In early 2023, FINRA released its 2023 Examination and Risk Monitoring Program report, emphasizing the need to strengthen the trade and surveillance programs for its member firms. With a significant increase in financial crimes, member firms were encouraged to strengthen their compliance and reporting framework. The FINRA findings accentuate the need for member firms to implement robust supervisory procedures and effective surveillance controls to ensure compliance with regulations. Here are a few key points from the report:
- There were inadequate written supervisory procedures that failed to identify specific steps and individuals responsible for detecting and reporting manipulative conduct.
- Findings also highlighted gaps around non-specific surveillance thresholds that failed to reasonably design and implement surveillance controls.
- Surveillance-related deficiencies were found, including inadequate monitoring of customer activities for potential manipulation, failure to document review results, incomplete surveillance scenarios and rules, and insufficient training of responsible staff.
How Member Firms Should Respond
Member firms should proactively review and strengthen their trade surveillance programs in response to findings in the FINRA report. This includes ensuring adequate coverage of potentially manipulative trading practices such as layering, front-running, spoofing, wash sales and prearranged trading, as well as monitoring trading activity across trading platforms involving related financial instruments or correlated securities products. Member firms must also be able to detect layering and spoofing activities, identify transactions in cross-product securities, and monitor wash trading instances to collect liquidity rebates. In addition, member firms should implement a robust supervisory program to prevent front-running and trading ahead based on exchange-traded products (ETPs) and provide formal training programs for those with trade surveillance and reporting responsibilities.
Looking at these findings and establishing potential solutions, firms must enhance their data practices. Data can play a crucial role in improving trade surveillance at banks. By analyzing vast amounts of data, banks can detect and prevent fraudulent activities, comply with regulatory requirements, and ultimately reduce risk. Below are some use cases where streamlined data can be of tremendous value to firms.
1. Data Quality and Source: Data can often be siloed across various divisions and systems of a firm. Streamlining functional data at the source can provide remarkable value in the consistency of data quality that is being consumed by various stakeholders at the firm. It will lead to accurate transactional data being utilized to generate various compliance reports as well as assess illicit activities taking place. Having a strong data practice also arms the firm with the right advance analytics foundation, which will help with predictive analysis and machine learning (ML) in the future, to proactively mitigate any risks cause by unlawful trading activities, thereby maintaining market integrity.
2. Data Visualization: Data visualization can be a powerful tool in trade surveillance at a bank. It can help analysts in understanding, interpreting, and presenting large volumes of data in a visual format. This makes it easier to identify patterns and trends, detect potential risks, and make informed decisions. By leveraging the power of data visualization, banks can enhance their trade surveillance capabilities and maintain transparency of the financial markets. Below are a few examples of how data visualization can help strengthen a banks surveillance:
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- Dashboard reporting: Displaying the KPIs and metrics can provide a bird’s eye view of a client or a collective view of all clients and their trading activities, making it easier to identify trends and patterns that may require further investigation.
- Heat maps: Highlights areas of potential risk, such as clients or products with high transaction volumes or suspicious activity. By using color-coding, heat maps make it easy for analysts to quickly identify areas that require further scrutiny.
- Time series and network analysis: These tools assist in visualizing relationships between clients, products, and transaction data over time. By using data visualization techniques such as network diagrams, firms can analyze complex behavior for client relationships across their transactional history that may indicate potential risks like insider trading or market manipulation.
- Interactive visualizations: These are used to explore data in more detail into specific areas of interest. For example, firms can use interactive visualizations to explore data by region, product, or client, enabling them to identify potential risks more easily.
3. Reporting and Alerts: There are multiple mandatory reports that firms must produce to comply with various regulatory requirements, as outlined by the FINRA report. Having the right data practices can help firms successfully deliver the ever-changing reporting requirements that are becoming increasingly complex and stringent, while also managing their data effectively. Here are some examples of how reporting can be streamlined with the right data set:
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- Data governance: Banks can implement strong data governance practices to ensure the accuracy, completeness, and integrity of their data. This involves creating policies and procedures for data management, establishing data quality standards, and implementing controls to ensure data is accurate and reliable.
- Automated alerts: Notifications around conditions such as fraudulent activities by clients in trying to engage with multiple transactions below an established reporting threshold.
- Real-time monitoring: Banks can use real-time data feeds to monitor trading activities and detect anomalies immediately. This can help them identify and report suspicious trades before any illicit activities are performed.
4. Advanced Analytics, Artificial Intelligence (AI) and ML: AI and ML can prove to be a highly sophisticated and effective combination for the financial industry to identify predictive patterns. Once the data practices are solidified at a firm, over time, AI/ML algorithms can be utilized to analyze large amounts of data to identify patterns and anomalies that may indicate fraudulent activity. For example, an ML algorithm can be trained to detect trades that are outside of the norm for a particular trader or account. This is a powerful tool for detecting both internal and external fraudulent activities. Algorithms can also be used for risk profiling clients based on their historical data to flag risky transactions based on previous trading patterns. Through advanced analytics, firms can consume large amounts of data to identify potential risks that may have been missed by traditional surveillance.
Strengthening Member Firm Compliance and Reporting Framework with Data
Due to the complex nature of the financial markets today, there is a constant need to evolve a firm’s trade surveillance practices. In response, member firms should be prepared to adapt their compliance programs which include manipulative trading, money laundering, cybersecurity, fraud, and other potential areas of abuse. FINRA is expected to increase its focus on both trade surveillance and financial crimes, and member firms should proactively review their systems of internal controls to ensure they can respond to increased threats.
Firms should take a holistic approach to compliance and coordinate and share data between various compliance processes to avoid missing potential correlations between criminal threats. Apexon’s deep data expertise in the financial domain can help firms achieve their goal of collaborating across various divisions of the firm and using the same data ingestion as the single source of truth. Apexon’s experts can help build cutting edge data solutions, which can help banks cut costs, build efficiency, and service their regulatory needs. Having a cutting-edge and robust compliance regime is critical for all financial firms to ensure market integrity.
