Artificial Intelligence AI in the Securities Industry

He has also mentored multiple start-ups and actively engages with a nonprofit institution that enables middle school girls to become future technology leaders. 46 These are some of many possible areas that broker-dealers may wish to consider as they explore adjusting their supervisory processes. This does not express any legal position, does not create any new requirements or suggest any change in any existing regulatory obligations, nor does it provide relief from any regulatory obligations.

Statistics for the 2023 Artificial Intelligence (AI) in Security market share, size and revenue growth rate, created by Mordor Intelligence™ Industry Reports. Artificial Intelligence (AI) in Security analysis includes a market forecast outlook to 2028 and historical overview. The Gartner annual top strategic technology trends research helps you prioritize your investments, especially in the age of AI. Gartner urges you to evaluate the impacts and benefits of each of these technology trends to determine which innovation — or strategic combination — will have the most significant impact on your organization’s success.

AI Applications in the Securities Industry

Generative AI (aka, GenAI) is becoming democratized by the confluence of massively pre-trained models, cloud computing, and open source — making these models accessible to workers worldwide. By 2026, Gartner predicts, over 80% of enterprises will have used GenAI APIs and models and/or deployed GenAI-enabled applications in production environments, up from less than 5% in early 2023. Large Language Models and other AI tools have tremendous promise and potential, but care and forethought are required to ensure that organizations optimize risk while maximizing value.

The experience of finance suggests that AI will transform some industries (sometimes very quickly) and that it will especially benefit larger players. Understanding the potential of AI and how it will be used in the security industry begins with the formative efforts of early pioneers. The low-hanging fruit is data mining using machine learning; for example, with loss prevention in department stores, grocers, wholesalers, etc.

The financial implications of cybercrime across the globe are dire and will continue getting worse unless better solutions are created. Organizations can use generative AI as a powerful tool to outsmart these bad actors, proactively identify the new threats and take corrective actions. As the frequency and severity of cybersecurity threats continue to rise, the likelihood of attacks also becomes increasingly high.

Potential benefits for investors include enhanced access to customized products and services, lower costs, access to a broader range of products, better customer service, and improved compliance efforts leading to safer markets. Potential benefits for firms include increased efficiency, increased productivity, improved risk management, enhanced customer relationships, and increased revenue opportunities. While this section of the paper provides a high-level informational summary of key AI applications that securities industry participants AI Trading in Brokerage Business shared with FINRA staff, it is neither an exhaustive list of possible applications nor intended to be an endorsement of any particular use case. Although the use cases noted below may offer several potential benefits, they also involve potential challenges, costs, and regulatory implications. Each firm should conduct its own due diligence and legal analysis when exploring any AI application to determine its utility, impact on regulatory obligations, and potential risks, and set up appropriate measures to mitigate those risks.

AI Applications in the Securities Industry

AI applications have also been leveraged to assist broker-dealers in tailoring customer content based on customer data. This includes challenges related to model explainability, data integrity, and customer privacy. Protection of financial and personal customer information is a key responsibility and obligation of FINRA member firms. Firms also should provide initial and annual privacy notices to customers describing information sharing policies and informing customers of their rights. Firms should assess the applicability of these laws as they build their AI applications and any underlying infrastructures. In a recent survey of IT leaders, concerns around generative AI included security risks (79%), bias (73%), and carbon footprint (71%).

AI Applications in the Securities Industry

Continuous threat exposure management (CTEM) is a pragmatic and systemic approach that allows organizations to evaluate the accessibility, exposure, and exploitability of an enterprise’s digital and physical assets continually and consistently. Aligning CTEM assessment and remediation scopes with threat vectors or business projects, rather than an infrastructure component, surfaces not only the vulnerabilities but also the unpatchable threats. By 2026, Gartner predicts that organizations prioritizing their security investments based on a CTEM program will realize a two-thirds reduction in breaches.

AI for security solutions involves the integration of endpoint data and analytics to gain threat intelligence, which aids in detecting and exposing an attack in a particular environment. With the growth in online transactions and a surge in NEFT, RTGS and mobile transactions are increasing the demand for security solutions. The banking sector noticed a significant rise in the adoption of artificial intelligence-based security solutions, which helped improve banking services. A specific request was made for comments about how FINRA can develop rules that support the adoption of AI applications in the securities industry in a manner that does not compromise investor protection and market integrity.

(3) Governance and oversight — Firms should create governance and risk management policies pertaining to the use of AI that will help ensure that investors’ interests are prioritized over those of the firm or individual advisors. The IAC also urges the SEC to create clearer guidance and best practices on the topic of AI. (1) Equity — Firms should consider the context of the data that is both being used to train AI models and that is being produced by these models, with an eye to identifying any implicit biases. The IAC suggests that firms seek multidisciplinary guidance from experts to assist with this.

  • The use of AI in applications to enhance customer experience has gained significant traction, not just in the securities industry but broadly within the financial services industry.
  • Reuters provides business, financial, national and international news to professionals via desktop terminals, the world’s media organizations, industry events and directly to consumers.
  • Using vulnerability detection features that can scan and predict risk across thousands of attack vectors and threats, you increase the chances of identifying many loopholes and the risk impact variations on your business.
  • Large Language Models and other AI tools have tremendous promise and potential, but care and forethought are required to ensure that organizations optimize risk while maximizing value.

With nearly 9 out of 10 IT leaders believing generative AI will have a prominent role in their organizations in the near future, business leaders must understand the strategic technology trends highlighted by Gartner for 2024 and beyond. In order to do this, businesses must commit to education, stakeholder reskilling, and strategic partnerships in order to ready themselves for a future that is led by AI-powered products and services. A number of broker-dealers are exploring the use of AI to target outreach to customers or potential customers. Some firms are using AI tools to analyze their customers’ investing behaviors, website and mobile app footprints, and past inquiries, and in turn, to proactively provide customized content to them. This could include curated educational information, news, and research reports on specific investment products or asset classes.

In the common lexicon, “AI” has become a general term for any computer system that solves problems by emulating the rational thought processes and decision-making capabilities of humans. Within this definition are a multitude of specialized (and often overlapping) AI architectures and applications, e.g., machine learning, natural language processing, robotics process automation, etc. Considering the massive costs of data breaches, both on operations and finances, it’s well worth investing your resources in acquiring AI-powered cyber solutions for maximum protection against all types of cyber threats.

The largest sales-generating tech markets in 2024 will be around IT services, software and communications services. Artificial intelligence has several diverse applications on both the sell side (investment banking, stockbrokers) and buy side (asset managers, hedge funds). The market sizes and forecasts are provided in terms of value (USD million) for all the above segments. The market size and forecasts are provided in terms of value (USD million) for all the above segments. By 2026, generative AI will significantly alter 70% of the design and development effort for new web applications and mobile apps.

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