Five generative AI use cases for the financial services industry Google Cloud Blog

Brand’s predictive AI also reduces false positives by up to 200% while accelerating the identification of at-risk dealers by 300%. Faster alerts to banks, quicker card replacements, Chat GPT and enhanced trust in the digital infrastructure. This latest advancement further strengthens Mastercard’s robust suite of security solutions, ensuring a safer landscape for all.

• However, the tech can help the functions themselves improve efficiency and effectiveness.
• For example, it can recommend a credit card based on a customer’s spending habits, financial goals, and lifestyle.
• Some chatbots have been deployed to manage employee queries about product terms and conditions, for example, or to provide details on employee benefits programs.
• GOBankingRates works with many financial advertisers to showcase their products and services to our audiences.
• To tackle this issue, banks can explore techniques like data augmentation, synthetic data generation, and transfer learning to enhance the available data and improve AI model performance.

Because of this, office technology dealers can use this to their advantage, making better use of data they may already be collecting but don’t have an efficient way to analyze. The more tasks a machine can handle, the more time workers have for the tasks only a human can do. Any artificial intelligence solution you adopt in your dealership is also a solution your clients can use if you show them the way. Brion brought up how advice without context might not be relevant to the circumstance of the person asking for advice.

Organizations are not wondering if it will have a transformative effect, but rather where, when, and how they can capitalize on it. This article explains the top 4 use cases of generative AI in banking, with some real-life examples. This article was edited by Mark Staples, an editorial director in the New York office.

While they offered 24/7 assistance with an IVR system, it lacked functionality and contextual-understanding that restricted the volume of calls it could handle, and the quality in which it managed them. Detecting anomalous and fraudulent transactions is one of the applications of generative AI in the banking industry. Finally, it is seen that using a GAN-enhanced training set to detect such transactions outperforms that of the unprocessed original data set. Marketing and sales is a third domain where gen AI is transforming bankers’ work. This could cut the time needed to respond to clients from hours or days down to seconds.

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. Eventually, businesses might find it beneficial to let individual functions prioritize gen AI activities according to their needs.

The evolution of AI in banking

As the technology matures, the pendulum will likely swing toward a more federated approach, but so far, centralization has brought the best results. Scaling gen AI capabilities requires companies to rewire how they work, and a critical focus of rewiring is on developing the necessary talent for these capabilities. The gen AI landscape and how software teams work with the technology to build products and services are likely to stabilize in the next two to three years as the technology matures and companies gain experience. The skills and practices needed to succeed now may well change considerably over time.

Those only come when you think holistically and focus on outcomes rather than costs. Gen AI will be at the top of the regulatory agenda until existing frameworks adapt or new ones are established. For example, Generative Artificial Intelligence can be used to summarize customer communication histories or meeting transcripts.

And we’ve chosen the term “conversation” intentionally because partnership and dialogue between various gen AI tech providers are essential–all sides can and have learned from one another and, in doing so, help address the challenges ahead. For all industries, but particularly within financial services, gen AI security needs to be air-tight to prevent data leakage and interference from nefarious actors. We work with policymakers to promote an enabling legal framework for AI innovation that can support our banking customers. This includes advancing regulation and policies that help support AI innovation and responsible deployment.

Until then, companies must navigate through an uncertain period of change and learning. Delivering personalized messages and decisions to millions of users and thousands of employees, in (near) real time across the full spectrum of engagement channels, will require the bank to develop an at-scale AI-powered decision-making layer. Despite billions of dollars spent on change-the-bank technology initiatives each year, few banks have succeeded in diffusing and scaling AI technologies throughout the organization.

Gen AI can help junior RMs better meet client needs through training simulations and personalized coaching suggestions based on call transcripts. For many banks that have long been pondering an overhaul of their technology stack, the new speed and productivity afforded by gen AI means the economics have changed. Consider securities services, where low margins have meant that legacy technology has been more neglected than loved; now, tech stack upgrades could be in the cards. Even in critical domains such as clearing systems, gen AI could yield significant reductions in time and rework efforts.

These AI systems can automatically generate financial reports and analyze vast amounts of data to detect fraud. They automate routine tasks such as processing documents and verifying information. At a time when companies in all sectors are experimenting with gen AI, organizations that fail to harness the tech’s potential are risking falling behind in efficiency, creativity, and customer engagement. At the outset, banks should keep in mind that the move from pilot to production takes significantly longer for gen AI than for classical AI and machine learning. In selecting use cases, risk and compliance functions may be tempted to use a siloed approach. Instead, they should align with an entire organization’s gen AI strategy and goals.

But banks clearly understand the urgency; a huge majority are already dedicating resources to GenAI. Furthermore, investment and mortgage calculators tend to utilize technical jargon. This can hinder one’s ability to accurately estimate payments and comprehend the nature of the service. When applying Generative AI for payments, you may find that these complexities become more manageable. Generative AI is disrupting debt collection by enhancing efficiency and personalization in communication. By leveraging NLP and ML, AI systems analyze debtor behavior and preferences, generating tailored messages that increase engagement and repayment rates.

It takes a lot of deep customer analysis and creative work, which can be costly and time-consuming. There has never been a better time to seize the chance and gain a competitive edge while large-scale deployments remain nascent. The integration of generative AI solutions into banking operations requires strategic planning and consideration. Before we dive into Gen AI applications in the banking industry, let’s see how the sector has been gradually adopting artificial intelligence over the years. In this blog post, we aim to unravel the transformative potential of the novel technology in banking by delving into the practical application of generative AI in the banking industry. As we continue our exploration, we will highlight the potential Gen AI adoption barriers and offer some key fundamentals to focus on for its successful implementation.

Generative AI can handle vast amounts of financial data but must be used cautiously to ensure compliance with regulations such as GDPR and CCPA. While centralization streamlines important tasks, it also provides flexibility by enabling some strategic decisions to be made at different levels. This approach balances central control with the adaptability needed for the bank’s needs and culture and helps keep it competitive in fintech.

For example, gen AI can help bank analysts accelerate report generation by researching and summarizing thousands of economic data or other statistics from around the globe. It can also help corporate bankers prepare for customer meetings by creating comprehensive and intuitive pitch books and other presentation materials that drive engaging conversations. 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.

They can also act as mentors to coach new skills, such as how to break problems down, deliver business goals, understand end user needs and pain points, and ask relevant questions. The ability to compete depends increasingly on how well organizations can build software products and services. Already, nearly 70 percent of top economic performers, versus just half of their peers, use their own software to differentiate themselves from their competitors. One-third of those top performers directly monetize software.1“Three new mandates for capturing a digital transformation’s full value,” McKinsey, June 15, 2022.

Once confirmed, those skills are added not only to the individuals’ profiles but also to the company’s skills database for future assessments. This collaboration is critical for developing an inventory of skills, which provides companies with a fact base that allows them to evaluate what skills they have, which ones they need, and which ones gen AI tools can cover. This skills classification should use clear and consistent language (so it can be applied across the enterprise), capture expertise levels, and be organized around hierarchies to more easily organize the information. To highlight just a few examples, we are already seeing gen AI technologies handle some simple tasks, such as basic coding and syntax, code documentation, and certain web and graphic design tasks.

This blog delves into the most impactful Generative AI use cases in banking, showing GLCU’s success and why Generative AI in banking is becoming indispensable. GANs are capable of producing synthetic data (see Figure 2) and thus appropriate for the needs of the banking industry. Synthetic data generation can be achieved by different versions of GAN such as Conditional GAN, WGAN, Deep Regret Analytic GAN, or TimeGAN. Over the past ten years or so, a handful of corporate and investment banks have developed a genuine competitive edge through judicious use of traditional AI.

The industry has a constructive role to play in fostering dialogue with various government institutions. As an example of modern banking in India, SBI Card, a payment service provider in India, leverages Generative AI and machine learning to enhance their customer experience. The point is there are many ways that banks can use Generative AI to improve customer service, enhance efficiency, and protect themselves gen ai in banking from fraud. According to Cybercrime Magazine, the global cost of cybercrime was $6 trillion in 2021, and it’s expected to reach $10.5 trillion by 2025. These are key essentials you may want to focus on for a successful Gen AI implementation strategy. To establish a solid foundation for building robust generative AI solutions, banks need a comprehensive implementation roadmap to include yet more strategic steps.

Generative AI (gen AI) offers a tantalizing opportunity to increase this value opportunity by helping software talent create better code faster. Equally important is the design of an execution approach that is tailored to the organization. To ensure sustainability of change, we recommend a two-track approach that balances short-term projects that deliver business value every https://chat.openai.com/ quarter with an iterative build of long-term institutional capabilities. Furthermore, depending on their market position, size, and aspirations, banks need not build all capabilities themselves. They might elect to keep differentiating core capabilities in-house and acquire non-differentiating capabilities from technology vendors and partners, including AI specialists.

Before interface.ai, GLCU used a non-AI-powered IVR system that averaged a 25% call containment rate (the % of calls successfully handled without the need for human intervention). With interface.a’s Voice AI, the call containment rate now averages 60% during business hours, and up to 75% after hours. There’s a lot of conversation around the potential of Generative AI in banking.

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Or it can look at risk and compliance support, as many banks are doing, whereby gen AI can provide support to first- and second-line functions to identify relevant regulations and compliance requirements and to help locate relevant instructions. The technology is not yet at a state where banks can have sufficient confidence to hand over risk and compliance tasks fully. A focus on data quality and addressing data scarcity is required to accomplish this.

Generative AI in banking refers to the use of advanced artificial intelligence (AI) to automate tasks, enhance customer service, detect fraud, provide personalized financial advice and improve overall efficiency and security. Management teams with early success in scaling gen AI have started with a strategic view of where gen AI, AI, and advanced analytics more broadly could play a role in their business. This view can cover everything from highly transformative business model changes to more tactical economic improvements based on niche productivity initiatives. For example, leaders at a wealth management firm recognized the potential for gen AI to change how to deliver advice to clients, and how it could influence the wider industry ecosystem of operating platforms, relationships, partnerships, and economics.

Over time, gen AI should be able to generate insights from automatically created tests, system logs, user feedback, and performance data. Gen AI can use self-created insights and ideas for new features to create proofs of concept and prototypes, as well as to reduce the cost of testing and unlock higher verification confidence (for example, multiple hypotheses and A/B testing). These developments are expected to significantly reduce PDLC times from months to weeks or even days, improve code quality, and reduce technical debt.

Such a human-in-the-loop approach is the only way to reliably detect anomalies before they lead to an actionable decision. Using Gen AI to produce initial responses as a starting point and creating AI-human feedback loops can significantly improve decision making accuracy. Such systems impede the adoption of novel technologies and the integration of the new capabilities that these innovations can deliver for several reasons. First, legacy systems often use outdated data formats, structures, and protocols that may be incompatible with modern AI technologies. Secondly, they may store data in siloed or proprietary formats, making it difficult to access and retrieve data for AI model training and analysis.

Much has been written (including by us) about gen AI in financial services and other sectors, so it is useful to step back for a moment to identify six main takeaways from a hectic year. With gen AI shifting so fast from novelty to mainstream preoccupation, it’s critical to avoid the missteps that can slow you down or potentially derail your efforts altogether. GOBankingRates works with many financial advertisers to showcase their products and services to our audiences.

Successful gen AI scale-up—in seven dimensions

Finally, AI-driven robo-advisors have democratized access to financial advisory services, empowering customers to make more informed decisions about their financial future. As AI continues to evolve, its potential to drive positive change in the banking sector is immense, ushering in a new era of efficiency, security, and customer satisfaction. While it’s important to understand the risks of gen AI, banks and technology providers can – and must – work together to mitigate rather than simply accept those risks. That’s an essential prerequisite as we look to the incredible opportunities gen AI can bring—such as enhanced productivity, immense time savings, improved customer experiences, and enhanced responsiveness to regulatory and compliance demands. Our view is that gen AI can actually herald a safer and more efficient banking system for everyone involved.

Built for stability, banks’ core technology systems have performed well, particularly in supporting traditional payments and lending operations. However, banks must resolve several weaknesses inherent to legacy systems before they can deploy AI technologies at scale (Exhibit 5). Core systems are also difficult to change, and their maintenance requires significant resources.

These virtual experts can also collect data and evaluate climate risk assessments to answer counterparty questions. 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.

As AI becomes more integrated into banking processes, banks must invest in upskilling their workforce to prepare for the future. This includes providing continuous training and development opportunities to ensure employees are equipped with the skills needed to thrive in an AI-driven environment. As AI continues to evolve and shape the banking industry, banks must remain agile and adaptive to stay competitive. This involves staying up-to-date with the latest developments in AI research and technology and exploring new applications that can drive growth and innovation. Payments providers need to consider customer experience design, risk, technology, and data and analytics to achieve smart growth. While such front-office use cases can yield high-profile wins, they can also create new risks.

Among the obstacles hampering banks’ efforts, the most common is the lack of a clear strategy for AI.6Michael Chui, Sankalp Malhotra, “AI adoption advances, but foundational barriers remain,” November 2018, McKinsey.com. Two additional challenges for many banks are, first, a weak core technology and data backbone and, second, an outmoded operating model and talent strategy. Generative AI is revolutionizing the asset management industry by offering innovative solutions for smarter investment management and trading. Enhanced portfolio optimization, advanced risk management, improved investment decision-making, efficient trade execution, and adaptive trading strategies are some of the key benefits of incorporating AI-driven algorithms in the asset management process. By analyzing vast amounts of data from diverse sources and uncovering hidden trends and relationships, generative AI empowers asset managers to make data-driven decisions that align with their clients‘ risk tolerance and financial goals. In addition, AI-driven systems enable asset managers to optimize trade execution, minimize transaction costs, and adapt their strategies to the ever-changing market conditions, ultimately delivering better performance for their clients.

And others may require new groups, organizations, and institutions – as we are seeing at agencies like NIST. For all the promise of the technology, gen AI may not be appropriate for all situations, and banks should conduct a risk-based analysis to determine when it is a good fit and when it’s not. Like any tool, it’s safest and most effective when used by the right people in the right situation. New gen AI tools can direct a large model—whether it be a large language model (LLM) or multimodal LM—toward a specific corpus of data and, as part of the process, show its work and its rationale. This means that for every judgment or assessment produced, models can footnote or directly link back to a piece of supporting data.

This new Copilot+ PC seamlessly integrates advanced AI capabilities, which elevate productivity and creativity to new heights. Sales is a people business, and sales conversations are about listening to people. However, the best sales meeting in the world won’t amount to anything if nobody remembers to do their action items afterward—sending clients the info they requested, syncing calendars, or following up. 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. “For better or for worse, the financial decisions of parents and older family members result in the economic outcomes an individual experiences in their youth,” said Louis Brion, founder and CEO of Lakefront Finance.

Some banks are pushing ahead in the design of omnichannel journeys, but most will need to catch up. Each layer has a unique role to play—under-investment in a single layer creates a weak link that can cripple the entire enterprise. Leaders in the banking sector must address significant challenges as they consider large-scale deployments. These include managing data security, integrating legacy technology, navigating ethical issues, addressing skills gaps, and balancing benefits with regulatory risks.

As banks navigate data security concerns, legacy system constraints, ethical considerations, skills gaps, and regulatory risks, adopting a cautious and strategic approach is paramount. To bridge the skills gap, financial services firms will have to figure out what new skills the workforce will have to acquire and whether they need to reskill and upskill existing employees or hire new ones. This will require extensive investments in retraining and hiring initiatives to meet changing talent needs.

For this reason, companies should pay particular attention to apprentice models, which tend to be overlooked as part of a business’s upskilling repertoire. Apprenticing offers hands-on learning to demystify change and role modeling to demonstrate hard-to-teach skills, such as problem-solving mindsets and how to use good judgment in evaluating code suitability. But for apprenticing to be effective, senior experts must be active participants rather than just checking a box. They have the credibility and often institutional knowledge that can be useful, such as navigating risk issues specific to the company. Experts will need to code and review code with junior colleagues, shadow them as they work, and set up go-and-see visits so they can discover how teams work with gen AI.

This AI-powered analysis empowered risk and compliance teams, ensuring rapid understanding and informed decision-making. A testament to Citigroup’s innovative approach, this move showcases how AI is disrupting the domain in the face of complex regulations. Organizations and banks, such as Swift, ABN Amro, ING Bank, BBVA, and Goldman Sachs, are experimenting with Generative AI in banking. These industry leaders are introducing technology to automate processes, enhance customer interactions, analyze behavior patterns, optimize wealth management, and more. Let’s explore further how 11 influential brands are adopting or testing this transformative force.

However, the real holy grail in banking will be using generative AI to radically reduce the cost of programming while dramatically improving the speed of development, testing and documenting code. Imagine if you could read the COBOL code inside of an old mainframe and quickly analyze, optimize and recompile it for a next-gen core. Uses like this could have a significant impact on bank expenses, as around 10% of the cost base of a bank today is related to technology, of which a sizable chunk goes into maintaining legacy applications and code. Though they cost billions to develop, many of these cloud-based AI solutions can be accessed cheaply.

Gen AI could summarize a relevant area of Basel III to help a developer understand the context, identify the parts of the framework that require changes in code, and cross check the code with a Basel III coding repository. 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. Leaders must acquire a deep personal understanding of gen AI, if they haven’t already. Investments in executive education will equip them to show employees precisely how the technology and the bank’s operations connect, thereby generating excitement and overcoming trepidation. 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. 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.

The ability for any competitor to use and string together these AI tools is the real development for banks here. While AI can automate many tasks, human expertise remains essential in the banking industry. You can foun additiona information about ai customer service and artificial intelligence and NLP. Banks must strike the right balance between automation and human intervention to ensure optimal results and maintain customer trust.

One of the world’s biggest financial institutions is reimagining its virtual assistant, Erica, by incorporating search-bar functionality into the app interface. This design change reflects the growing trend of users seeking a more intuitive and search-engine-like experience, aligning with the increasing popularity of generative tools. To assist its 16,000 advisors, the bank has introduced AI @ Morgan Stanley Assistant, powered by OpenAI. This tool grants consultants access to over 100,000 reports and documents, simplifying information retrieval. The chatbot is designed to handle a wide range of research and administrative tasks, allowing counselors to concentrate on delivering personalized financial advice and building stronger consumer relationships. Another use case is to provide financial product suggestions that help users with budgeting.

At Google Cloud, we’re optimistic about gen AI’s potential to improve the banking sector for both banks and their customers. As a rule of thumb, you should never let Generative AI have the final say in loan approvals and other important decisions that affect customers. Instead, have it do all the heavy lifting and then let financial professionals make the ultimate decisions. All that said, Generative AI can still be a powerful banking tool if you know how to use it properly. But manually sorting through, analyzing, and signing off on various financial documents and applications can take a lot of time and money.

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. First, banks will need to move beyond highly standardized products to create integrated propositions that target “jobs to be done.”8Clayton M. Christensen, Taddy Hall, Karen Dillon and David S. Duncan, “Know your customers ‘jobs to be done,” Harvard Business Review, September 2016, hbr.org. Further, banks should strive to integrate relevant non-banking products and services that, together with the core banking product, comprehensively address the customer end need. An illustration of the “jobs-to-be-done” approach can be seen in the way fintech Tally helps customers grapple with the challenge of managing multiple credit cards. In new product development, banks are using gen AI to accelerate software delivery using so-called code assistants.

Goldman Sachs, for example, is reportedly using an AI-based tool to automate test generation, which had been a manual, highly labor-intensive process.7Isabelle Bousquette, “Goldman Sachs CIO tests generative AI,” Wall Street Journal, May 2, 2023. And Citigroup recently used gen AI to assess the impact of new US capital rules.8Katherine Doherty, “Citi used generative AI to read 1,089 pages of new capital rules,” Bloomberg, October 27, 2023. For slower-moving organizations, such rapid change could stress their operating models. 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.

By analyzing this wealth of information, AI-driven algorithms can create a more accurate and nuanced credit score, enabling banks to make better-informed lending decisions. As a first step, banks should establish guidelines and controls around employee usage of existing, publicly available GenAI tools and models. Those guidelines can be designed to monitor and prevent employees from loading proprietary company information into these models. Additionally, top-of-the-house governance and control frameworks must be established for GenAI development, usage, monitoring and risk management agnostic of individual use cases. Strong use cases will include “high-touch” activities historically owned by people, which leverage large datasets or require a generative response logic.

Partner with Master of Code Global to gain a sustainable competitive advantage. Let’s start a conversation about how we can help you navigate this exciting frontier and shape the future of banking. Furthermore, 4 in 10 individuals are already seeing AI as a tool to manage their finances.

When it comes to GenAI specifically, banks should not limit their vision to automation, process improvement and cost control, though these make sense as priorities for initial deployments. GenAI can impact customer-facing and revenue operations in ways current AI implementations often do not. For example, GenAI has the potential to support the hyper-personalization of offerings, which helps drive customer satisfaction and retention, and higher levels of confidence. Given the newness of GenAI and the limited tech capabilities of many banks, acquisitions or partnerships may be necessary to access the necessary skills and resources.