates alerts, allowing the system to serve itself rather than relying on support team agents for every monitoring task.
Solution Approach 2:
The patent replaces the mechanical manual monitoring system with an automated machine learning-based system. Instead of human agents manually reviewing trade data, the system uses trained models that process trade information, predict settlement failures, and generate alerts automatically, substituting human mechanical effort with computational automation.
2Measurement precision
If automated monitoring of all trades is implemented, then settlement failure prediction accuracy is improved, but computational resources (computing time, memory, storage) increase
Solution Approach 1:
The system applies local quality by focusing computational resources only on trades that are predicted to be at-risk of settlement failure. Instead of uniformly processing all trades with the same level of computational intensity, the model identifies specific trades requiring attention and directs resources accordingly, making the computational effort proportional to the actual risk level of each trade.
Solution Approach 2:
The patent implements partial action by not monitoring all trades with equal intensity. The machine learning model processes trade data to identify a subset of at-risk trades that require detailed monitoring and intervention. This partial focus on high-probability failure cases rather than exhaustive monitoring of all trades reduces overall computational resource consumption while maintaining high prediction accuracy for critical cases.
Data Source
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AI summary
A computer-implemented method for transaction settlement prediction may include receiving data for a plurality of past financial trades, training a machine learning model using the data for the plurality of past financial trades, receiving one or more parameters for a subject financial trade among a plurality of recently executed financial trades, determining a likelihood that the subject financial trade will fail using the trained machine learning model, determining a most likely reason that the subject financial trade will fail using the trained machine learning model, and presenting the likelihood that the subject financial trade will fail and the most likely reason that the subject financial trade will fail to a user.