AI Challenges for Securities Firms: Key Summer Reading From FINRA Carlton Fields


Each cluster is defined by the criteria needed to meet its requirements; that criteria are then matched with the processed data to form the clusters. An unsupervised model built using this input data will create one cluster of fish and another cluster of birds by grouping the data based on common features. The machine learning technique is regression modeling by which an equation is created to reflect the correlation between shrink and variables, such as refunds, inventory balance and voided transactions at PoS. Data-driven retailers are die-hard fans of this technique and report savings in the millions of dollars and rapid return on investment. AI technology has the potential to disrupt and transform supervisory functions within a broker-dealer. Firms may benefit from conducting an overall assessment of the functions and activities that are employing AI-based tools, and updating their supervisory procedures accordingly.

AI Applications in the Securities Industry

Only 40 people work on the trading floor of the firm, overseeing computers that employ algorithms to fill stock orders. Before we can understand AI’s applications to financial services, we must understand the technology itself. While the paper highlights certain regulatory and implementation areas that broker-dealers may wish to consider as they adopt AI, the paper does not cover all applicable regulatory requirements or considerations. FINRA encourages firms to conduct a comprehensive review of all applicable securities laws, rules, and regulations to determine potential implications of implementing AI-based applications.

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.

22 Please note that FINRA does not endorse or validate the use or effectiveness of any specific tools in fulfilling compliance obligations. FINRA encourages broker-dealers to conduct a comprehensive assessment of any compliance tools they wish to AI Trading in Brokerage Business adopt to determine their benefits, implications and ability to meet their compliance needs. Although artificial intelligence use in combating cybercrime is relatively new, it has tremendous capabilities that enhance life and business operations.

In so doing, we play a critical role in building a better working world for our people, for our clients and for our communities. Initially, a training data set with labeled input and output examples are fed to the algorithm (hence the name supervised). Then, the algorithm runs on the training set with its parameters adjusted until it reaches a satisfactory level of accuracy. The model is then trained on the labeled data of cats until it can recognize the patterns in the images of cats. As a result, the model would be able to predict if later images are showing cats or not cats by responding to the previously recognized patterns.

As many companies, including firms in the securities industry, race to implement AI-based tools into their service offerings and backend operations, it’s worth grappling with both the potential benefits and drawbacks of such technology. One of the most significant artificial intelligence applications in cybersecurity is in advanced threat detection. Unlike traditional signature-based threat detection systems, AI-based threat detection involves the observation of abnormal behavior. It can be used to reinforce cybersecurity best practices and minimize cyber threats through real-time monitoring and early detection of attacks.

  • As digital applications become more powerful and widespread, good governance and effective controls will play an increasingly important role.
  • In addition, continuous provision of new data, both in terms of raw and feedback data, may aid in the ongoing training of the model.
  • Organizations’ digital immune system (DIS) must be strengthened and resilient to detect and counter threats.
  • Through technological advancements, regulators have more efficient 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.
  • GWYN concluded that I wanted “a bunch of colors,” and offered appropriate recommendations.

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.

A brief history of AI helps to clarify how it has become a phenomenon today, with common applications that include computer vision, natural language processing, speech recognition and data mining. Industry participants noted that one of the most critical steps in building an AI application is to obtain and build the underlying database, such that it is sufficiently large, valid, and current. Depending on the use case, data scarcity may limit the model’s analysis and outcomes, and could produce results that may be narrow and irrelevant.

Users of AI analytics must have a thorough understanding of the data that has been used to train, test, retrain, upgrade and use their AI systems. This is critical when analytics are provided by third parties or when proprietary analytics are built on third-party data and platforms. There are also concerns over the appropriateness of using big data in customer profiling and credit scoring. In November 2016, for instance, a British insurer abandoned a plan to assess first-time car owners’ propensity to drive safely – and use the results to set the level of their insurance premiums – by using social media posts to analyse their personality traits. However, it is unclear how easily individuals can opt out of the sharing of their data  for customer profiling.

AI Applications in the Securities Industry

Regardless of how sophisticated an algorithm or model might be, an AI system can only be as good as the data it receives — quantity, completeness, relevancy, accuracy, and timeliness can all affect a system’s output. But even when a system is trained on quality data and is designed to be “bias-free,” algorithms can still sometimes skew results in unexpected ways. Firms may wish to review their AI-based investment tools to determine whether related activity may be deemed as offering discretionary investment advice and therefore implicate the Investment Advisors Act of 1940. Broker-dealers are also exploring and using AI applications within their portfolio management and trading functions. They use vulnerability assessment tools that are designed to detect loopholes in legacy systems. However, these tools cannot identify vulnerabilities especially in new technologies such as IoT and hybrid environments.

AI Applications in the Securities Industry

They could be used, for example, at a bank’s branch office to return a benchmark of customer satisfaction based on the collective emotion reflected in the faces of customers. While this sounds amusing, it illustrates how the years of academic research has paid off, and we now have artificial intelligence (AI) systems that can learn. Firms also should update their written policies and procedures with respect to customer data privacy, to reflect any changes in what customer data and information is being collected in association with AI applications, and how that data is collected, used, and shared. [4] Id. (“[t]he theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages”). As a market regulator, the CFTC could leverage AI to distinguish salient activity, use data to develop market models, and identify risk factors.

Once the model is left on its own to figure out the best approach to maximizing reward, it progresses from random trials to sophisticated tactics. For example, Google’s Alpha Go computer program trained to play the game Go and ended up beating the world champion. This was a huge achievement because there are 10¹⁷⁰ possible board configurations (more than the number of atoms in the known universe) and no computer program had previously beat a professional Go player.

Artificial intelligence in the security market is highly competitive and fragmented as many new companies are coming up with innovative technologies due to the rise in cyber attacks over the years. Artificial intelligence (AI) is a rapidly growing field of technology that is capturing the attention of commercial investors, defense intellectuals, policymakers, and international competitors. 41 Some examples of tools that reportedly assist in identifying potential bias in data include IBM’s AI Fairness 360 and Google’s Responsible AI Practices.

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