Gen AI use cases by type and industry Deloitte US

Now that we know what business value the technology proposes, it’s time to move on to discussing the strategies to manage the challenges we identified initially. At Master of Code Global, as one of the leaders in Generative AI development solutions, we have extensive expertise in deploying such projects. Generative AI can be used for fraud detection in finance by generating synthetic examples of fraudulent transactions or activities. These generated examples can help train and augment machine learning algorithms to recognize and differentiate between legitimate and fraudulent patterns in financial data. Firms are at very different points in terms of how well they are satisfying these success imperatives, but everyone is trying to move as fast as possible given the range of constraints the asset and wealth management industries face. Figuring out how to best deploy these capabilities will be a crucial determinant of an organization’s long-term success.

• AI is having a moment, and the hype around AI innovation over the past year has reached new levels for good reason.
• However, it’s crucial to acknowledge hurdles such as security, reliability, safeguarding intellectual property, and understanding outcomes.
• While gen AI is still in its early stages of deployment, it has the potential to revolutionize the way financial services institutions operate.
• Some or all of the services described herein may not be permissible for KPMG audit clients and their affiliates or related entities.

For the past few years, federal financial regulatory agencies around the world have been gathering insight on financial institutions’ use of AI and how they might update existing Model Risk Management (MRM) guidance for any type of AI. We shared our perspective on applying existing MRM guidance in a blog post earlier this year. If not developed and deployed responsibly, AI systems could amplify societal issues. Tackling these challenges will again require a multi-stakeholder approach to governance. Some of these challenges will be more appropriately addressed by standards and shared best practices, while others will require regulation – for example, requiring high-risk AI systems to undergo expert risk assessments tailored to specific applications. Imagine you’re an analyst conducting research or a compliance officer looking for trends among suspicious activities.

Measuring Generative AI ROI: Key Metrics and Strategies

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.

While existing Machine Learning (ML) tools are well suited to predict the marketing or sales offers for specific customer segments based on available parameters, it’s not always easy to quickly operationalize those insights. To fully understand global markets and risk, investment firms must analyze diverse company filings, transcripts, reports, and complex data in multiple formats, and quickly and effectively query the data to fill their knowledge bases. In today’s rapidly evolving landscape, the successful deployment of gen AI solutions demands a shift in perspective—that is, starting with the end user experience and working backward. This approach entails a rethinking of processes and the creation of AI agents that are not only user-centric but also capable of adapting through reinforcement learning from human feedback. This ensures that gen AI–enabled capabilities evolve in a way that is aligned with human input.

The Hybrid Approach: The Best of Both Worlds

How a bank manages change can make or break a scale-up, particularly when it comes to ensuring adoption. The most well-thought-out application can stall if it isn’t carefully designed to encourage employees and customers to use it. Employees will not fully leverage a tool if they’re not comfortable with the technology and don’t understand its limitations. Similarly, transformative technology can create turf wars among even the best-intentioned executives. At one institution, a cutting-edge AI tool did not achieve its full potential with the sales force because executives couldn’t decide whether it was a “product” or a “capability” and, therefore, did not put their shoulders behind the rollout. Banks that foster integration between technical talent and business leaders are more likely to develop scalable gen AI solutions that create measurable value.

According to a Gartner study, 80% of CFOs surveyed in 2022 expected to spend more on AI in the coming two years.2 With that investment, however, around two-thirds think their function will reach an autonomous state within six years. The combination of Generative AI with blockchain technology is expected to strengthen security, transparency, and efficiency in financial transactions while also cutting costs and optimizing processes. The solution has dramatically reduced the time required for developers to create AI applications from months to weeks. Notably, Microsoft’s GitHub Copilot, a key AI tool used on the platform, has enhanced developer productivity by 20%. This initiative, spearheaded by Chief Information Officer Marco Argenti, centralizes all of the firm’s proprietary AI technology on an internal platform known as the GS AI Platform.

Discover what’s next for the asset management industry with our annual 10 predictions looking ahead at 2023. Wealth managers can gain a competitive advantage and tap into a $600 billion AUM opportunity by adopting a strategic, data-driven approach to enhance their advisor recruitment efforts, which we’ve termed “moneyball” for advisor growth. To enable coverage of these client segments, a product range that combines best-in-class corporate banking and investment products is crucial. Additionally, the provision of linkages/relationships to potential investors, such as financial sponsors, is important. The 2022 market downturn once again showed that asset managers continue to face tremendous downside exposure to markets on the revenue side, but with stubbornly high/growing cost bases. Managers, particularly those with larger institutional client bases, who have faced persistent price deflation and service-level inflation, need to adopt more analytical and systematic approaches to help them counter these challenges.

Given the macroeconomic backdrop, our outlook for the asset management industry is for modest growth. We forecast total externally managed assets to grow at 7% from 2022 to 2027, or a more normalized rate of 3.6% when measured from 2021, driven mainly by private markets. It excels in finding answers in large corpuses of data, summarizing them, and assisting customer agents or supporting existing AI chatbots.

Moreover, company capital (or access to more capital) is finite, and projects compete with one another. For CFOs to maximize value creation, they must rank the company’s 20 to 30 most value-accretive projects regardless of whether they are AI-related. The Pareto principle always applies; usually a very small number of opportunities will deliver most of the company’s cash flows over the next decade. The CFO cannot let the highest-value initiatives wither on the vine merely because a competing project has “gen AI” attached to it. Sooner or later, shareholders have to pay for everything, and none of them should be on the hook for a gen AI premium. To further demystify the new technology, two or three high-profile, high-impact value-generating lighthouses within priority domains can build consensus regarding the value of gen AI.

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.

They can also explain to employees in practical terms how gen AI will enhance their jobs. Use the RFP submission form to detail the services KPMG can help assist you with. By submitting, you agree that KPMG LLP may process any personal information you provide pursuant to KPMG LLP’s Privacy Statement. It is the combination of a predominant mindset, actions (both big and small) that we all commit to every day, and the underlying processes, programs and systems supporting how work gets done. And since Finance draws upon enormous amounts of data, it’s a natural fit to take advantage of generative AI. Integrating Generative AI into existing financial systems is not straightforward.

© 2024 KPMG LLP, a Delaware limited liability partnership and a member firm of the KPMG global organization of independent member firms affiliated with KPMG International Limited, a private English company limited by guarantee. KPMG has market-leading alliances with many of the world’s leading software and services vendors. 2023 was a game-changing year for business, with an explosion of interest in generative artificial intelligence. 2024 is the year to experiment, prove value, and begin adoption of AI in finance.

On top of that, using AI-generated synthetic data provides a safe and controlled environment for testing compliance measures. Financial institutions are allowed to thoroughly assess their systems, processes, and controls. Business leaders are increasingly enthusiastic about Generative AI (GenAI) and its potential to bolster efficiency in almost every finance function. With this archetype, it is easy to get buy-in from the business units and functions, as gen AI strategies bubble from the bottom up. It can slow execution of the gen AI team’s use of the technology because input and sign-off from the business units is required before going ahead.

Financial services’ ERP solution get Gen AI top up

Indeed, one of the biggest misconceptions we find is the belief that it’s the job of the CFO to wait and see—or, worse, be the organization’s naysayer. Capital shouldn’t sit; it should be aggressively moved to fund profitable growth. The best CFOs are at the vanguard of innovation, constantly learning more about new technologies and ensuring that businesses are prepared as applications rapidly evolve.

Gen AI-powered advising leads to greater consumer satisfaction, stronger advisor-client relationships, and increased confidence in suggested decision-making guides. Integrating GAI for report generation frees up expert’s time for strategic analysis, reduces errors for greater accuracy, and accelerates the identification of key recommendations for boosting agility. Let’s now examine how companies across the globe are implementing generative solutions for competitive advantage. As highly regulated industry players, banks get regular requests from regulators.

Processes such as funding, staffing, procurement, and risk management get rewired to facilitate speed, scale, and flexibility. The second factor is that scaling gen AI complicates an operating dynamic that had been nearly resolved for most financial institutions. While analytics at banks have been relatively focused, and often governed centrally, gen AI has revealed that data and analytics will need to enable every step in the value chain to a much greater extent. Business leaders will have to interact more deeply with analytics colleagues and synchronize often-differing priorities.

Gen AI, along with its boost to productivity, also presents new risks (see sidebar “A unique set of risks”). Risk management for gen AI remains in the early stages for financial institutions—we have seen little consistency in how most are approaching the issue. Sooner rather than later, however, banks will need to redesign their risk- and model-governance frameworks and develop new sets of controls.

At Google Cloud, we’re optimistic about gen AI’s potential to improve the banking sector for both banks and their customers. Generative AI is creating new operational efficiencies and solutions to transform the insurance business model. Our joint Global Asset Management report with Morgan Stanley for 2020 provides an overview of most relevant trends as well as perspectives on Covid-19’s impact on the industry. Nevertheless, it should still outgrow other segments, ultimately accounting for 16% of global AUM by 2027 versus 12% in 2022. If you look at just a few of the Generative AI applications this model renders, it also becomes apparent why it has captivated the attention of both society and the business world across the spectrum of industries. Sometimes, customers need help finding answers to a specific problem that’s unique and isn’t pre-programmed in existing AI chatbots or available in the knowledge libraries that customer support agents can use.

This structure—where a central team is in charge of gen AI solutions, from design to execution, with independence from the rest of the enterprise—can allow for the fastest skill and capability building for the gen AI team. A new McKinsey survey shows that the vast majority of workers—in a variety of industries and geographic locations—have tried generative AI tools at least once, whether in or outside work. One surprising result is that baby boomers report using gen AI tools for work more than millennials. In this visual Explainer, we’ve compiled all the answers we have so far—in 15 McKinsey charts. You can foun additiona information about ai customer service and artificial intelligence and NLP. We expect this space to evolve rapidly and will continue to roll out our research as that happens. To stay up to date on this topic, register for our email alerts on “artificial intelligence” here.

Since gen AI can’t do math and can’t “create” out of thin air—instead, it’s constantly solving for a what a human would want—it can “hallucinate,” presenting what seems to be a convincing output but what is actually a nonsense result. Gen AI models can also produce wildly incorrect financial reports; the product appears flawless, but the line items don’t apply to the company and the math looks like it should sum but doesn’t. What seems like a real 10-K form on the first flip through may be wholly untethered from reality. The CFO is often a company’s de facto chief risk officer, and even when a company already has a separate risk team (as is the case, for example, with financial institutions), CFOs remain a key partner in helping to identify and mitigate risks.

Examples of Generative AI applications

In our experience, this transition is a work in progress for most banks, and operating models are still evolving. Generative AI applications are revolutionizing finance operations, automating routine tasks, fraud detection, risk management, and credit scoring, and bolstering customer service operations. Driven by advancements in machine learning models, increasing data volumes, and the need for cost efficiency, Generative AI is becoming integral to finance and banking. May 29, 2024In the year or so since generative AI burst on the scene, it has galvanized the financial services sector and pushed it into action in profound ways.

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.

Developers need to quickly understand the underlying regulatory or business change that will require them to change code, assist in automating and cross-checking coding changes against a code repository, and provide documentation. Instead, it’s the CFO’s role to allocate resources at the enterprise level—rapidly, boldly, and disproportionately—to the projects that create the most value, regardless of whether they are driven by gen AI. Similarly, in leading the finance function, the CFO can’t implement gen AI for everyone, everywhere, all at once. CFOs should select a very small number of use cases that could have the most meaningful impact for the function.

Clear career development and advancement opportunities—and work that has meaning and value—matter a lot to the average tech practitioner. The regulatory environment for GenAI, particularly in finance, is still evolving and varies widely across different regions. This lack of uniformity creates uncertainty for international financial institutions and can hinder the adoption of GenAI. As mentioned, generative AI relies on large, high-quality datasets to perform effectively. However, real financial data can be costly to obtain, fragmented across institutions, and restricted by privacy regulations, limiting the data available for training GenAI models. Generative artificial intelligence bridges this gap in customer service automation by excelling at analyzing, summarizing, and finding answers within large datasets.

They can also have difficulty going deep enough on a single gen AI project to achieve a significant breakthrough. It can be difficult to implement uses of gen AI across various business units, and different units can have varying levels of functional development on gen AI. Throughout the week students also had the opportunity to network with speakers to learn more from them outside the gen ai in finance confines of panel presentations and to grow their networks. Several speakers and students stayed in touch following the Trek, and this resulted not just in meaningful relationships but also in employment for some students who attended. For most of the technical capabilities shown in this chart, gen AI will perform at a median level of human performance by the end of this decade.

The bright spots in core active management have been limited, and the relentless trend toward passive has been driven by many factors; chief among them is that active management has not been able to consistently demonstrate its value-add. That said, we see significant opportunity ahead for firms that can capture share despite persisting secular challenges. For the first time in more than a decade, global household wealth shrank in 2022, but a rapid rebound is expected. Inflation, rising interest rates, heightened geopolitical tensions, and uncertainty regarding economic growth negatively affected wealth growth, leading to a decrease of approximately 4% in 2022. When looking at the emerging AI tools and their various generative applications, the opportunities they present to finance and accounting are tremendous.

There is an opportunity to significantly reduce the time it takes to perform banking operations and financial analysts’ tasks, empowering employees by increasing their productivity. CFOs typically aren’t software engineers, let alone practiced experts in predictive language models. Their first step should be to try out the technology to get a feel for what it can do—and where its limits are at the moment. Solutions such as OpenAI’s ChatGPT are available online, and other applications (including McKinsey’s Lilli) are already in use. Banks also need to evaluate their talent acquisition strategies regularly, to align with changing priorities. They should approach skill-based hiring, resource allocation, and upskilling programs comprehensively; many roles will need skills in AI, cloud engineering, data engineering, and other areas.

That kind of information won’t be easily available in the usual AI chatbots or knowledge libraries. Picking a single use case that solves a specific business problem is a great place to start. It should be impactful for your business and grounded in your organization’s strategy. Responsible use of gen AI must be baked into the scale-up road map from day one. Naturally, banks encounter distinct regulatory oversight, concerning issues such as model interpretability and unbiased decision making, that must be comprehensively tackled before scaling any application.

Let’s explore a few use cases and success stories before delving into actionable mitigation strategies inspired by these illustrations. Business can either rely on off-the-shelf large language models or fine-tune LLMs for their use cases. For instance, internal audit functions can be greatly enhanced by generative AI through automated analysis and reporting. As a fine-tuned generative model for finance, it outperformed other models by succeeding in sentiment analysis.

Gen AI is a predictive language model—a translator that

sits above existing unstructured data and seeks to generate content that a human would find pleasing. The data sets themselves first need to be rigorously processed and curated, just as data scientists prepare data lakes for advanced analytics and analytical AI. As the technology advances, banks might find it beneficial to adopt a more federated approach for specific functions, allowing individual domains to identify and prioritize activities according to their needs. Institutions must reflect on why their current operational structure struggles to seamlessly integrate such innovative capabilities and why the task requires exceptional effort.

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.

In a 2023 McKinsey survey, CFOs cited capability building and advanced technologies as the two most effective ways to build resilience in their organizations. For example, Bloomberg announced its finance fine-tuned generative model BloombergGPT, which is capable of making sentiment analysis, news classification and some other financial tasks, successfully passing the benchmarks. Financial institutions can benefit from sentiment Chat GPT analysis to measure their brand reputation and customer satisfaction through social media posts, news articles, contact centre interactions or other sources. Banks want to save themselves from relying on archaic software and have ongoing efforts to modernize their software. Enterprise GenAI models can convert code from old software languages to modern ones and developers can validate the new software saving significant time.

Traditional planning tools struggle to provide truly tailored recommendations, potentially resulting in generic advice that fails to fully consider individual necessities. With platform’s help, lenders can promise higher approval rates for these underserved groups. Thus, ZAML’s distinctive approach paves the way for more inclusive financial practices. At the same time, the solution aligns with regulatory standards through its transparent data modeling explanations.

By gaining insights into customers’ emotions and opinions, companies can devise strategies to enhance their services or products based on these findings. In the context of conversational finance, generative AI models can be used to produce more natural and contextually relevant responses, as they are trained to understand and generate human-like language patterns. As a result, generative AI can significantly enhance the performance and user experience of financial conversational AI systems by providing more accurate, engaging, and nuanced interactions with users. For instance, Morgan Stanley employs OpenAI-powered chatbots to support financial advisors by utilizing the company’s internal collection of research and data as a knowledge resource. In this article, we explain top generative AI finance use cases by providing real life examples.

Finally, scaling up gen AI has unique talent-related challenges, whose magnitude will depend greatly on a bank’s talent base. Leading corporate and investment banks, for example, have built up expert teams of quants, modelers, translators, and others who often have AI expertise and could add gen AI skills, such as prompt engineering and database curation, to their capability set. Banks with fewer AI experts on staff will need to enhance their capabilities through some mix of training and recruiting—not a small task. Generative Al’s large language models applied to the financial realm marks a significant leap forward. With generative AI for finance at the forefront, this new AI technology guides the path towards strategic integration while addressing the accompanying challenges, ultimately driving transformative growth. Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company limited by guarantee (“DTTL”), its network of member firms, and their related entities.

However, these client segments have complex needs that span beyond wealth management (WM) to include corporate and investment banking (CIB) services. Family offices serve complex investment needs and require customized investment solutions, as well as access to exclusive investment opportunities. Entrepreneurs and business owners present a sizable client segment and make up half of high-net-worth individuals (HNWIs) globally.

The dynamic landscape of gen AI in banking demands a strategic approach to operating models. Banks and other financial institutions should balance speed and innovation with risk, adapting their structures to harness the technology’s full potential. As financial-services companies navigate this journey, the strategies outlined in this article can serve as a guide to aligning their gen AI initiatives with strategic goals for maximum impact. Scaling isn’t easy, and institutions should make a push to bring gen AI solutions to market with the appropriate operating model before they can reap the nascent technology’s full benefits. As the deployment of generative AI becomes increasingly prevalent, organizations must carefully assess and mitigate the unique technological and usage risks and limitations inherent in the technology. Responsible deployment of generative AI tools requires that all stakeholders understand that generative AI is a capability in need of significant oversight.

Just as the smartphone catalyzed an entire ecosystem of businesses and business models, gen AI is making relevant the full range of advanced analytics capabilities and applications. Convolutional natural network is a multilayered neural network with an architecture designed to extract increasingly complex features of the data at each layer to determine output; see “An executive’s guide to AI,” QuantumBlack, AI by McKinsey, 2020. But scaling gen AI will demand more than learning new terminology—management teams will need to decipher and consider the several potential pathways gen AI could create, and to adapt strategically and position themselves for optionality. The nascent nature of gen AI has led financial-services companies to rethink their operating models to address the technology’s rapidly evolving capabilities, uncharted risks, and far-reaching organizational implications. More than 90 percent of the institutions represented at a recent McKinsey forum on gen AI in banking reported having set up a centralized gen AI function to some degree, in a bid to effectively allocate resources and manage operational risk.

Generative AI and other digital technologies are transforming the way work is done, and finance roles are no exception. Less than a year after generative AI tools became widely available, 24 percent of staff in financial https://chat.openai.com/ services companies were already using them in their work. Sentiment analysis, an approach within NLP, categorizes texts, images, or videos according to their emotional tone as negative, positive, or neutral.

And its performance will compete with the top 25 percent of people completing any and all of these tasks before 2040. “Above all, it’s crucial to remember that if you don’t have a unique view of the market, you’re just gambling with your money. Indexes and funds managed by experts will always out perform your ‘hot picks,’ and leaning on them is the safest way to ensure growth in the long term,” Panik said. Brion brought up how advice without context might not be relevant to the circumstance of the person asking for advice.