Trade Surveillance Model Activation for Compliance Coverage Gaps
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Solution Overview
Problem
Companies with limited compliance audit capabilities in trade compliance systems face challenges in detecting suspicious market activities due to disabled analytical models, leading to potential fines and penalties, and lack awareness of which models to enable for improved surveillance.
Innovation Solution
A system utilizing a trained machine learning model to identify suspicious market activities through shape detection metrics, enabling corresponding analytical models to confirm and alert users, thereby enhancing trade surveillance and compliance coverage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all analytical models are enabled in the trade compliance system, then trade surveillance coverage and detection capability are improved, but system resource requirements and operational complexity increase beyond most customer service level agreements
Solution Approach 1:
The system dynamically adjusts the set of enabled analytical models based on the specific characteristics of the market data being analyzed. Rather than statically enabling all models or a fixed subset, the system adapts which models are active depending on the detection needs, data types, and compliance requirements for each analysis session, optimizing both coverage and resource usage
Solution Approach 2:
The system changes the operational parameters of the trade compliance solution by selectively enabling or disabling analytical models based on detected suspicious activities and customer-specific compliance needs. This parameter adjustment allows the system to provide high surveillance coverage when needed while maintaining lower resource consumption during normal operations
2Productivity
If only a portion of analytical models are enabled due to budgetary and operational constraints, then system resource consumption is reduced, but trade compliance audit capability is limited and suspicious activities may go undetected
Solution Approach 1:
The system performs preliminary analysis using a core set of enabled analytical models to identify suspicious activities in market data. When potential compliance issues are detected, the system then selectively activates additional analytical models specifically tailored to investigate those suspected activities, ensuring thorough audit capability only when and where needed rather than continuously
Solution Approach 2:
The system automatically determines which analytical models to enable based on its own detection of suspicious patterns and the specific compliance requirements of the customer. Rather than requiring manual configuration or expert intervention to select appropriate models, the system self-adjusts its analytical capabilities to match the detected needs, optimizing both resource efficiency and detection effectiveness
3Reliability
If customers independently identify and enable disabled analytical models to improve detection capability, then compliance coverage can be enhanced, but customers may lack awareness of suspicious activities or uncertain which models to enable
Solution Approach 1:
The system provides feedback to customers about detected suspicious activities, the analytical models that were disabled, and the potential compliance issues that could be addressed by enabling specific models. This feedback loop empowers customers to make informed decisions about which disabled models to enable based on actual detected risks rather than guesswork or comprehensive resource allocation
Solution Approach 2:
The system acts as an intermediary between the available analytical models and the customer's compliance needs. It translates complex model selection decisions into actionable recommendations by analyzing the gap between current detection capabilities and identified risks, then guiding customers on which specific disabled models would address their particular compliance gaps
Data Source
AI summary
Systems adapted to provide trade surveillance and compliance coverage and methods, and non-transitory computer readable media, include providing to a machine learning model, trained to output an indication of whether suspicious activity has occurred, input data comprising market data for a unique financial instrument; generating an output based on the input data, using the trained machine learning model, the output comprising a shape detection metric that identifies a suspicious activity; identifying a set of analytical models that correspond with the suspicious activity identified by the shape detection metric, wherein the set of analytical models is enabled once identified; analyzing the input data using the identified set of analytical models to confirm the suspicious activity identified by the shape detection metric; and triggering, based on confirmation of the suspicious activity identified by the shape detection metric, a suspicious activity alert.