95% Improved Trade Surveillance Alert Accuracy

95% Improved Trade Surveillance Alert Accuracy

Executive Summary

Granite Peak Advisors, a leading RIA managing over $8 billion in assets, struggled with an outdated trade surveillance system that generated a high volume of false positives. This resulted in significant wasted time and resources on unnecessary investigations, diverting attention from potentially genuine instances of market abuse. Golden Door Asset implemented an AI-powered solution to refine Granite Peak's trade surveillance algorithms, leveraging machine learning and enhanced data visualization. The result was a 95% improvement in alert accuracy, a reduction of 80% in false positives, and a savings of 50 hours per month in investigation time.

The Challenge

Granite Peak Advisors prides itself on maintaining the highest standards of regulatory compliance. However, their existing trade surveillance system, while well-intentioned, had become increasingly inefficient. The system, relying on static rules and thresholds, flagged a substantial number of "false positive" alerts – transactions that appeared suspicious on the surface but were ultimately legitimate.

Specifically, the system generated an average of 200 alerts per month. Upon investigation, approximately 160 of these alerts (80%) were determined to be false positives. This meant that Granite Peak's compliance team, a team of five individuals, spent an estimated 60 hours each month sifting through irrelevant data. Each alert required an average of 30 minutes to investigate, involving analyzing order books, researching client profiles, and contacting traders for clarification.

The sheer volume of false positives posed several critical challenges:

• Resource Drain: The significant time spent investigating false positives detracted from more important compliance tasks, such as proactive risk assessments and in-depth investigations of genuinely suspicious activity. The estimated cost of these wasted hours was $15,000 per month, considering the fully burdened cost of the compliance team.
• Increased Operational Risk: With resources tied up in false positives, the risk of overlooking actual instances of market abuse, such as insider trading or market manipulation, increased substantially. Even a single missed instance could lead to significant regulatory fines and reputational damage.
• Decreased Team Morale: The constant barrage of false alerts and the repetitive nature of the investigations led to frustration and decreased morale within the compliance team.
• Limited Scalability: As Granite Peak's assets under management continued to grow, the volume of trades increased, further exacerbating the problem of false positives and threatening the scalability of their compliance program. They projected a 20% increase in trades over the next year, which would have increased the alert volume to 240 per month.

A specific example highlighting the problem involved a large purchase of shares in XYZ Corp. by a client who had previously purchased smaller amounts of the same stock. The legacy system flagged this as potentially unusual activity, requiring an extensive investigation into the client's trading history, financial situation, and potential access to non-public information. Ultimately, the investigation revealed that the client simply decided to increase their position in a company they believed was undervalued.

The Approach

Golden Door Asset worked closely with Granite Peak Advisors' compliance team to develop and implement a solution that addressed the root causes of the false positive problem. Our approach involved several key steps:

1. Comprehensive Data Analysis: We began by conducting a thorough analysis of Granite Peak's historical trading data, alert data, and client data. This involved leveraging Kensho Technologies' data analytics platform to identify patterns and trends that contributed to the generation of false positives. We specifically looked at the features of trades that were marked as alerts and later deemed false positives, such as trade size, time of day, sector volatility, and client trading history.
2. Refined Rule Sets: Based on the data analysis, we refined the existing rule sets used by Granite Peak's trade surveillance system. This involved adjusting thresholds, adding new rules, and removing outdated rules that were no longer effective. For example, we adjusted the threshold for flagging large trades in highly liquid stocks, recognizing that larger trades are more common and less suspicious in those situations.
3. Machine Learning Model Development: We developed a machine learning model using Python and TensorFlow to identify and filter out false positives. The model was trained on Granite Peak's historical data, learning to distinguish between genuine instances of suspicious activity and harmless trading patterns. Features used in the model included trade size, volume, price volatility, order type, client risk profile, and historical trading behavior. We used a gradient boosting algorithm for its ability to handle complex, non-linear relationships in the data and its interpretability, allowing us to understand the factors driving the model's predictions.
4. Enhanced Data Visualization: We implemented enhanced data visualization tools to aid in alert investigation. These tools allowed compliance officers to quickly and easily visualize trading patterns, identify anomalies, and assess the context of potentially suspicious transactions. The tools included interactive charts, graphs, and heatmaps that provided a comprehensive view of trading activity.
5. Integration with NICE Actimize: We seamlessly integrated the refined rule sets, machine learning model, and data visualization tools with Granite Peak's existing NICE Actimize trade surveillance platform. This ensured a smooth transition and minimized disruption to their compliance operations. We used Actimize's API to receive trade data, run the machine learning model, and display the results in Actimize's interface.
6. Ongoing Monitoring and Refinement: We established a process for ongoing monitoring and refinement of the trade surveillance system. This involved regularly reviewing alert data, retraining the machine learning model, and adjusting the rule sets as needed to maintain optimal performance. We planned to re-train the model every quarter with the most recent data to account for changes in market dynamics and trading behavior.

Our strategic thinking centered around moving from a rules-based system, prone to rigid interpretations and false alarms, to an intelligent system that adapts to changing market conditions and individual client behavior. This allowed us to focus on the genuine anomalies that required investigation.

Technical Implementation

The technical implementation of the solution involved the following key components:

• Data Extraction and Transformation: We extracted historical trading data, alert data, and client data from Granite Peak's various systems using secure APIs and data pipelines. This data was then transformed into a format suitable for analysis and model training. The data transformation process involved cleaning the data, handling missing values, and creating new features. We also performed feature scaling to ensure that all features had a similar range of values, which improved the performance of the machine learning model.
• Machine Learning Model Development: We developed a machine learning model using Python and TensorFlow to identify and filter out false positives. The model was a gradient boosting machine, a supervised learning algorithm trained to predict whether an alert is a true positive or a false positive. We used cross-validation to ensure the model generalized well to unseen data. We used metrics like precision, recall, and F1-score to evaluate the model's performance.
• Integration with NICE Actimize: We integrated the machine learning model with Granite Peak's existing NICE Actimize trade surveillance platform using Actimize's API. This allowed us to automatically score each alert based on the model's prediction and prioritize alerts based on their risk score. We also implemented a feedback loop that allowed compliance officers to provide feedback on the model's predictions, which was used to retrain the model and improve its accuracy over time.
• Data Visualization Tools: We implemented enhanced data visualization tools using Tableau and Python libraries like Matplotlib and Seaborn. These tools allowed compliance officers to quickly and easily visualize trading patterns, identify anomalies, and assess the context of potentially suspicious transactions. The visualizations included interactive charts, graphs, and heatmaps that provided a comprehensive view of trading activity. We also created custom dashboards that summarized key metrics, such as the number of alerts generated, the percentage of false positives, and the average time spent investigating alerts.

The implementation also involved several financial calculations and methodologies, including:

• Sharpe Ratio Analysis: We incorporated Sharpe Ratio analysis into the alert scoring process to identify trades that deviated significantly from a client's typical risk-adjusted return profile. This helped us to identify potential instances of unauthorized trading or portfolio mismanagement.
• VWAP (Volume-Weighted Average Price) Analysis: We used VWAP analysis to identify trades that were executed at prices significantly different from the average price for that security over a specific period. This helped us to identify potential instances of market manipulation or best execution violations.
• Time-Series Analysis: We used time-series analysis to identify unusual patterns in a client's trading activity over time. This helped us to identify potential instances of insider trading or front-running.

Results & ROI

The implementation of Golden Door Asset's AI-powered trade surveillance solution yielded significant improvements for Granite Peak Advisors:

• 95% Improvement in Alert Accuracy: The system achieved a 95% improvement in alert accuracy, significantly reducing the number of false positives.
• 80% Reduction in False Positives: The number of false positives was reduced by 80%, from an average of 160 per month to just 32 per month. This equates to approximately 128 fewer false positives monthly.
• 50 Hours per Month Savings in Investigation Time: The compliance team saved an estimated 50 hours per month in investigation time. Each alert investigation time went from 30 minutes to an average of 6 minutes, as the system pre-filtered out the clear false positives.
• $12,500 Monthly Cost Savings: Reduced false positives translated into an estimated $12,500 monthly cost savings, freeing up valuable resources for other compliance initiatives. This is based on the fully burdened cost of the compliance team.
• Reduced Operational Risk: By significantly reducing the number of false positives, the system enabled the compliance team to focus on genuine instances of suspicious activity, reducing operational risk and improving overall regulatory compliance.
• Improved Team Morale: The reduction in false positives and the improved efficiency of the alert investigation process led to a significant improvement in team morale. The compliance team was able to focus on more meaningful and challenging work, leading to increased job satisfaction.
• Increased Scalability: The AI-powered solution provided a scalable framework for managing trade surveillance as Granite Peak's assets under management continued to grow. The system was able to handle the increased volume of trades without a corresponding increase in false positives.
• Improved Detection Rate: While reducing false positives, the enhanced system also improved the detection rate of genuine suspicious activity by 15%, as the compliance team had more time to focus on investigating the alerts that were truly important.

Granite Peak Advisors reported a significant improvement in their compliance program, attributing the success to the increased accuracy and efficiency of the Golden Door Asset solution. They were also able to reallocate resources to more proactive compliance measures, such as enhanced employee training and ongoing risk assessments.

Key Takeaways

Here are key takeaways for other RIAs considering similar improvements:

1. Embrace AI and Machine Learning: Machine learning algorithms can significantly improve the accuracy of trade surveillance systems by learning from historical data and identifying patterns that are difficult for humans to detect.
2. Focus on Data Quality: High-quality data is essential for effective trade surveillance. Ensure that your data is accurate, complete, and consistent.
3. Invest in Enhanced Data Visualization: Data visualization tools can help compliance officers quickly and easily understand trading patterns, identify anomalies, and assess the context of potentially suspicious transactions.
4. Integrate AI with Existing Systems: Seamless integration with existing trade surveillance platforms is crucial for a smooth transition and minimal disruption to compliance operations. Ensure that any AI solution can integrate with your current infrastructure.
5. Continuously Monitor and Refine: Trade surveillance is an ongoing process that requires continuous monitoring and refinement. Regularly review alert data, retrain machine learning models, and adjust rule sets as needed to maintain optimal performance.

About Golden Door Asset

Golden Door Asset builds AI-powered intelligence tools for RIAs. Our platform helps advisors reduce compliance costs and improve regulatory outcomes. Visit our tools to see how we can help your practice.