2 AI Stocks That Could Create Lasting Generational Wealth The Motley Fool

We bring together passionate problem-solvers, innovative technologies, and full-service capabilities to create opportunity with every insight. For all its tantalizing potential to automate and augment processes, generative AI will still require human talent. Generative AI has the potential to transform Finance, and business, as we know it.

According to our analysis, the flows between core active funds are estimated to be more than three times that of net flows into passive funds (Exhibit 2 below). In other words, for every $1.00 outflow to a passive fund from an active fund, approximately $3.00 in flows between core active funds are available to be captured by active managers. The rise of passive investments at the expense of active management has been the single most disruptive trend to the asset management industry over the last 20 years. With such a vast array of applications and customizable capabilities, Generative AI can serve as a powerful tool for finance leaders to address key agenda items and realize strategic priorities and objectives for finance and controllership. For example, Deutsche Bank is testing Google Cloud’s gen AI and LLMs at scale to provide new insights to financial analysts, driving operational efficiencies and execution velocity.

The conversations we have been having with banking clients about gen AI have shifted from early exploration of use cases and experimentation to a focus on scaling up usage. The technology is now widely viewed as a game-changer and adoption is a given; what remains challenging is getting adoption right. So far, nobody in the sector has a long-enough track record of scaling with reliable-enough indicators about impact. Yet that is not holding anyone back—quite the contrary, it’s now open season for gen AI implementation and the learnings that go with it. Since customer information is proprietary data for finance teams, it introduces some problems in terms of its use and regulation.

The consultancy also anticipates that GenAI will transform customer interactions with financial institutions and revolutionize how routine tasks are performed. Without the right gen AI operating model in place, it is tough to incorporate enough structure and move quickly enough to generate enterprise-wide impact. To choose the operating model that works best, financial institutions need to address some important points, such as setting expectations for the gen AI team’s role and embedding flexibility into the model so it can adapt over time.

Deploy proprietary data as a strategic asset with the right data environment

In addition to incorporating models from OpenAI, Microsoft, and Google, this platform is refined with Goldman’s own data. At times, customers need help with specific issues that aren’t pre-programmed into existing AI chatbots Chat GPT or covered by the knowledge bases that customer support agents use. Business units that do their own thing on gen AI run the risk of lacking the knowledge and best practices that can come from a more centralized approach.

The EtonGPT integrates the ‘transactional capabilities’ of the company’s ERP platform with conversational AI functionalities. It will be available exclusively to AtlasFive users to enhance the productivity of their family offices. According to an AI and Financial Reporting Survey by KPMG, a majority of financial reporting leaders (65%) are already utilizing AI functions in their reporting workflows, while 48% have piloted or deployed some form of Gen AI solution. Fraudulent activities continually evolve, making it challenging for traditional monitoring systems to keep pace. This leaves financial service providers vulnerable to monetary losses and undermines customer trust.

This development is a big step in AI for market intelligence promising more efficiency and accuracy in research. Generative AI models, when fine-tuned properly, can generate various scenarios by simulating market conditions, macroeconomic factors, and other variables, providing valuable insights into potential risks and opportunities. Specialized transformer models help finance units in automating functions such as auditing, accounts payable including invoice capture and processing. With deep learning functions, GPT models specialized in accounting can achieve high rates of automation in most accounting tasks. In conjunction with proper data governance practices, privacy design principles, architectures with privacy safeguards, currently existing tools can help anonymize, mask, or obfuscate sensitive data, feeding into those systems and models.

Since everyone has investing goals and financial plans, you want to do your best to find specific advice that matches your expectations. You don’t want to be steered in the wrong direction because you took advice from a relative who didn’t understand your situation. It’s common to get financial advice from family and friends when you’re young, as these people instinctively want to help you. However, you must be realistic by assessing the track record of the person sharing the advice to determine whether it even applies to your situation.

Asset managers will always be beholden to market performance to some extent; however, a key question for managers is how to construct their operating model so that for any level of the market, operating margins remain as high and as resilient as possible. Therefore, it is not surprising to see many organizations announcing ambitious operational efficiency and cost programs with cost saving targets of 5–15%. Gen AI can act as an assistant or a coach to employees by helping them do their job more efficiently and ultimately enabling them to focus on strategic, high-impact activities. For example, coding assistance and generation, such as Codey, which is a family of code models built on PaLM 2, can dramatically increase programming speed, quality, and comprehension. Using gen AI can help address some of the most acute talent issues in the industry, such as software developers, risk and compliance experts, and front-line branch and call center employees.

Instead, CFOs should select a handful of use cases—ideally two to three—that could have the greatest impact on their function, focus more on effectiveness than efficiency alone, and get going. KPMG’s multi-disciplinary approach and deep, practical industry knowledge help clients meet challenges and respond to opportunities. The Deloitte AI Institute helps organizations transform through cutting-edge AI insights and innovation by bringing together the brightest minds in AI services. In the short term, generative AI will allow for further automation of financial analysis and reporting, enhancement of risk mitigation efforts, and optimization of financial operations.

Our report has been informed by interviews with senior executives of asset and wealth managers with approximately $21 trillion in combined assets under management (AUM). Below is an excerpt of our report, please click here for the full version of “The AI Tipping Point.” It is a large umbrella encompassing many technologies, some of which are already widespread in society and businesses and used daily. When we talk to digital assistants, use autocomplete, incorporate process automation tools, or use predictive analytics, we are using AI.

This ensures access to the latest methodologies and technologies while maintaining controls and standards. Centralized expertise typically comes from the team responsible for training proprietary models acting as a platform team. Centralizing AI infrastructure enables organizations to efficiently manage the complex, resource-intensive processes of training, fine-tuning, and developing proprietary AI models while achieving economies of scale. This consolidation streamlines data management, analytics, and model maintenance, reducing costs and complexity across the enterprise. Traditionally, financial planning was a tedious and time-consuming process, heavily dependent on human advisors. However, technology has dramatically transformed this landscape, automating and streamlining workflows, enhancing overall efficiency, and fostering greater trust and confidence among clients.

To read this article for free

In capital markets, gen AI tools can serve as research assistants for investment analysts. But to that same point of maximizing shareholder value, a CFO must recognize existential threats to a company’s businesses and be clear about the most important levers for generating and sustaining higher cash flows. When an opportunity squarely addresses or significantly relies on gen AI, CFOs should not shunt it aside because they don’t understand the technology or lack imagination to recognize the value it could create. Generative AI (gen AI) is a predictive language model that produces new unstructured content such as text, images, and audio. Traditional, or analytical, AI, by contrast, is used to solve analytical tasks such as classifying, predicting, clustering, analyzing, and presenting structured data.

This hybrid model offers a powerful strategic advantage, enabling organizations to maintain control while fostering agility. By centralizing core infrastructure and decentralizing application development, companies can navigate the complexities of AI adoption while maximizing its transformative potential. Jamir is an experienced professional with over 18 years in wealth management technology, specializing in digital solutions.

To thrive in the AI-driven future, organizations must position themselves at the forefront of innovation while ensuring robust governance and scalability by acting now to develop a nuanced strategy that leverages both centralized and decentralized elements. While Gartner’s research identifies significant challenges, it’s not all bad news for Gen AI. Some companies report they’ve already seen benefits from the technology, such as revenue increases, cost savings, and productivity lifts. For one, describing and marketing financial products to customers is often an uphill battle, both because of the complex nature of these products and because of strict regulatory oversight.

Professionals in fields such as education, law, technology, and the arts are likely to see parts of their jobs automated sooner than previously expected. This is because of generative AI’s ability to predict patterns in natural language and use it dynamically. Revenue from AMD’s client segment, including sales of PC processors, is exploding right now, with revenue up 49% year over year last quarter. Demand for AMD’s Ryzen central processing units (CPUs) should only grow in the years to come, as a new generation of AI-optimized PCs come to market.

The tool represents the first Large Transaction Model (LTM) powered by Generative AI for payments. It aims to revamp how transactions are monitored, promising a significant https://chat.openai.com/ leap in fraud detection. TallierLTM has proven to be remarkably effective, showing up to 71% improvement in identifying fraudulent activities over existing models.

At Oliver Wyman, we help our clients think critically about generative AI opportunities across the value chain, pilot and scale use cases, and set up programs and portfolios to deliver immediate and long-term impact. The intersection of wealth management and corporate and investment banking presents a range of revenue synergies and opportunities. Our analysis shows that wealth managers that can comprehensively serve these clients can unlock net new money of more than $200 billion across traditional wealth management and sophisticated wealth management and corporate and investment banking solutions. Artificial intelligence (AI) technologies are rapidly transforming today’s business models, and the emerging Generative AI and advanced applications are presenting new opportunities and possibilities for AI in finance and accounting. From Generative AI to machine learning and other foundation model solutions, we look at the new era of AI innovations, the tools they may offer accounting and finance, and considerations for incorporating an AI framework for success. In the financial services industry, new regulations emerge every year globally while existing rules change frequently, requiring a vast amount of manual or repetitive work to interpret new requirements and ensure compliance.

He leverages his deep understanding of digital innovation, automation, and problem-solving to deliver strategies that help businesses reduce costs and enhance efficiency. His expertise cuts through the complexities of technology and operations, offering practical solutions and innovative approaches to streamline processes. Through his thought leadership, Jamir has established himself as a trusted resource in the wealth management technology space. The initial focus for generative AI is overwhelmingly focused on driving efficiency gains versus directly expanding new revenue streams or driving alpha. It is important to note, however, that efficiency gains free up time and resources that can be reallocated to higher-value activities to support revenue-generating activities, enable better investment decisions, improve client engagement and experience.

• Overall, this is a conversation worth having as gen AI continues to drive public discourse.
• An effectively designed operating model, which can change as the institution matures, is a necessary foundation for scaling gen AI effectively.
• Helping product specialists identify gaps in the market and inform design of new products that meet market demand.
• Knowing the nature of the models and tools will only assist in bolstering defenses.
• Fraud management powered by AI raises security standards, safeguards client assets, strengthens brand image, and reduces the operational strain on the investigation teams.

Such innovations significantly improve client satisfaction through curated advice and proactive assistance. Ultimately, financial settings gain a competitive edge by offering a superior, personalized CX. Buyers increasingly demand tailored digital journeys and customized offers, posing a challenge for businesses with limited resources and traditional service approaches. Creating accurate and insightful financial reports is a labor-intensive, time-consuming process. Analysts must gather data from various sources, perform complex calculations, and craft digestible narratives, often under strict deadlines. Morgan Stanley is setting a new standard on Wall Street with its AI-powered Assistant, developed in partnership with OpenAI.

Data leaders also must consider the implications of security risks with the new technology—and be prepared to move quickly in response to regulations. However, it’s crucial to acknowledge hurdles such as security, reliability, safeguarding intellectual property, and understanding outcomes. Armed with appropriate strategies, generative AI can elevate your institution’s reputation for finance and AI. Successfully adopting generative AI requires a balanced approach that combines urgency and risk awareness. The finance domain can pave the way by establishing an organizational framework that is aligned with your company’s risk tolerance, cultural intricacies, and appetite for technology-driven change. As Generative AI rapidly advances, its implementation in finance brings some big hurdles and potential risks.

Generative AI models can be highly complex, making understanding how they arrive at certain decisions or recommendations challenging. This lack of transparency is particularly concerning in finance, where justifying AI-driven decisions is essential for regulatory compliance and customer trust. DocLLM is designed to process and understand complex business documents such as forms, invoices, and reports, while SpectrumGPT analyzes large volumes of documents and proprietary research, providing valuable insights to portfolio managers. These tools have significantly boosted document comprehension and operational efficiency, delivering a 15% performance improvement compared to more general technologies like GPT-4. With a strong understanding of the overall sentiment, financial institutions can quickly respond to changing public perceptions, anticipate market movements, and tailor their strategies to meet customer needs. Generative AI capabilities in generating synthetic data and enhancing model accuracy allow it to provide a more precise credit risk evaluation.

Generative AI plays a big role in helping finance professionals deliver personalized financial advice and tailor investment portfolios. By analyzing detailed customer information, such as transaction history, spending patterns, and financial goals, Generative AI algorithms can create personalized recommendations that cater to each customer’s unique situation. Generative AI systems do a good job of analyzing customer sentiment in-depth and precisely to effectively gauge public opinion on financial products, services, or trends in financial markets.

Explore more on how generative AI can contribute to software development and reduce technology costs, helping software maintenance. AI will be critical to our economic future, enabling current and future generations to live in a more prosperous, healthy, secure, and sustainable world. Governments, the private sector, educational institutions, and other stakeholders must work together to capitalize on AI’s benefits. There’s work to be done to ensure that this innovation is developed and applied appropriately. This is the moment to lay the groundwork and discuss—as an industry—what the building blocks for responsible gen AI should look like within the banking sector. While headlines often exaggerate how generative AI (gen AI) will radically transform finance, the truth is more nuanced.

Hexaware’s expertise in digital transformation ensures that financial institutions can efficiently implement and benefit from gen AI-driven financial planning solutions. AI plays a significant role in the banking sector, particularly in loan decision-making processes. It helps banks and financial institutions assess customers’ creditworthiness, determine appropriate credit limits, and set loan pricing based on risk. However, both decision-makers and loan applicants need clear explanations of AI-based decisions, such as reasons for application denials, to foster trust and improve customer awareness for future applications.

It also leads to faster turnaround times, boosted performance across operations, and a profound understanding of complex financial details. The need to handle redundant and time-consuming duties, such as manually entering data, and summarizing lengthy papers. A curated collection of Generative AI in Finance use cases designed to help spark ideas, reveal value-driving deployments, and set organizations on a road to making the most valuable use of this powerful new technology. By gen ai in finance leveraging its understanding of human language patterns and its ability to generate coherent, contextually relevant responses, generative AI can provide accurate and detailed answers to financial questions posed by users. Central to this issue is the difference between consumer LLMs and enterprise LLMs. In the case of the former, once proprietary data or intellectual property is uploaded into an external model, retrieving or gating that information is exceptionally difficult.

In our 2022 edition with Morgan Stanley, we discuss investment priorities for wealth and asset managers to successfully evolve to Wealth Management 3.0. Generative AI has rapidly transitioned from the realm of academic tinkering to practical testing and deployment in a broad array of industries, including asset and wealth management. Wealth managers possessing a premium brand and access to robust corporate and investment banking (CIB) capabilities have substantial opportunities within the high-net-worth (HNW) and ultra-high-net worth (UHNW) client segments. Money-in-motion (reallocations within the active space) create a battlefield for active asset managers that cannot be ignored.

In the overall average for global growth, generative AI adds about 0.6 percentage points by 2040 for early adopters, while late adopters can expect an increase of 0.1 percentage points. Since the release of ChatGPT in November 2022, it’s been all over the headlines, and businesses are racing to capture its value. Within the technology’s first few months, McKinsey research found that generative AI (gen AI) features stand to add up to $4.4 trillion to the global economy—annually. TikTok, Instagram, Facebook and X all fall under one umbrella here, as many young people are on these platforms. The biggest issue with taking financial advice on these platforms is that the content is often designed to drive views, which may compromise the integrity of the information shared. Adoption of AI PCs is a strong growth catalyst for AMD, considering its client segment makes up a quarter of total revenue.

We recently conducted a review of gen AI use by 16 of the largest financial institutions across Europe and the United States, collectively representing nearly $26 trillion in assets. Our review showed that more than 50 percent of the businesses studied have adopted a more centrally led organization for gen AI, even in cases where their usual setup for data and analytics is relatively decentralized. This centralization is likely to be temporary, with the structure becoming more decentralized as use of the new technology matures.

We see three initiatives that wealth managers can take to unlock net new money and drive profitable growth. An overreliance on gen AI and lack of understanding underlying analyses or data can also reduce the preparedness of finance teams to gut check “reasonable­ness” of outputs. It’s critical to bear in mind that gen AI is designed to enhance the productivity of people, not to replace them.

Of course, Lenovo’s AutoTwist is just a proof of concept, so you won’t be able to go out and buy one any time soon. However, I could see Lenovo integrating the technology into a special ‘AutoTwist’ edition of a ThinkBook or ThinkPad after more development. Artificial intelligence (AI) is creating tremendous new opportunities in software and computing hardware. The AI market is projected to grow at an annualized rate of 28% through 2030 to reach $826 billion, according to Statista. “Historically, many CFOs have not been comfortable with investing today for indirect value in the future. This reluctance can skew investment allocation to tactical versus strategic outcomes.” “Gen AI requires a higher tolerance for indirect, future financial investment criteria versus immediate return on investment,” said Sallam.

We have found that across industries, a high degree of centralization works best for gen AI operating models. Without central oversight, pilot use cases can get stuck in silos and scaling becomes much more difficult. Looking at the financial-services industry specifically, we have observed that financial institutions using a centrally led gen AI operating model are reaping the biggest rewards. As the technology matures, the pendulum will likely swing toward a more federated approach, but so far, centralization has brought the best results. The advanced machine learning that powers gen AI–enabled products has been decades in the making. But since ChatGPT came off the starting block in late 2022, new iterations of gen AI technology have been released several times a month.

The emerging technology also automates product development’s ideation and prototyping phases, significantly shortening the time needed for design iterations. Additionally, it simulates market demand, accurately predicting customer preferences and tailoring financial services accordingly. In this highly competitive financial sector, offering an individualized customer experience becomes essential if banks want to stand out.

No one should act upon such information without appropriate professional advice after a thorough examination of the particular situation. Helping clients meet their business challenges begins with an in-depth understanding of the industries in which they work. In fact, KPMG LLP was the first of the Big Four firms to organize itself along the same industry lines as clients. At Neurond, we specialize in helping organizations adopt Generative AI through precise planning, thorough research, and state-of-the-art technology. Our expert Generative AI consulting team provides tailored solutions to meet the unique needs of finance firms of all sizes.

An initial $11.0 trillion–$17.7 trillion could come from advanced analytics, traditional machine learning, and deep learning. You can foun additiona information about ai customer service and artificial intelligence and NLP. And additional $2.6 trillion–$4.4 trillion of incremental economic impact could be added from new generative AI use cases, resulting in a total use-case-driven potential of $13.6 trillion–$22.1 trillion. Centralization ensures consistent data quality, security, and compliance standards—critical factors for successfully developing and deploying reliable generative AI models. By unifying these resources, organizations can more effectively navigate the challenges of implementing AI technology while maximizing its potential benefits. Thus, the question isn’t “to be or not to be”; rather, it’s about when you will start utilizing Generative AI in finance.

Such capabilities not only streamline the retrieval of information but also significantly elevate client service efficiency. It is a testament to Morgan Stanley’s commitment to embracing Generative AI in banking. Furthermore, the company also positions itself as a leader in the industry’s technological evolution.

• Stocks don’t move up in a straight line, but the long-term growth of Palantir’s business should deliver massive returns for investors.
• As Generative AI rapidly advances, its implementation in finance brings some big hurdles and potential risks.
• Said they believed that the technology will fundamentally change the way they do business.
• “Gen AI requires a higher tolerance for indirect, future financial investment criteria versus immediate return on investment,” said Sallam.

To ensure success, prioritize information quality, explainable models, strong data governance, and robust risk control. We can partner with you to develop strategies that tackle any difficulties, enabling you to reap the transformative benefits of Gen AI. By learning from historical financial data, generative AI models can capture complex patterns and relationships in the data, enabling them to make predictive analytics about future trends, asset prices, and economic indicators. Despite the market rebound in 2023, the asset and wealth management industries still face a long-term shift in the macroeconomic environment. The Generative AI Tipping Point is our 2023 global wealth and asset management report with Morgan Stanley.

Data quality—always important—becomes even more crucial in the context of gen AI. Again, the unstructured nature of much of the data and the size of the data sets add complexity to pinpointing quality issues. Leading banks are using a combination of human talent and automation, intervening at multiple points in the data life cycle to ensure quality of all data.

For example, in this video, we explore how gen AI can speed up credit card fraud resolution — a win-win for customers and customer service agents. Foundational models, such as Large Language Models (LLMs), are trained on text or language and have a contextual understanding of human language and conversations. These capabilities can be particularly helpful in speeding up, automating, scaling, and improving the customer service, marketing, sales, and compliance domains. In fact, the old phrase that “to err is human; to really foul things up requires a computer” applies now more than ever. To start with, even the most cutting-edge gen AI tools can make egregious mistakes.

Now, let’s explore how finance leaders worldwide are actualizing these Generative AI benefits. With Generative AI, producing realistic and representative data for regulatory financial reporting also gets streamlined, making it easier for finance professionals to fulfill their reporting obligations accurately and quickly. It is easy to get buy-in from the business units and functions, and specialized resources can produce relevant insights quickly, with better integration within the unit or function. These dimensions are interconnected and require alignment across the enterprise.

Especially in financial services, where every service or product starts with a contract, terms of service, or other agreement. Gen AI is particularly good at discovering and summarizing complex information, such as mortgage-backed securities contracts or customer holdings across various asset classes. While demonstrated commercial success has largely come from digital natives, some traditional, nontechnology companies are moving aggressively as well.

The most successful banks have thrived not by launching isolated initiatives, but by equipping their existing teams with the required resources and embracing the necessary skills, talent, and processes that gen AI demands. Generative AI (gen AI) burst onto the scene in early 2023 and is showing clearly positive results—and raising new potential risks—for organizations worldwide. Two-thirds of senior digital and analytics leaders attending a recent McKinsey forum on gen AI1McKinsey Banking & Securities Gen AI Forum, September 27, 2023; more than 30 executives attended.