Trade surveillance is about to trade up has been saved
Analysis
Trade surveillance is about to trade up
Is AI the answer to more secure markets?
Artificial intelligence (AI) and machine learning (ML) are emerging as critical tools for helping maintain the integrity of financial markets, thus boosting investor confidence. With all that AI and ML can do—from uncovering suspicious trading patterns to reducing the volume of false alerts—both regulatory authorities and financial institutions are strongly considering these technologies as a possible answer to more effective trade surveillance, more powerful risk management, and more secure financial markets.
Common AI use cases in the financial services sector
- Risk management
- Regulatory compliance monitoring
- Personalization of financial investment advice
- Enhancement of customer experience
Augmenting trade surveillance programs with artificial intelligence and machine learning
Traditional trade surveillance has its limits. Mainly, the traditional part.
In the past, market surveillance has been rule-based, generating alerts from specific conditions that lead to specific actions. But while conventional tactics are simple, reliable, and easy to understand, they are also static. New markets are not. For example, in the blockchain and digital assets market, evolving threats and adaptability constraints have resulted in dangerous surveillance lapses.
Rule-based trade surveillance limitations
- New threats: New or even slightly modified manipulative patterns can flummox rule-based systems designed to tackle prescribed or preconditioned directives.
- Adaptability for cross-market and cross-product detection: Firms require models that cover many different asset classes and can identify cross-market and cross-product manipulation patterns.
- Maintainability: As rule-based systems are extended to cover more markets, asset classes, and potential patterns, more and more parameters are added, which creates a growing and operational challenge in managing, testing, and tuning hundreds or sometimes thousands of parameters. These operations become very costly and, at the same time, are prone to error.
The very model of market monitoring
To tackle the challenges of rule-based systems, regulatory authorities and financial institutions have been adding, or are considering, AI and ML models as they can provide more dynamic and robust surveillance both now and into the future.
Alternative solutions
New AI-powered methodologies and capabilities can help transform the effectiveness and scalability of trade surveillance.
- Network and behavioral analysis techniques to help reveal hidden patterns and identify potential market manipulation behaviors
- Holistic surveillance to monitor and cross-reference trading data
- Quantum computing for its ability to process and analyze large datasets at faster speeds
- Dynamic parameters to more accurately evaluate specific trading behaviors and help reduce false positives
- Integration of distributed ledger technology (DLT) with AI to deliver the security, trackability, and transparency of blockchain with the clarity and efficiency of AI-based surveillance systems
In addition to alternative solutions, many financial institutions are also considering the integration of AI into their current capabilities.
Benefits of AI and ML augmentation
Is AI-driven trade surveillance even viable right now?
Trade surveillance involves data from various sources, such as exchanges, trading platforms, and news feeds. While integrating this data with communication surveillance data can be complex, deploying different AI models, each excelling in various aspects of surveillance, can vastly improve the analysis of suspicious activity. For example, specific models can be trained for speech-to-text translation, analyzing news and market sentiment, detecting trade patterns, analyzing relationships in the market, and flagging unusual trading behavior.
However, AI adoption requires clear objectives for AI use in surveillance, human expertise for input selection and model maintenance, the right infrastructure, access to historical data, and adequate education across teams. Meeting these foundational requirements is key for a successful AI implementation in surveillance systems.
The regulatory perspective
While the banking and financial sector embraces AI and ML, regulators face a number of challenges in model explainability, which is crucial for data protection and customer privacy. Addressing these challenges requires a trustworthy AI framework that includes enhanced transparency, accountability for AI-driven decisions, implementing security measures to protect models from risks, and ensuring the reliability of these models for accurate outputs and error recovery.
So, is AI the future of secure markets?
Developing AI- and ML-powered models is vital to financial institutions that wish to establish a comprehensive surveillance strategy. And a clear AI framework with regulatory safeguards for data privacy, accountability, and reliability is crucial for implementing a trustworthy AI program. However, the implementation of AI in trade surveillance should be viewed as an evolving process, requiring continuous learning, testing, and enhancement.
The future of AI in surveillance is promising—and through ongoing collaboration between firms and regulatory authorities, it could help financial markets successfully mitigate risk and reap the benefits of AI well into the future.
To learn more about opportunities and obstacles of AI-driven trade surveillance, read our latest report or speak with one of our specialists.
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Roy Ben-Hur |
Adam Clarke |
Niv Bodor |
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Anand Ananthapadmanabhan |
David Isherwood |
Romit Deb Mookerjea |
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