FINRA Publishes Report Detailing Uses of AI in the Securities Industry

By being able to react faster, FINRA believes it is using deep learning to make the market safer. As a business manager or security professional, you should stay updated on cyber security to get exposure to the best solutions for identifying and eliminating cyber threats. However, in order to achieve maximum social engineering detection, AI tools should be backed by a VPN that can detect phishing and cyber security awareness. Regular awareness training on new attacks can help your employees to be alert for social engineering tactics and to report upon detection. The media company implemented one of the most effective applications of artificial intelligence techniques to combat cybercrime which allowed employees to focus on service delivery. One of the most significant applications of machine learning in cyber security is in improving authentication.

AI Applications in the Securities Industry

We highlight a number of specific applications, including risk management, alpha generation and stewardship in asset management, chatbots and virtual assistants, underwriting, relationship manager augmentation, fraud detection, and algorithmic trading. These applications are particularly helpful when new regulations, such as the European Union Markets in Financial Instruments Directive II (MiFID II), increase senior management’s level of responsibility to review and consider higher-quality data from within the firm. However, if organisations do not exercise enough prudence and care in AI applications, they face potential pitfalls. These include bias in input data, process and outcome when profiling customers and scoring credit, and due diligence risk in the supply chain.

  • AI-backed authentication technologies analyze individual factors about login attempts to come up with a risk score for a scenario.
  • Gartner has identified the top 10 strategic technology trends for 2024 and generative and other types of AI solutions take center stage with widespread adoption and risks that are primary focus areas.
  • Courtesy of Graphus, the company significantly improved phishing prevention, one of the main applications of artificial intelligence in cyber security, and could now concentrate on customer service.
  • I’m Roshan, a 16 year old passionate about the intersection of artificial intelligence and finance.

Gartner estimates that AI is about two-five years away from mainstream adoption, for instance, and 75% of executives polled by the Economist Intelligence Unit think AI is so important they plan to implement in their business within the next three years. However, in 1997 the Deep Blue supercomputer from IBM beat the reigning chess champion and having reignited interest in AI, the turn of the millennium saw the emergence of robotics amid advances in sensor technology. Very recently, the emergence of neural networks has proven to be better than humans in tasks such as visual object recognition. In the 1950s, scientists laid the theoretical foundation for AI, but they did not have the computing power to build practical applications. Data bias, in general, may also be inadvertently introduced during the data preparation process, as data scientists determine which data fields and related features to incorporate in the ML model. Any biases in the underlying data may propagate through the ML model and may lead to inappropriate decision-making.

By 2027, Gartner predicts, more than 70% of enterprises will use industry cloud platforms (ICPs) to accelerate their business initiatives, up from less than 15% in 2023. ICPs address industry-relevant business outcomes by combining underlying SaaS, PaaS, and IaaS services into a whole product offering with composable capabilities. These typically include an industry data fabric, a library of packaged business capabilities, composition tools, and other platform innovations. ICPs are tailored cloud proposals specific to an industry and can further be tailored to an organization’s needs. Intelligent applications include intelligence — which Gartner defines as learned adaptation to respond appropriately and autonomously — as a capability.

However, more complex models (e.g., deep learning models) involve multiple layers and a dynamic, iterative learning process, where the internal learnings are opaque, making it difficult to identify the specific factors and their interrelationships that lead to the final outcome. Despite the challenges, these more complex AI models continue to garner interest from the industry AI Trading in Brokerage Business because they offer the potential to be more powerful in identifying patterns and making more precise predictions relative to simpler models. An appropriate level of explainability may be particularly important in AI applications that have autonomous decision-making features (e.g., deep learning-based AI applications that trigger automated investment decision approvals).

In addition, consider adding to your arsenal a VPN with additional protection against cyber threats. AI-backed authentication technologies analyze individual factors about login attempts to come up with a risk score for a scenario. For instance, it can flag a user attempting to log in from certain IP addresses in the middle of the night. Check out some of the best VPN services that can protect your dedicated and static IP addresses. A US-based healthcare service provider deployed an AI-powered threat detection system to augment its existing antivirus.

In fact, a study by Willis Towers Watson found that 90% of cybersecurity issues originate from human error. In addition, AI can be trained to understand cybersecurity threats well and provide protection against more advanced threats. Platform engineering is the discipline of building and operating self-service internal development platforms. Each platform is a layer, created and maintained by a dedicated product team, designed to support the needs of its users by interfacing with tools and processes.

EY refers to the global organization, and may refer to one or more, of the member firms of Ernst & Young Global Limited, each of which is a separate legal entity. Ernst & Young Global Limited, a UK company limited by guarantee, does not provide services to clients. Information about how EY collects and uses personal data and a description of the rights individuals have under data protection legislation are available via

AI Applications in the Securities Industry

In the security industry a number of vendors provide “black box” video analytics that can be used for nonscan detection at PoS and object recognition, but the big tech vendors are taking this further by offering cognitive services in the Cloud. Roger E. Barton is the managing partner of New York City-based Barton LLP and a litigator. He represents clients in the capital markets and financial services industries and is a fellow of the Litigation Counsel of America. He speaks nationally and internationally on law firm innovation and the practice of law as a business and can be reached at The 5 main applications of artificial intelligence in cyber security that we’ve discussed, already demonstrate how integral AI is in creating solutions to ensure cyber security. Social engineering detection is one of the most impactful applications of machine learning in cyber security.

AI Applications in the Securities Industry

As organizations prioritize digitization, the focus on cloud security is rapidly growing, but unfortunately, many organizations still consider data and application security as an afterthought, creating a void that bad actors can easily exploit. In addition, a lack of skilled cybersecurity resources restrict organizations from being a step ahead of these bad actors. Machine learning, a subset of artificial intelligence, focuses on developing computer programs that autonomously learn and improve from experience without being explicitly programmed.

While the definitions for AI discussed above provide a general outline of the meaning of the term, there is no single universally agreed upon definition of AI. In practice, AI is used as an umbrella term that encompasses a broad spectrum of different technologies and applications, some of which are described below. The use of AI is changing the regulatory landscape from that of a static, rule-based one into a dynamic, risk-based paradigm. Arbitration and mediation case participants and FINRA neutrals can view case information and submit documents through this Dispute Resolution Portal.

A recent incident involving Facebook revealed insights into its research experiment with chatbots. As it happened, two chatbots developed their own language and started to communicate with each other, which had to be shut down immediately, possibly because they had deviated from the script. Each of these factors—and FINRA’s recommendations on potential areas for broker-dealers to consider—are summarized in the chart below. When it comes to FINRA’s exam priorities, AI can review disclosures, complaints, or employment history data to help staff determine which registered representatives to examine.

Financial regulators are increasingly turning to AI to enhance and streamline their processes and systems. 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. While these AI tools offer the potential to customize investment suggestions for customers, firms should be cognizant of potential concerns and challenges related to data privacy, use of corrupt or misleading data, and adapting to each customer’s unique circumstances. AI-backed systems can offer better context for faster response to security alerts and elucidate root causes for vulnerability mitigation measures to be put in place. Kellogg Executive Education empowers business leaders to foster growth in themselves, their teams and their organizations.