Move to the Edge: The Future of AI in the Palm of our Hands UBS Global

From modeling analytics to automating manual tasks to synthesizing unstructured content, the technology is already changing how banking functions operate, including how financial institutions manage risks and stay compliant with regulations. Too often, banking leaders call for new operating models to support new technologies. Successful institutions’ models already enable flexibility and scalability to support new capabilities. An operating model that is fit for scale-up is cross-functional and aligns accountabilities and responsibilities between delivery and business teams. Cross-functional teams bring coherence and transparency to implementation, by putting product teams closer to businesses and ensuring that use cases meet specific business outcomes.

In capital markets, gen AI tools can serve as research assistants for investment analysts. While several compelling use cases exist in which gen AI can propel productivity, prioritizing them is critical to realizing value while adopting the tech responsibly and sustainably. We see three critical dimensions that risk leaders can assess to determine prioritization of use cases and maximize impact (exhibit).

Discover more examples of how Generative AI in banking is transforming the landscape, along with strategic insights to realize its maximum capacity for your organization. Unlike the digital revolution or the advent of the smartphone, banks won’t be able to cordon off generative AI’s impact on their organization in the early days of change. It touches almost every job in banking—which means that now is the time to use this powerful new tool to build Chat GPT new performance frontiers. Karim Haji, Global Head of Financial Services, outlines why it’s such an exciting time for the financial services industry. Dialogue on multiple levels is necessary to establish reasonable expectations and clear up any potential misconceptions about the risks that gen AI models pose. Identifying and engaging with key stakeholders in the cloud and cybersecurity space will facilitate better security requirements.

Data leaders also must consider the implications of security risks with the new technology—and be prepared to move quickly in response to regulations. 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. 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. Banks and other financial institutions can take different approaches to how they set up their gen AI operating models, ranging from the highly centralized to the highly decentralized.

Optimize Workflows with Intelligent Information Management

For the majority of banking leaders, the question of how and where generative AI could deliver the biggest value still stands. Banks are increasingly adopting generative AI to elevate customer service, streamline workflows and improve operational efficiency. This adoption advances the ongoing digital transformation of the banking industry. Gen AI isn’t just a new technology buzzword — it’s a new way for businesses to create value. While gen AI is still in its early stages of deployment, it has the potential to revolutionize the way financial services institutions operate.

• Generative AI, powered by advanced machine learning models, including gen AI models, is revolutionizing the banking and financial sectors.
• This move follows a successful six-month trial where participating staff reported completing tasks 50% faster on average.
• Participants included IT decision-makers, business decision-makers, and CXOs from 1,000+ employee organizations considering or using AI.
• These will inevitably be double-edged, both in terms of facilitating attacks and defending against them.
• 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.

Banks may suffer losses if liquidity, credit, operational, and other risks are not appropriately handled. AI’s impact on banking is just beginning and eventually it could drive reinvention across every part of the business. Banks are right to be optimistic but they also need to be realistic about the challenges that come along with advancements in technology.

These algorithms simulate human-like interactions, offering empathetic answers and solutions that resonate with debtors, thereby reducing hostility and improving collection outcomes. Additionally, this technology can predict client responses and adjust strategies in real-time, optimizing the process and ensuring compliance with regulations. Generative AI for banking is a game-changer in the battle against fraudulent activities. By training on past instances of scams and continuously scrutinizing financial operations, it swiftly pinpoints unusual behavior and promptly notifies clients.

Insights

This insightful narrative underscores the growing influence of generative AI in enhancing customer engagement and operational efficiency in the banking and financial services industry. The transition to more advanced generative AI models represents a shift towards addressing the challenges traditional AI systems can’t grapple with. Some banks have already embraced its immense impact by applying Gen AI to a variety of use cases across their multiple functions.

Beyond customer service, generative AI in banking is also transforming fraud detection and risk management. By analyzing vast amounts of transaction data, AI models can identify unusual patterns that might indicate fraudulent activities. This proactive approach enables banks to mitigate risks more effectively, safeguarding customer assets. While using AI applications, data privacy and compliance with regulatory requirements are crucial for maintaining customer trust and meeting industry standards. Generative AI (gen AI) is poised to become a catalyst for the next wave of productivity gains across industries, with financial services very much among them.

• We see three critical dimensions that risk leaders can assess to determine prioritization of use cases and maximize impact (exhibit).
• Once companies have embedded gen AI in these roles and functions, they have seen a second wave of emerging use cases across other aspects of risk management.
• The assistant is named Olive and has had several significant impacts for the credit union.
• To enable at-scale development of decision models, banks need to make the development process repeatable and thus capable of delivering solutions effectively and on-time.
• What’s better, however, is when you can integrate genAI across a broader process.
• The success of interface.ai’s Voice Assistant at Great Lakes Credit Union is just one of many Generative AI use cases in banking that showcase the transformative impact of this technology.

Moreover, generative AI can adapt to evolving fraud patterns, continuously updating its detection algorithms to stay ahead of the curve. This proactive approach not only helps banks minimize financial losses but also fosters trust and confidence among customers, who can rest assured that their financial information is secure. Starting off small and driving quick wins will allow banks to assess their capabilities, recognize key challenges and considerations, and assess current and prospective partnerships or acquisitions to further scale. You can foun additiona information about ai customer service and artificial intelligence and NLP. Over time, banks should develop a comprehensive vision for the business, incorporating the full innovation portfolio and be ready to pivot in an agile way as AI technology continues to evolve rapidly.

Consider the benefits of gen AI automation in helping customers move to net zero. The tech can identify market trends and environmental impact from years of company reports. In turn, financial institutions can use that new information to find investment opportunities. The right operating model for a financial-services company’s gen AI push should both enable scaling and align with the firm’s organizational structure and culture; there is no one-size-fits-all answer. An effectively designed operating model, which can change as the institution matures, is a necessary foundation for scaling gen AI effectively.

Any engineering talent rethink needs to begin with an understanding of how gen AI will affect the product development life cycle (PDLC). The changes are likely to be significant and affect every phase of the life cycle (exhibit). Recent McKinsey research suggests that gen AI tools have almost twice as much positive impact on content-heavy tasks (such as synthesizing information, creating content, and brainstorming) as on content-light tasks (for example, visualization). Reimagining the engagement layer of the AI bank will require a clear strategy on how to engage customers through channels owned by non-bank partners. All of this aims to provide a granular understanding of journeys and enable continuous improvement.10Jennifer Kilian, Hugo Sarrazin, and Hyo Yeon, “Building a design-driven culture,” September 2015, McKinsey.com.

Integrating data-driven AI systems increases the risk of data breaches, requiring continuous monitoring and updates to protect sensitive customer information. Furthermore, AI models rely on accurate and up-to-date data to produce reliable results. Poor or incomplete datasets can lead to incorrect outputs, negatively impacting financial decision-making and customer trust. All organizations face inbound risks from gen AI, in addition to the risks from developing gen AI use cases and embedding gen AI into standard workplace tools.

To cut operational costs, banks can have gen AI models comb through large volumes of documents to identify important data or summarize them for review. The online payment platform Stripe, for example, recently announced its integration of Generative AI technology into its products. The mitigation solution is to have robust cybersecurity measures in place to prevent hacking attempts and data breaches. As for regulatory compliance, Gen AI itself provides banking and finance with an efficient means of keeping abreast of changing regulatory environments.

One more example is the OCBC bank, which has rolled out a generative AI chatbot for its 30,000 global employees to automate a wide range of time-consuming tasks, such as writing investment research reports and drafting customer responses. The staff had reported a 50% increase in productivity rate during the trial period. For example, Fujitsu and Hokuhoku Financial Group have launched joint trials to explore promising use cases for generative AI in banking operations. The companies envision using the technology to generate responses to internal inquiries, create and check various business documents, and build programs. They use the technology to recognize patterns in historical data to identify root causes of past events or define trends for the future. Such systems use predefined rules and are trained on structured data often stored in databases and spreadsheets.

We work with ambitious leaders who want to define the future, not hide from it. Prashant Kher,  Senior Director Digital Assets Strategy Lead, EY-Parthenon, Ernst & Young LLP and Zachary Trull, Financial Services Strategy EY-Parthenon, Ernst & Young LLP  were contributing authors for this article. Taking advantage of the transformational power of GenAI requires a combination of new thinking about a longstanding challenge for banks — how to innovate while keeping the lights on.

This archetype has more integration between the business units and the gen AI team, reducing friction and easing support for enterprise-wide use of the technology. To be useful, however, companies should treat skills as data rather than a document. The talent transformation starts with HR leaders developing a strategic workforce plan that’s built around skills. The new skills needed to use gen AI will affect how and what people do in their jobs, raising significant questions about how roles need to adapt and what oversight leadership must provide.

Another significant challenge is the integration of AI technologies within existing banking systems. Many banks operate with legacy systems that might not be compatible with new AI frameworks, which can create costly and time-consuming issues. In investment banking, generative AI can compile and analyze financial data to create detailed pitchbooks in a fraction of the time it would take a human, thus accelerating deal-making and providing a competitive edge. Generative AI can also automate time-consuming tasks such as regulatory reporting, credit approval and loan underwriting. For example, AI can quickly process and summarize large volumes of financial data, generating draft reports and credit memos that would traditionally require significant manual effort. 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.

These AI-driven platforms improve customer experience by providing instant responses and personalized interactions and streamlining numerous banking processes. Risk management is essential to avoiding financial disasters and keeping the business running smoothly. When trained on historical data, Generative AI can detect and identify potential risks and financial risks and provide early warning signs so that banks have time to adapt and prevent (or at least mitigate) losses. These include reshaping AI customer service, that employs AI for enhanced fraud detection, using machine learning to predict financial trends, and customizing banking services for individual needs.

This approach ensures that AI serves as a powerful tool to enhance banking operations without overstepping its limitations. AI solutions simulate natural language by using natural language processing (NLP). Banks (for example, Morgan Stanley) use these AI tools to supercharge fintech such as customer-facing chatbots.

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Interestingly, Gen AI itself can serve as a solution to the legacy infrastructure problem by accelerating the transition from legacy software and data storage, which previously seemed cost prohibitive. First, there is a risk of unintentional violation of privacy rights when collecting large amounts of client data for profiling and forecasting, even if the data is already publicly available. Gen AI could inadvertently reveal sensitive or personally identifiable information, such as personal identification details, transaction history, and account balances. Swedbank used GANs to detect fraudulent transactions.3 GANs are trained to learn legal and illegal transactions in order to detect the fraudulent ones by creating graphs that reveal their patterns. Our community is about connecting people through open and thoughtful conversations. We want our readers to share their views and exchange ideas and facts in a safe space.

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. With Generative AI still in its infancy, now is the time to learn how to implement it in your business.

In addition, Generative Artificial Intelligence can continually mine synthetic data and update its detection algorithms to keep up with the latest fraud schemes. This proactive approach helps banks anticipate fraudulent behavior before it happens. Like utilizing Generative AI in Insurance for fraud detection, banks can use it to track transactions in terms of location, device, and operating system. From there, bank personnel can review the suspicious behavior and decide if it deserves further investigation.

Incumbent banks face two sets of objectives, which on first glance appear to be at odds. On the one hand, banks need to achieve the speed, agility, and flexibility innate to a fintech. On the other, they must continue managing the scale, security standards, and regulatory requirements of a traditional financial-services enterprise. Intelligent information management—an evolution of existing software-as-a-service workflow optimization tools—builds on IT solutions clients may already have in place.

Conversational AI a subset of Artificial Intelligence, can enhance user accessibility by simplifying the provision of multilingual support through virtual assistants and aiding those with disabilities through text and voice navigation options. However, these can be costly to run and maintain, and in some cases, they aren’t very effective. Learn how Brazilian bank Bradesco is giving personal attention to each of its 65 million customers with IBM Watson.

Train, validate, tune and deploy generative AI, foundation models and machine learning capabilities with IBM watsonx.ai, a next-generation enterprise studio for AI builders. The efficiency of generative AI in summarizing regulatory reports, preparing drafts of pitch books and software development significantly speeds up traditionally time-consuming tasks. This feature improves operational efficiency and reduces manual workloads, allowing teams to focus on more strategic activities. QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges.

While a financial advisor could be the best source of information, Gen Z may not always be able to afford it and may not relate to a professional as they do to someone on social media. 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.

Authorities will likely expect firms to deploy advanced GenAI systems in areas like financial crime. Evolving regulations create uncertainty about compliance requirements and the liability risks banks could face. From a resiliency perspective, banks need to be prepared for hackers, fraudsters and other bad actors taking advantage of the power of GenAI. Because regulation is catching up, firms will need to think about how they build and enable systems that anticipate developments in regulation, rather than building processes that might be overtaken by restrictions. Similarly, banks looking to deploy must bear in mind regulators’ claims that existing rules will apply to GenAI. Investing, regulated cryptocurrencies, stock trading, and exchange-traded funds is needlessly complex.

The new-gen AI processors, as claimed by Intel, offer longer battery life for the laptops, more than what Qualcomm has shown recently with the Snapdragon X Elite series and the new AMD HX lineup. These Windows laptops manufacturers have one target in sight – beating or matching Apple’s M-series Mac models. The following paragraphs explore some of the changes banks will need to undertake in each layer of this capability stack. While hallucination presents a particular kind of unfamiliar risk, Flaherty says that one of the underlying prevailing issues with how we treat any new technology is the perception that it is competing with human perfection. To recap just briefly, Stanford published research in January – Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools – which found that the AI research tools made by LexisNexis (Lexis+ AI) and Thomson Reuters (Westlaw AI-Assisted Research and Ask Practical Law AI) each hallucinate more than 17% of the time.

It’s where the productivity gains get to a point where you can start to do things you never thought possible. With genAI and a host of other complementary technologies applied, one could theoretically start to run a continuous close. Hook some visualization tools up to that data, and CEOs and decision-makers could

tap into a real-time dashboard of key financial, compliance, risk and cost metrics, for example.

Banks introduced ATMs in the 1960s and electronic, card-based payments in the ’70s. The 2000s saw broad adoption of 24/7 online banking, followed by the spread of mobile-based “banking on the go” in the 2010s. Partnering with technology providers experienced in Gen AI can help banks navigate the complexities of implementation and integration. For all GenAI applications in financial services, not just in banking, read our article on generative AI in financial services. CIB marketers can also use the new tools to automatically summarize a bank’s knowledge and use it to create viable marketing content, such as market recaps, research reports, and pitch books. A leading investment bank, for example, has built a gen AI tool to help analysts write first drafts of pitch books.

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.

Further, synthetic customer data are ideal for training ML models to assist banks determine whether a customer is eligible for a credit or mortgage loan, and how much can be offered. Organizations must consider when and how employees can leverage GenAI and evaluate the distinct risks of internal and external use cases. For example, the application of GenAI to lending decisions could lead to biased outcomes based on protected characteristics (e.g., gender or race). The burden of proof rests with banks, meaning they will need to collect evidence to show regulators why applications are denied and that applicants are considered fairly. Even where there are no legal or regulatory boundaries at present, governance models must be designed to promote responsible and ethical use of GenAI. While AI governance processes and controls are somewhat similar to those for legacy technologies, new risks require new models and frameworks, both for internal use cases and use of third-party tools.

Additionally, generative AI enables banks to deploy intelligent virtual assistants that can understand natural language and provide instant, accurate responses to customer inquiries. These virtual assistants can handle a wide range of tasks, from answering account-related questions to providing financial advice, ultimately leading to faster resolution times and higher customer satisfaction. Advanced generation AI models are shaping the future of the banking industry, offering transformative potential and creating new challenges. In this comprehensive article, we explore the evolution of generative AI models, their impact on the banking sector, and how to address the ethical and compliance concerns they raise.

While Gen Z has access to more information than ever before, it’s important that they filter out the noise and seek out sources with a proven track record to make well-informed decisions about their financial future. Start with a pilot project to evaluate the feasibility of the technology, analyze its potential risks, and measure the adoption. While full regulation of AI by the government is under consideration, the potential value of an extensive application of Gen AI should be balanced against regulatory risks. Fortunately, Gen AI itself provides the finance sector with an efficient means of keeping abreast of changing regulatory environments. Integrating Gen AI into banking operations will certainly reshape many roles in the banking workforce in that workers will have to learn new skills or change occupations.

Appropriate controls should inform initial planning and ­­help minimize the risk of damage to service quality, customer satisfaction and the bank’s brand and reputation. Banks must also recognize that regulators will pay particular attention to customer-facing use cases and those where AI enables automated decisioning. The competing options for deploying AI challenge banks to identify the most impactful initial use cases. Many banks are prioritizing legacy automation capabilities (e.g., robotic process automation) in back-office functions. A clear majority of respondents say their banks are waiting for further development and testing before prioritizing front-office use cases. Embedded and decentralized finance, tokenization, real-time payments and generative AI (GenAI) are among the powerful forces shaping the banking landscape today.

What is more, many banks’ data reserves are fragmented across multiple silos (separate business and technology teams), and analytics efforts are focused narrowly on stand-alone use cases. Without a centralized data backbone, it is practically impossible to analyze the relevant data and generate an intelligent recommendation or offer at the right moment. Lastly, for various analytics and advanced-AI models to scale, organizations need a robust set of tools and standardized processes to build, test, deploy, and monitor models, in a repeatable and “industrial” way. Many banks, however, have struggled to move from experimentation around select use cases to scaling AI technologies across the organization.

Layer 2: Building the AI-powered decision-making layer

There is no single path to victory in finding and keeping the talent a company needs. Our experience shows that companies need to implement a range of talent strategies, from more customer-centered hiring practices to tailored training pathways. But because gen AI moves quickly and there is little clarity about which skills will be needed, upskilling will need to be front and center. Among the challenges in developing upskilling programs are the lack of codified best practices and workers’ potential resistance to learning new skills.

Banks should prioritize the use of multiple authentication factors to enhance their cyber resilience. AI’s impact on banking is just beginning and eventually it could drive reinvention across every part … 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.

Gen AI can give developers context about the underlying regulatory or business change that will require them to change code by providing summarized answers with links to a specific location that contains the answer. It can assist in automating coding changes, with humans in the loop, helping to cross-check code against a code repository, and providing documentation. An organization looking to automate customer engagement using gen AI must have up-to-date, accurate data.

Long-term roadmaps must reflect how these technologies, when deployed in the right combinations, can transform core business functions (e.g., operations, finance, risk management, product development and sales). More importantly, they can also open new revenue streams and create entirely new value propositions. Generative AI can be used to create virtual assistants for employees gen ai in banking and customers. It can speed up software development, speed up data analysis, and make lots of customized content. It’s expected that Generative AI in banking could boost productivity by 2.8% to 4.7%, adding about $200 billion to $340 billion in revenue. Generative AI in banking isn’t just for customer-facing applications; it’s reshaping internal operations as well.

Making purposeful decisions with an explicit strategy (for example, about where value will really be created) is a hallmark of successful scale efforts. Finally, scaling up gen AI has unique talent-related challenges, whose magnitude will depend greatly on a bank’s talent base. Banks with fewer AI experts on staff will need to enhance their capabilities through some mix of training and recruiting—not https://chat.openai.com/ a small task. 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.

So let us elaborate on how the traditional banking experience can be transformed into a highly differentiated, secure, and efficient service by the convergence of generative AI and banking. These most promising generative AI use cases in banking, with some real-life examples, demonstrate the potential value arising from the technology. Interest in Gen AI solutions has been sky-high in the sector, and the future trajectory of generative AI in banking is set to soar even higher. MSCI is also partnering with Google Cloud to accelerate gen AI-powered solutions for the investment management industry with a focus on climate analytics.

A financial institution can draw insights from the details explored in this article, decide how much to centralize the various components of its gen AI operating model, and tailor its approach to its own structure and culture. An organization, for instance, could use a centralized approach for risk, technology architecture, and partnership choices, while going with a more federated design for strategic decision making and execution. JPMorgan Chase has filed a patent application for a gen AI service that can help investors select equities.3Kin and Carta Blog, “6 enterprise GenAI applications making a big impact,” August 17, 2023. Morgan Stanley has built a tool to help RMs deliver relevant ideas to customers in real time.4For more, see “Morgan Stanley Wealth Management announces key milestone in innovation journey with OpenAI,” Morgan Stanley press release, March 14, 2023. Still others are hung up on concerns about computing cost or stalled because of intellectual-property constraints.