How AI is advancing the securities and commodities industry | morgan lewis

Artificial intelligence (AI) is transforming the global financial services industry, including by helping financial institutions offer innovative new products, increase revenue through efficiencies, and improve customer service. Within the securities and commodities industry, AI-based applications are advancing the sector in customer interactions, investment and trading processes, market surveillance, and operational functions. To operate, AI technology needs three components: data, algorithms, and human interaction.


Customer Interactions: Virtual assistants can be programmed to perform simple digital customer service tasks, including tracking account balances and portfolio holdings, market data, address changes, and password resets. Other functions include selecting and clarifying income, sending client emails, and conducting targeted outreach activities to clients based on their investment behaviors.

With any widespread use of technology, there are a number of issues to consider, including how to maintain customer privacy, eliminate scheduling bias, and avoid instances where actors use technology to commit fraud. Other issues to consider are the customer authentication process, cybersecurity needs, and fair and accurate record keeping.

Accounts administration: Client profiles can be created and analyzed based on their assets, both within the investment firm and externally, as well as their spending patterns, debt balances obtained through data aggregation tools, social media updates, and other public websites, company browsing history. website and mobile applications, and prior communications. AI-based tools can also provide client social media data and sentiment analysis related to investment products and asset classes.

Portfolio Management: New patterns can be identified, possible price movements of specific products or asset classes can be predicted, and satellite activity can be interpreted to improve portfolio management. A significant trend over the last decade has been the introduction of automated advisors (roboadvisers) that use algorithms to deliver advisory services over the Internet.

Commerce: AI can help with intelligent order routing, price optimization, best execution and optimal block transaction allocations, in addition to automated algorithmic trading. With a number of proposed rules and open comment periods from the US Securities and Exchange Commission (SEC), any rulemaking will have the potential to generate significant data sets for the SEC to use in monitoring markets. and market participants.

Surveillance and Monitoring: AI can capture and monitor large amounts of structured and unstructured data in various forms, such as text, speech, voice, image, and video data, from internal and external sources, to identify patterns and anomalies. AI has the ability to decipher tone, jargon, and keywords. To improve market surveillance, AI could be used for risk-based predictive surveillance.

Know your customer and customer follow-up: Machine learning, natural language processing, and biometric technologies could be deployed to detect potential money laundering, terrorist financing, bribery, tax evasion, insider trading, market manipulation, and other fraudulent or illegal activities that continue to be threats. for the industry.

Regulatory Intelligence: New and existing regulatory intelligence can be digitized, reviewed, and interpreted, including rules, regulations, compliance actions, and no-action letters, and appropriate changes can be incorporated into compliance programs. Regtech is on the rise, as evidenced by the Financial Industry Regulatory Authority (FINRA) initiative to provide a machine-readable rulebook.

Liquidity and Cash Management: AI systems can be used to identify trends, note anomalies, and make predictions; for example, in relation to intraday liquidity needs, maximum liquidity demands and working capital requirements.

Credit Risk Management: AI systems can be used to provide more accurate and fair credit risk assessments by retrieving vast amounts of data not used in traditional credit reports, including personal cash flow, payment application usage, on-time utility payments and other data hidden in large data sets.


Financial regulators are increasingly turning to AI to improve and optimize their processes and systems. Through technological advances, regulators have more efficient monitoring methods and the ability to collect broader ranges of data sets, perform more extensive analysis, and make compliance more cost-effective for financial institutions.

The use of AI is changing the regulatory landscape from a static rule-based paradigm to a dynamic risk-based paradigm.


Released in October 2022, the FINRA (FIRST) Rule Book Search Tool is a machine-readable rulebook through the creation of an embedded taxonomy, a method of classifying and categorizing a hierarchy of key terms and concepts, which has been applied or “tagged” to the 40 most frequently viewed FINRA rules, making it that allows users to narrow down the universe of potentially applicable rules through sophisticated search filters. The comment period runs through February 21, 2023.

When it comes to FINRA Exam PrioritiesAI may review disclosures, complaints or employment history data to help staff determine which registered representatives to review.

FINRA has begun using deep learning to Market manipulation surveillance. to cope with changing market conditions, increased volatility, increased volumes and changing behavior in order to protect investors and ensure market integrity. Working closely with the SEC and stock exchanges, FINRA plays “a central role in conducting continuous monitoring within and across markets, monitoring misconduct and promptly intervening” once it is discovered. By being able to react faster, FINRA believes it is using deep learning to make the market more secure.


In July 2022, the Commodity Futures Trading Commission (CFTC) announced that LaboratoryCFTCa unit focused on “efforts to promote responsible fintech innovation and fair competition,” will be restructured to “assume a new identity” as the Office of Technology Innovation (OTI) and serve as the “fintech innovation hub of the CFTC, Driving Change”. and improve knowledge through innovation, consultancy/collaboration and education”.

As a market regulator, the CFTC could take advantage of AI to distinguish prominent activities, use data to develop market models, and identify risk factors.

In December 2020, the CFTC adopted a final rule addressing electronic trading risk principlesmarking a shift toward a principles-based approach to regulating automated trading compared to previous CFTC regulatory efforts.


Without any official guidance, financial agencies are likely to regulate AI through the app. The CFTC has filed several cases related to phishing, and the SEC has launched enforcement actions involving governance over an investment model’s algorithm and against digital advisors for misleading disclosures in marketing materials.


While various organizations have proposed AI frameworks, an investment firm has some flexibility in creating an AI compliance framework. Some frameworks use guiding principles that include governance, performance, and monitoring data.

When addressing how to build an AI compliance program, a company should take an inventory of existing AI systems; assess where future AI systems will be used; evaluate existing policies or establish new AI-specific policies; assign responsibility or designate a role to manage AI initiatives and ongoing monitoring; keep records, including from third-party systems; and be prepared to respond to regulatory inquiries or discuss AI systems with regulators.

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