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Generative AI in Insurance: Key Applications & Benefits


2023-06-02 Facebook Twitter LinkedIn Google+ FinTech


In addition, the massive demand for electricity impacts power generation and distribution (highly regulated industries) and the environment. These considerations introduce a high degree of uncertainty and complexity to the future development of GenAI. In October 2024, the Biden administration finalized rules restricting U.S. companies’ AI and related investments in China. Nevertheless, we suspect U.S. policymakers will be reluctant to aggressively regulate the space for fear of disadvantaging https://www.xcritical.com/ U.S. companies relative to AI researchers in other countries. Smaller companies may struggle to keep up with the investment requirements, potentially leading to a concentration of AI capabilities among a few large entities. Strategic partnerships, open-source collaborations and government support could help mitigate these financial barriers.

  • FINRA encourages firms to conduct a comprehensive review of all applicable securities laws, rules, and regulations to determine potential implications of implementing AI-based tools and systems.
  • Explore a new way to invest that combines big data, scientific research, and deep human expertise to make sense of market complexity.
  • Multiagent systems, also known as agentic systems, have been around for years but have been kicked into a higher gear in the past two years, thanks to the natural-language capabilities of generative AI (gen AI).
  • On the other hand, incorporating data from many different sources may introduce newer risks if the data is not tested and validated, particularly if new data points fall outside of the dataset used to train the model.
  • Also, where possible, try to stay aware of the health of the open source and upstream projects that create the components you are using.
  • It is not clear to us that it’s very widely used at the moment, say, for high-frequency trading.

What are some of the key regulatory concerns around AI adoption?

AI Applications in the Securities Industry

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. FINRA rules require firms to establish and maintain reasonable supervisory policies and procedures related to supervisory control systems in accordance with applicable rules (e.g., broker ai FINRA Rules 3110 and 3120). This includes having reasonable procedures and control systems in place for supervision and governance of AI-based tools and systems across applicable functions of a broker-dealer. The growth of AI in the financial markets will lead to more attention from regulators regarding the use of AI. Accordingly, broker-dealers and investment advisers should begin to assess their use of AI, including future use, and put in guardrails to ensure that their customers are protected. Next, firms should implement and periodically review their written policies and procedures to address AI governance and the regulatory risks posed by AI.

Manual Processes and Automation

This information is not intended to provide investment, tax or other advice, or to Stockbroker be a solicitation to buy or sell any securities. Sign up now to get industry-leading insights and timely articles delivered to your inbox. 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.

Why Is AI A Game-changer for The Securities Landscape?

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. It is not intended to 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 tools and systems. What is new in recent years is, of course, the arrival of generative artificial intelligence.

AI Applications in the Securities Industry

In our view, the future of AI looks promising, but we see six potential hurdles that could impede growth. 42 Deep fakes refer to a form of synthetic or manipulated communication in which an existing video, image, or audio clip is replaced or superimposed with someone else’s likeness in order to create false impressions or communications. Banks that unlock value from AI are making balanced investments across the entire AI capability stack. Envisioning this target-state AI stack is critical to ensuring that the right capabilities and innovations are built with an end goal in mind.

AI Applications in the Securities Industry

In traditional automated trading, you can go through the code, and you understand how the code works. In generative AI, you can’t trace back the code to decisions [that] are being taken. The lack of explainability, the magnitude of unpredictability and this lack of accountability in decision-making are certainly issues.

As the reach of AI expands across industries, this insight explores its impact and applications in investment management. Within BlackRock Systematic, AI and machine learning have played a pivotal role in our investment process for nearly two decades. We leverage these capabilities with the goal of continually shifting from the realm of qualitative to quantitative, increasing the breadth of what we’re able to measure in pursuit of more precise and differentiated investment outcomes.

Banks that extract value from AI view the technology as a transformational tool and use AI for core strategic priorities such as boosting revenue, differentiating the bank from competitors, and driving higher satisfaction for customers and employees. It is not easily verifiable for a user of AI [as to] what kind of information has been fed into the model and what is the relative importance of good data and malicious data. It is – in principle – possible that the models were calibrated to malicious data where some actors may want to manipulate the outcome of the model. It’s difficult to assess the extent [to which] your model is robust to such forms of manipulation. That is another area of concern, and I think the financial industry is working on that. But it’s certainly not an easy challenge to overcome due to the complexity and the magnitude of the models.

Despite the recent solid gains in AI-related stocks, we think tremendous financial and economic benefits have yet to be realized from the technology. 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. On the other hand, incorporating data from many different sources may introduce newer risks if the data is not tested and validated, particularly if new data points fall outside of the dataset used to train the model. In addition, continuous provision of new data, both in terms of raw and feedback data, may aid in the ongoing training of the model.

NuScale has entered into a partnership with the private investment platform ENTRA-1 and the private asset management firm Habboush Group to answer this problem. Both investment firms specialize in energy and infrastructure financing and operation. Even when small and modular, nuclear power plant projects are a major investment, with years of expenditure before starting to generate income from the energy generated, this makes their financing a task almost as crucial as the engineering and science itself. This in theory can be advantageously replaced by nuclear power plants, especially as the electricity generation is already a result of the production of ultra-hot supercritical steam by the reactor core. Founded in 2007, the company was very early in betting on SMRs, at a time when nuclear energy in general looked like it was on a trajectory of permanent decline, especially after the 2011 Fukushima incident.

As far back as the 1940s, an artificial intelligence (AI) technology revolution has forever been just around the corner. Today, with significant advancements in technologies like machine learning, natural language processing and computer vision, and new customer applications like virtual assistants, that revolution is in many respects here. Not with a single, loud bang, but rather through a steady march of more readily available and inexpensive computing power, new innovations like cloud storage and massive amounts of data from a myriad of sources. As a regulatory body focused on fostering innovation in a manner that’s safe for investors, FINRA has taken an active role in understanding and facilitating these technologies and their impact on broker-dealer businesses. FINRA’s review found broker-dealers primarily use AI to facilitate (1) customer communications and outreach; (2) investment processes; and (3) operational functions.

Implementation of AI in the world of securities, either through advanced machine learning, robo-advising, big data and natural language processing have carved and cemented the respectability of AI in the financial services industry. Broker-dealers and investment advisers have long used AI tools in the financial markets. AI-based applications have proliferated for uses such as operational functions, compliance functions, administrative functions, customer outreach, or portfolio management.

A transformation begins with one subdomain and the development of various use cases in that subdomain, moving through several phases, from minimum viable product to more sophisticated stages. As the transformation proceeds, reusable components from use cases in the first subdomain can be used in other subdomains (Exhibit 8). This process necessitates building and improving the AI stack in phases, as opposed to trying to create it all at once. A successful AI transformation of a bank balances delivering a positive financial impact in the near term with building lasting AI capabilities for the enterprise. Multiagent systems, also known as agentic systems, have been around for years but have been kicked into a higher gear in the past two years, thanks to the natural-language capabilities of generative AI (gen AI). Although they are still in a nascent phase, and much of the value they could generate remains hypothetical, multiagent systems are expected to improve over time.

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