Generative AI in Banking: Practical Use Cases and Future Potential

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.

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.

Generative AI and Its Use Cases in Banking

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.

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.

To solve this challenge, in August 2023, GLCU partnered with interface.ai to launch its industry-first Generative AI voice assistant. The assistant is named Olive and has had several significant impacts for the credit union. Organizations are not wondering if it will have a transformative effect, but rather where, when, and how they can capitalize on it. For example, Generative AI should be used cautiously generative ai use cases in banking when dealing with sensitive customer data. It also shouldn’t be relied upon to stay compliant with different government regulations, such as the General Data Protection Regulation (GDPR) or the California Consumer Privacy Act (CCPA). In the video, DeMarco delves into how Carta’s remarkable growth and expansion of product lines have been supported by its strategic adoption of Generative AI technologies.

How Can Banks Implement Generative AI?

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.

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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.

Pilot the technology

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.

With a hyper-intelligent understanding of the context and specifics of each inquiry, interface.ai’s Voice AI ensures that members receive accurate and relevant responses quickly. The ability to handle tasks has further boosted member satisfaction, as members can now manage their finances at any time of the day, instantly. You can also https://chat.openai.com/ use gen AI solutions to help you create targeted marketing materials and track conversion and customer satisfaction rates. From there, it can split your leads into segments, for which you can create different buyer personas. That way, you can tailor your marketing campaigns to different groups based on market conditions and trends.

• In this article, we explain top generative AI finance use cases by providing real life examples.
• AI-powered chatbots can provide fast and accurate responses to customer queries, freeing up human customer service representatives to handle more complex issues.
• These models learn from new data, making them highly adaptable to emerging threats.
• Corporate and investment banks (CIB) first adopted AI and machine learning decades ago, well before other industries caught on.
• As a result, the institution is taking a more adaptive view of where to place its AI bets and how much to invest.

AI reinforces risk management by generating predictive models capable of identifying potential risks and compliance issues. With its ability to stimulate various risk scenarios, generative AI can be used to develop mitigation strategies and ensure adherence to regulatory requirements. This allows businesses to reduce the burden on compliance officers, improve accuracy, and ensure timely reporting, thus avoiding costly fines and reputational damage. Making part of an integrated solution, generative AI helps to analyze individual customer profiles, market trends, and historical data to offer tailored investment advice.

Banks can also use Generative AI to require users to provide additional verification when accessing their accounts. For example, an AI chatbot could ask users to answer a security question or perform a multi-factor authentication (MFA). However, these can be costly to run and maintain, and in some cases, they aren’t very effective. Not only is this good business practice, but it will help accelerate the beneficial outcomes your financial institution can achieve with GenAI. Strategy topics will include board performance, technology implementation, data, talent acquisition, deposits and much more. Content related to lending will address topics ranging from small business and commercial to hedging, digitalization and more.

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 Chat GPT 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.

Some banks have already embraced its immense impact by applying Gen AI to a variety of use cases across their multiple functions. This includes lower costs, personalized user experiences, and enhanced operational efficiency, to name a few. In line with approaching generative AI for innovation, banks are expected to utilize the technology to improve efficiency in existing and older AI applications. Just like that, automating customer-facing processes creates digital data records that generative AI can use to refine services and internal workflows.

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, 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.

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.

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.

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. AI can assist employees by providing instant access to information, automating routine tasks, and generating insights, allowing them to focus on more strategic activities. In the future, banks should adopt a hybrid approach where AI tools augment human capabilities and implement training programs to help employees effectively use AI tools and understand their outputs. To improve customer experience and enhance their support capacity, the bank collaborated with McKinsey to develop a generative AI chatbot capable of providing immediate and tailored assistance.

By generating alerts and providing actionable insights, such AI-driven systems help prevent fraud and mitigate risks effectively. Its capability to generate unique and meaningful outputs from human language inputs has made this technology particularly invaluable for streamlined customer service, financial report generation, personalized investment advice, and more. 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. For example, Deutsche Bank is testing Google Cloud’s gen AI and LLMs at scale to provide new insights to financial analysts, driving operational efficiencies and execution velocity.

He is passionate about data science and has championed data analytics practice across start-ups to enterprises in various verticals. As a thought leader, start-up mentor, and data architect, Anand brings over two decades of techno-functional leadership in envisaging, planning, and building high-performance, state-of-the-art technology teams. Learn how Brazilian bank Bradesco is giving personal attention to each of its 65 million customers with IBM Watson. Start by formulating a comprehensive AI strategy aligned with the bank’s goals and regulatory requirements.

With the release of Python for Data Analysis, or pandas, in the late 2000s, the use of machine learning in banking gained momentum. Banking and finance emerged as some of the most active users of this earlier AI, which paved the way for new developments in ML and related technologies. In new product development, banks are using gen AI to accelerate software delivery using so-called code assistants. These tools can help with code translation (for example, .NET to Java), and bug detection and repair.

Looking ahead, AI continues to drive innovation in banking, positioning businesses at the forefront of digital transformation and customer-centric financial services. 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. 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.

Elevate the banking experience with generative AI assistants that enable frictionless self-service. Beyond any doubt, the use of generative AI in banking is poised to bring both expected and surprising changes, leading to an evolution and expansion of AI’s role in the sector. You can foun additiona information about ai customer service and artificial intelligence and NLP. However, significant changes from generative AI in banking will require some time.

Major financial institutions such as Bank of America and Wells Fargo have integrated this technology as the backbone of their AI virtual assistants. These AI-driven platforms improve customer experience by providing instant responses and personalized interactions and streamlining numerous banking processes. The banking industry has long been familiar with technological upheavals, and generative AI in Banking stands as the most recent influential development. This advanced machine learning technology, adept at sifting through vast data volumes, can generate distinct insights and content.

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.

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.

This can provide valuable insights for banks, helping them to improve their products and services and make more informed decisions. This refers both to unregulated processes such as customer service and heavily regulated operations such as credit risk scoring. Generative AI is a class of AI models that can generate new data by learning patterns from existing data, and generate human-like text based on the input provided. This capability is critical for finance professionals as it leverages the underlying training data to make a significant leap forward in areas like financial reporting and business unit leadership reports. AI-driven personalized financial services cater to individual customer needs by offering tailored recommendations and solutions. By analyzing customer data and behavior patterns, AI algorithms provide insights into spending habits, savings goals, and investment opportunities.

Banking users can employ chatbots to monitor their account balances, transaction history and other account-related information. Users forget information but remember experiences, and experiences are created from emotions. Gen AI will be at the top of the regulatory agenda until existing frameworks adapt or new ones are established.

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.

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.

In this insightful blog, we will explore seven compelling use cases that vividly demonstrate how Generative AI is beneficial to the banking industry. Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company limited by guarantee (“DTTL”), its network of member firms, and their related entities. DTTL and each of its member firms are legally separate and independent entities. DTTL (also referred to as “Deloitte Global”) does not provide services to clients.

The reduced waiting time and improved interaction with banks result in improved customer experience. Risk management is vital in preventing financial disasters and ensuring banks operate smoothly. Gen AI algorithms trained with data can identify financial risks and send alerts to the banks so that losses are mitigated or avoided.

Without central oversight, pilot use cases can get stuck in silos and scaling becomes much more difficult. Looking at the financial-services industry specifically, we have observed that financial institutions using a centrally led gen AI operating model are reaping the biggest rewards. As the technology matures, the pendulum will likely swing toward a more federated approach, but so far, centralization has brought the best results. Generative AI can be used to create virtual assistants for employees and customers. It can speed up software development, speed up data analysis, and make lots of customized content.