Artificial intelligence (AI) is reshaping the global financial services industry by helping financial institutions offer innovative new products, increase revenue through efficiency, and improve customer service, among other things. Within the securities and commodities industry, AI-based applications are advancing the sector in customer interaction, investment and trading processes, market surveillance and operational functions. AI technology needs three components to work: data, algorithms and human interaction.
AI APPLICATIONS IN THE SECURITIES AND COMMODITIES INDUSTRY
Interaction with customers: Virtual assistants can be programmed to perform simple digital customer service tasks, including monitoring account balances and portfolio holdings, market data, address changes and password resets. Additional features include screening and clarifying income, emailing clients, and conducting targeted customer outreach based on their investment behavior.
There are a number of issues to keep in mind with any widespread use of the technology, including how to preserve customer privacy, eliminate bias in programming, and avoid cases where the technology is used by actors to commit fraud. Other issues to keep in mind are the customer verification process, cybersecurity needs, and fair and accurate record keeping.
Account management: Customer profiles can be created and analyzed based on their assets, held both internally and externally, as well as their spending patterns, debt balances obtained through data aggregation tools, updates on social media and other public websites, browsing history on company websites. websites and mobile applications and previous communications. AI-based tools can also provide customer data from social media and related sentiment analysis about investment products and asset classes.
Portfolio management: New patterns can be identified, potential price movements of specific products or asset classes can be predicted, and satellite activity can be interpreted to improve portfolio management. A significant trend in the last decade has been the introduction of automated advisors – robo-advisors – that use algorithms to provide advisory services over the Internet.
trading: In addition to automated algorithmic trading, AI can help with intelligent order routing, price optimization, best execution and optimal allocation of block trades. 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.
Supervision 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, slang and code words. To improve market surveillance, artificial intelligence could be used for predictive risk-based surveillance.
Know your customer and customer monitoring: Machine learning, natural language processing and biometric technologies could be implemented to detect potential money laundering, terrorist financing, bribery, tax evasion, insider trading, market manipulation and other fraudulent or illegal activities that continue to be a threat to the industry .
Regulatory news: New and existing regulatory information can be digitized, revised, and interpreted, including rules, regulations, enforcement 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’s (FINRA) initiative to provide a machine-readable rulebook.
Liquidity and cash management: Artificial intelligence systems can be used to identify trends, record anomalies and make predictions; for example in relation to intraday liquidity needs, peak liquidity requirements and working capital requirements.
Credit risk management: AI systems can be used to provide more accurate and fairer credit risk assessments by retrieving a wealth of data not used in traditional credit reports, including personal cash flow, payment app usage, on-time utility payments and other data stored in big data sets.
REGULATORY APPLICATIONS, CONSIDERATIONS AND PERSPECTIVES
Financial regulators are increasingly turning to artificial intelligence to improve and streamline their processes and systems. Thanks to technological advances, regulators have more effective monitoring methods and the ability to collect wider ranges of data sets, perform more extensive analysis and make compliance more cost-effective for financial institutions.
The use of artificial intelligence is changing the regulatory environment from a static rules-based environment to a dynamic risk-based paradigm.
It was launched in October 2022 FINRA Rulebook Search Tool (FIRST) 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—that has been applied or “tagged” to FINRA’s 40 most frequently viewed rules, allowing users to narrow down potentially applicable rules through sophisticated search filters. The comment period runs until February 21, 2023.
As regards FINRA Exam PrioritiesAI can review disclosures, complaints, or employment history data to help employees determine which registered representatives to review.
FINRA started using deep learning for supervision of market manipulation address changing market conditions, increased volatility, increased volumes and changes in behavior to protect investors and ensure market integrity. Working closely with the SEC and stock exchanges, FINRA plays a “central role in conducting ongoing oversight within and across markets, tracking wrongdoing and taking immediate action” once it is detected. By being able to react more quickly, FINRA believes it is using deep learning to make the market safer.
This was announced by the Commodity Futures Trading Commission (CFTC) in July 2022. LabCFTCa 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 CFTC’s “change-driving financial technology innovation hub.” and expanding knowledge through innovation, consulting/collaboration and education.”
As a market regulator, the CFTC could use AI to discern major activities, use data to develop market models and identify risk factors.
In December 2020, the CFTC adopted a final rule regarding the resolution e-commerce risk principleswhich, compared to the CFTC’s previous regulatory efforts, marks a shift toward a principles-based approach to the regulation of automated trading.
Without any official guidance, financial agencies are likely to regulate AI through enforcement. The CFTC has filed several spoofing cases, and the SEC has filed enforcement actions over an investment model algorithm and against digital advisors for misleading information in marketing materials.
CONSIDERATIONS WHEN CREATING AN AI COMPLIANCE PROGRAM
While various organizations have designed frameworks for AI, an investment firm has some flexibility in creating an AI compliance framework. Some frameworks use core principles that include management, performance and monitoring data.
When deciding how to create an AI compliance program, a business should take stock of existing AI systems; assess where future artificial intelligence systems will be used; evaluate existing or create new AI-specific policies; assign responsibility or designate a role to handle AI initiatives and ongoing monitoring; keep records, including third party systems; and be prepared to respond to regulatory inquiries or otherwise discuss AI systems with regulators.
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