How Would Generative AI Be Used in Banking? Bain & Company

Many banks operate with legacy systems that might not be compatible with new AI frameworks, which can create costly and time-consuming issues. 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. Modernize your financial services security and compliance architecture with IBM Cloud.

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Most importantly, the change management process must be transparent and pragmatic. 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.

Many banks use AI applications in process engineering and Six Sigma settings to generate conclusive answers based on structured data. Cross-industry Accenture research on AI found that just 1% of financial services firms are AI leaders. The median score for AI maturity in financial services is 27 on a scale — nine points lower than the overall median.

However, harnessing the value of Gen AI technology requires the expertise of a Generative AI development company Partner with Generative AI development service provider to maximize ROI. Massive paperwork involved in banking services is time-consuming and challenging to deal with. Further sorting through papers, required analysis, and finalizing the documents with bank stamps is quite a task that wastes a lot of valuable time for the bank staff. Gen AI models reduce operational cost and time by sifting through large volumes of documents, extracting essential data, and providing a summary in a fraction of a second. Gen AI techniques train fraud detection models, ensuring the algorithms can automatically track and flag potential breaches.

Banks are already seeking ways to optimize the capabilities of Generative AI chatbots and voice assistants so that it would be possible to solve almost any customer inquiry without a living person in sight. AI can be used to analyze historical data and make predictions about future customer behavior, which can be used to optimize products and services. We can forecast that Generative AI technology will impact the customer experience in the banking industry in several ways.

A growing number of Generative AI use cases in the banking sector showcase its immense impact across various banking aspects. Leading banking players incorporating Gen AI is evidence of the value technology adds to the banking business and their customers. You can foun additiona information about ai customer service and artificial intelligence and NLP. It’s high time to seize the chance and go the extra mile with Gen AI applications. Also, they can convert customer call conversations into data, search data, and provide contextual answers promptly that delight customers.

Choose an appropriate generative AI model and adapt it according to the defined objectives. Develop prototypes to validate AI algorithms and assess their feasibility in real-world banking applications. Conduct thorough testing and validation to refine the AI model based on performance metrics and user feedback. Recently, Citigroup leveraged generative AI to assess the impact of new US capital regulations.

Centrally led, business unit executed

In response to the mounting pressures placed on the banking community, Bank Director has created a board program that provides members of your board the necessary tools to stay on top of industry trends and regulatory updates. 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. A bank that fails to harness AI’s potential is already at a competitive disadvantage today.

This could happen if AI is integrated without a sharp focus on human centricity. It’s true that the key to becoming a successful financial company post-COVID is having 100% focus on solving the customers’ problems in the most effective way possible, instead of following a standardized scenario. What differentiates robots from people is the ability to feel emotions and empathy toward one another.

With proper mitigation strategies, like robust data governance, rigorous testing and validation, prioritization of transparency and explainability, and an ethical AI framework, banks will be able to maintain client trust and safety. The Singapore-based bank is deploying OCBC GPT, a Gen AI chatbot powered by Microsoft’s Azure OpenAI, to its 30,000 employees globally. This move follows a successful six-month trial where participating staff reported completing tasks 50% faster on average. Moreover, the tool goes beyond the basics, proactively identifying unusual activity, offering smart money moves, and even forecasting upcoming expenses.

Deutsche Bank

Utilizing generative AI allows financial companies to create tailored financial products based on individual customer profiles and behaviors, leading to higher customer engagement and satisfaction. Banks can integrate the technology into their digital solutions to analyze customer data and market trends and develop innovative and highly personalized financial products. Last but not least, generative AI algorithms can analyze customer data and preferences to create personalized marketing content and campaigns. It excels in finding answers in large corpuses of data, summarizing them, and assisting customer agents or supporting existing AI chatbots.

As conducted in a study by Wunderman, 63% of consumers state that the best brands are the ones that exceed expectations throughout the customer journey. The best way to exceed expectations and show customers that the financial brand cares about them is by offering a true value and benefit that is tailored to the specific needs the customers face. This explains why the demand for digital banking CX/UX experts is rapidly increasing. They are the user advocates that ensure a user-centered approach in digital product development. “The number one bank in the world will be a technology company,” as Brett King, Fintech influencer, author and futurist, predicted.

It also simplifies risk management and regulatory compliance, providing a unified strategy for legal and security challenges. AI-enabled banking solutions detect unusual patterns and potentially fraudulent activities by analyzing transaction data in real-time. This application reduces the incidence of false positives, https://chat.openai.com/ improves the accuracy of fraud detection, and enhances overall security, protecting both the institution and its customers from financial losses. Moreover, the rise of regulatory technology (RegTech) solutions powered by AI helped banks navigate increasingly complex regulatory landscapes more efficiently.

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. Our surveys also show that about 20 percent of the financial institutions studied use the highly centralized operating-model archetype, centralizing gen AI strategic steering, standard setting, and execution. About 30 percent use the centrally led, business unit–executed approach, centralizing decision making but delegating execution. Roughly 30 percent use the business unit–led, centrally supported approach, centralizing only standard setting and allowing each unit to set and execute its strategic priorities.

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. It can also be distant from the business units and other functions, creating a possible barrier to influencing decisions. 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. While Erica hasn’t yet integrated Gen AI capabilities, the bank is actively exploring its potential to further enhance the customer journey.

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. DevOps is a consolidation of practices and tools that increases how an organization delivers its applications and services.

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. For example, a customer may need help understanding how much of a mortgage they can afford. When AI models are provided with the relevant details such as interest rate, down payment amount, and credit score, Generative AI can quickly provide an accurate home purchasing budget.

By keeping all information within the bank’s secure environment, OCBC ensures data privacy while empowering its workforce with advanced AI capabilities. By scrutinizing a consumer’s unique objectives and risk appetite, it suggests customized investment recommendations. This goes beyond generic advice, ensuring that tips align with individual needs and preferences, ultimately enhancing the customer’s journey. Additionally, the technology relies on market trends and economic forecasts to provide up-to-date investment insights.

Explore the latest trends and applications of RPA in the pharmaceutical industry. Learn how RPA is improving efficiency, productivity, and accuracy in drug discovery, clinical trials, and more. None of the current methods of PHI de-identification ensure that all risks are removed. Discover how user-testing of conversational UI in rural contexts can provide insightful learnings for improving user experience. In a world where milliseconds can make a difference, Generative AI has become a crucial tool for financial institutions seeking to gain an edge in the highly competitive landscape of algorithmic trading.

This technology is reshaping the landscape of AI and automation in banking by introducing efficient solutions to automate previously time-consuming tasks. In the future, generative AI will play a pivotal role in shaping financial services by enabling predictive analytics for risk management, enhancing credit scoring systems, and offering customized financial advice. Furthermore, the integration of generative AI with existing banking systems will streamline operations, reduce costs, and improve decision-making processes. As banks continue to adopt and refine this technology, they will be better equipped to meet the evolving needs of their customers and maintain a competitive edge in the financial industry. Generative AI, leveraging advanced machine learning models, is revolutionizing the banking and financial sectors.

The bank’s risk and compliance team utilized the technology to efficiently analyze and summarize 1,089 pages of newly released capital rules from federal regulators. As the applications of generative AI in banking industry are gaining traction, more widely known global brands are integrating the technology into the core of their digital solutions. Among the financial institutions we studied, four organizational archetypes have emerged, each with its own potential benefits and challenges (exhibit). 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.

This comprehensive report on how GenAI will impact the banking industry includes insight into the regulatory roadmap, and details on how to safely, ethically and responsibly implement GenAI within your financial organization. Generative AI in banking is now widespread across the globe in the form of various Gen AI use cases. The new trend we expect to see in the Gen AI initiative is customer-centered AI integration. Banks are expected to embrace the emotional experience mindset to streamline the customer journey, integrate customer-centricity at all levels, and adopt a human-centered culture to deliver unparalleled customer value. Bank customers often find it challenging to decide which investment option is good and which one will help them achieve their financial goals. We see a significant shift from first e-payment to commercial computer tablets, P2P transfer to quantum computing, and mobile banking to Google Wallet.

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. Processes such as funding, staffing, procurement, and risk management get rewired to facilitate speed, scale, and flexibility. 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.

Data is vital to the growth of gen AI because LLMs require massive amounts of it to learn. But data can often be tied to individuals and their unique behaviors or be proprietary, internal data. The access to that data is one of the most paramount concerns as banks deploy gen AI.

Generative AI In Banking: 8 Use Cases And Challenges In 2024

This means that, while future technology might uncover superpowers for mankind, it’s up to the actual people behind the machines to determine the success of the outcome. There is a need for highly emotionally intelligent people who serve as translators between customers and the complexity of the opportunities uncovered by new technologies. The industry needs to be aware of the security threats gen AI can open but also the ways it can help mitigate potential vulnerabilities. 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. Conversely, with enterprise LLMs developed internally, this risk is minimized because the data is contained within the enterprise responsible for it.

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. 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 technology called Decision Intelligence Pro is projected to bolster fraud detection rates by up to 20%, with some institutions experiencing increases as high as 300%. For instance, a hedge fund might use AI to develop sophisticated trading algorithms that adapt in real-time to market conditions. This allows for more sophisticated trading decisions, better risk management, and improved returns on investment. For example, a credit union might use AI to analyze a wide range of data points, helping lenders make their credit decisions and benefit from the best loan terms. This leads to better risk management, reduced default rates, and increased access to credit for customers who may have been overlooked by traditional scoring methods. A credit card company, for instance, might use AI to monitor and analyze millions of transactions daily, identifying and flagging suspicious transaction patterns and unauthorized charges.

Hyper-personalized Customer Experience

Gen AI experts are avid at harnessing and integrating Gen AI capabilities into banking operations and processes. Browse extra benefits with Prismetrics Gen AI services that help business in achieving Gen AI initiative. Generative AI can analyze complex financial data and identify patterns and correlations to provide investment or other financial advice and assist users in making informed financial decisions.

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. Capabilities such as foundation models, cloud infrastructure, and MLOps platforms are at risk of becoming commoditized, given how rapidly open-source alternatives are developing. Making purposeful decisions with an explicit strategy (for example, about where value will really be created) is a hallmark of successful scale efforts.

Simultaneously, efficient AI-driven customer services, tailored marketing strategies, and custom financial advice improve the chances of conversion and increase sales and ROI. The AI models provide human experts-like financial advice based on market trends analysis of different investment options, customers’ income, and spending habits. It can simplify the user experience and reduce the complexity of banking operations, making it easier for even non-native speakers to use banking and financial services worldwide.

This customized, proactive approach empowers users to take control of their financial health, reduce stress, and confidently achieve their goals. Furthermore, 4 in 10 individuals are already seeing AI as a tool to manage their finances. In fact, one-third of those who’ve tried this technology say they’d trust it more than a human to handle their assets. 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.

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. They can also explain to employees in practical terms how gen AI will enhance their jobs. While this is not the most widely recognized example of GenAI in banking, it goes to show the many Generative AI use cases in banking that have unintended, but impactful, consequences. 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.

Overcoming ethical concerns and bias in AI models, as well as compliance with legal and data protection requirements, are also critical challenges in implementing generative AI in banking. Ethical concerns include the potential for biased decision-making, transparency, and the impact on employment. Banks need to adopt responsible AI practices, such as auditing algorithms for fairness, providing explainability, and ensuring human oversight.

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. First and foremost, gen AI represents a massive productivity and operational efficiency boost. Especially in financial services, where every service or product starts with a contract, terms of service, or other agreement. generative ai use cases in banking Gen AI is particularly good at discovering and summarizing complex information, such as mortgage-backed securities contracts or customer holdings across various asset classes. We have observed that the majority of financial institutions making the most of gen AI are using a more centrally led operating model for the technology, even if other parts of the enterprise are more decentralized.

With OpenAI’s GPT-4, Morgan Stanley’s chatbot now searches through its wealth management content. This simplifies the process of accessing crucial information, making it more practical for the company. 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 a small task.

While it’s true that the regulatory landscape is shifting and scrutiny is coming from numerous directions, this doesn’t mean that smaller financial institutions shouldn’t embrace the technology. This mindset isn’t surprising given that the banking industry can sometimes be slow to adopt new technologies, but financial institutions that hesitate on GenAI are leaving money on the table and will find themselves in the minority. Yet we’re still in the early innings of cloud-based AI’s impact on financial services and in society more broadly. This is akin to the flip-phone phase with the touchscreen era right around the corner.

They can also improve legacy code, rewriting it to make it more readable and testable; they can also document the results. Exchanges and information providers, payments companies, and hedge funds regularly release code; in our experience, these heavy users could cut time to market in half for many code releases. “It sure is a hell of a lot easier to just be first.” That’s one of many memorable lines from Margin Call, a 2011 movie about Wall Street.

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. Earlier this year, Q2 Executive Fellow Carl Ryden wrote an article about the Chat GPT reluctance of small financial institutions to integrate GenAI into their ecosystems. Though many believe that the biggest players are not utilizing the full potential of GenAI, that doesn’t mean small institutions can afford to sit on the sidelines, particularly since it has the potential to put them on equal footing.

Additionally, Citigroup plans to employ large language models (LLMs) to interpret legislation and regulations in various countries where they operate, ensuring compliance with local regulations in each jurisdiction. OCBC Bank in Singapore has recently reported that a six-month generative AI chatbot trial brought them a 50% efficiency lift, streamlining writing, translation, and research activities. In the past, when the company utilized technology to assist employees in developing code, summarizing documents, transcribing calls, and building an internal knowledge base, they achieved a similar productivity boost. According to Statista, the banking sector’s investment in generative AI is expected to reach $85 billion by 2030, growing at an impressive annual rate of over 55%. McKinsey estimates that across the global banking sector, AI and generative AI in particular could add up to $340 billion or 4.7% of total industry revenues annually.

• Banks should prioritize the use of multiple authentication factors to enhance their cyber resilience.
• Not only is this good business practice, but it will help accelerate the beneficial outcomes your financial institution can achieve with GenAI.
• This ability enables accurate risk assessments, aiding banks in making more informed decisions regarding loan applications, investments and other financial operations.
• GenAI is a subset of AI technologies designed to create new content, ideas or data that resemble or enhance original human-generated work.
• GenAI is more akin to advanced prosthetic limbs that restore or enhance human capabilities than fully autonomous androids that function independently.

The remaining institutions, approximately 20 percent, fall under the highly decentralized archetype. These are mainly large institutions whose business units can muster sufficient resources for an autonomous gen AI approach. Forrester reports that nearly 70% of decision-makers in the banking industry believe that personalization is critical to serving customers effectively. However, a mere 14% of surveyed consumers feel that banks currently offer excellent personalized experiences.

Let’s explore more details and specific use cases of Generative AI in banking and financial services. As the Managing Director & VP at Q2, Corey owns the Sensibill suite of services, helping organizations leverage their best-in-class spend management offerings for small business and commercial banking. Corey also leads Q2’s AI Center of Excellence, enabling the organization to use artificial intelligence tools, ethically and responsibly, to better serve our customers, partners, and people.

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. The assistant has reportedly handled 20 million interactions since it was launched in March 2023 and is poised to hit 100 million interactions annually. Using Google’s PaLM 2 LLM, the app is designed to answer customers’ everyday banking queries and execute tasks such as giving insight into spending patterns, checking credit scores, paying bills, and offering transaction details, among others. While some financial institutions are adopting generative AI tools at a breakneck pace (though mostly as pilot projects on a small scale), corporate implementation of Gen AI tools is still in its infancy. For the majority of banking leaders, the question of how and where generative AI could deliver the biggest value still stands.

Generative AI is playing a pivotal role in enhancing wealth management and portfolio optimization processes. Personalized marketing powered by Generative AI can lead to higher customer satisfaction, increased cross-selling opportunities, and a more significant return on marketing investments. Banks can deliver the right product or service to the right customer at the right time. While generative AI holds big promise for the banking industry, most of the current deployments are limited to just a few banking areas or don’t go beyond the experimental phase. Though early generative AI pilots appear rewarding and impressive, it will definitely take time to realize Gen AI’s full potential and appreciate its full impact on the banking industry. Banking and finance leaders must address significant challenges and concerns as they consider large-scale deployments.

These include managing data privacy risks, navigating ethical considerations, tackling legacy tech challenges, and addressing skills gaps. Mastercard has recently announced the launch of a new generative AI model to enable banks to better detect suspicious transactions on its network. According to Mastercard, the technology is poised to help banks improve their fraud detection rate by 20%, with rates reaching as much as 300% in some cases.

Generative AI-driven tools can also evaluate historical data, market trends and financial indicators in real time. This ability enables accurate risk assessments, aiding banks in making more informed decisions regarding loan applications, investments and other financial operations. These AI capabilities help banks optimize their financial strategies and protect themselves and their clients. 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. Generative AI powers advanced a new era of chatbots that handle customer inquiries with accurate human-like responses.

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. Said they believed that the technology will fundamentally change the way they do business. The pressing questions for banking institutions are how and where to use gen AI most effectively, and how to ensure the applications are fully adopted and scaled within their organizations. Here at Aisera, we offer Generative AI tools tailored to different industries, including the financial services and banking industries.

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. 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.

Artificial Intelligence prepares a pre-approved personalized offer in just a few seconds by scoring users’ financial profiles. Personalized offers created by Generative AI allow connections with customers on an emotional level, rather than annoying them with tons of useless product description and information overload. This would provide not only an amazing experience for the users but also a key factor that so many financial services of today lack─speed. It’s predicted that, in the upcoming years, Generative AI will completely replace most of the jobs in banking and other industries. Generative AI software would only require some regular maintenance as opposed to vacations, breaks, the risk of human error and the demand for raises.