AI and generative AI use cases in banking: 6 real-world examples

The future of AI in banking includes transformative applications that enhance operational efficiency and customer experiences. These three domains—new product development, customer operations, and marketing and sales—represent the most promising areas for the technology. Gen AI can extract textual content from customer interactions, loan and collateral documents, and public news sources to improve credit models and early-warning indicators.

It can increase efficiency and reduce costs for banks while providing
faster and more accurate customer support, allowing banks to avoid the need for large customer support teams. And all of this would be available 24/7, making it easy for customers to get help whenever needed by answering questions, resolving issues and providing
financial education outside of regular business hours. As we look ahead, the transformative potential of Generative AI remains boundless. Emerging trends like AI-powered financial advisors and predictive analytics are reshaping the industry. By embracing Generative AI and addressing its challenges, banks can lead innovation and deliver exceptional value. It’s a journey towards a more efficient, secure, and customer-centric industry.

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. 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.

Generative AI in banking isn’t just for customer-facing applications; it’s reshaping internal operations as well. Fujitsu, in collaboration with Hokuriku and Hokkaido Banks, is piloting the use of the technology to optimize various tasks. By using Fujitsu’s Conversational AI module, the institutions are exploring how AI can answer internal inquiries, generate and verify documents, and even create code. Such an approach could make the processes more efficient, accurate, and responsive to the evolving needs of the industry. AI-powered virtual assistants are available around the clock to answer inquiries and offer guidance tailored to each individual’s goals. Meanwhile, behind the scenes, Gen AI optimizes back-office processes, reducing operational costs and minimizing human errors.

• But if we’re talking about personalization, we’re not just talking about offers.
• Together, we can advance education technology and make a lasting impact on students and educators worldwide.
• For all GenAI applications in financial services, not just in banking, read our article on generative AI in financial services.
• Evaluate the quality, security, and reliability of existing data repositories.

About the Google Cloud Generative AI Benchmarking StudyThe Google Cloud Customer Intelligence team conducted the Google Cloud Generative AI Benchmarking Study in mid-2023. Participants included IT decision-makers, business decision-makers, and CXOs from 1,000+ employee organizations considering or using AI. Participants did not know Google was the research sponsor and the identity of participants was not revealed to Google. Financial services leaders are no longer just experimenting with gen AI, they are already way building and rolling out their most innovative ideas.

Risks to watch out for

Deploy generative AI models using NVIDIA NIM inference microservices to achieve low-latency and high-performance inference. Your donation to our nonprofit newsroom helps ensure everyone in Allegheny County can stay up-to-date about decisions and events that affect them. Readers tell us they can’t find the information they get from our reporting anywhere else, and we’re proud to provide this important service for our community.

For example, Generative Artificial Intelligence can be used to summarize customer communication histories or meeting transcripts. This can save time when dealing with customer concerns or collaborating on team projects.

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. Generative Artificial Intelligence models are Artificial Intelligence models that generate new content based on a prompt or input. The output content can be in various forms, including text, images, and video. Metaverse refers to a virtual world where users can interact with each other, objects and events in an immersive, realistic, and dynamic manner. A critical and foremost step in realizing the Metaverse is content creation for its different realms.

AI and generative AI use cases in banking: 6 real-world examples

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. Some financial institutions like mortgage brokers or investment companies provide financial advice to their customers using gen AI technology. This can be one of the best Generative AI use cases for financial https://chat.openai.com/ service companies. Such financial advisors and businesses can combine human expertise with the power of AI to give consumers more comprehensive and customized financial plans. Generative AI can help banks to analyze market trends and optimize investment portfolios. These models can determine potential risks and opportunities, enabling banks to make data-driven investment strategy decisions.

Let’s explore the seven use cases of Generative AI in modern banking in the USA, Canada, and India. 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. As AI matured, financial institutions started leveraging more sophisticated AI applications to improve decision-making processes. Advanced predictive analytics and data-driven insights enabled banks to assess credit risk, detect fraudulent activities, and optimize investment strategies. Before diving into practical use cases, let’s first define AI in banking and financial services.

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. 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. Besides certain software systems for risk minimization, the use of generative AI is one possible solution for minimizing such losses resulting from the lack of adequate risk management.

“A generative AI agent can break down complex tasks and lean on purpose-built sub-systems,” said Steven Hillion, who is the senior vice president of data & AI at Astronomer. McKinsey has found that gen AI could substantially increase labor productivity across the economy. To reap the benefits of this productivity boost, however, workers whose jobs are affected will need to shift to other work activities that allow them to at least match their 2022 productivity levels. If workers are supported in learning new skills and, in some cases, changing occupations, stronger global GDP growth could translate to a more sustainable, inclusive world.

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. It can slow execution of the gen AI team’s use of the technology because input and sign-off from the business units is required before going ahead. 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. These dimensions are interconnected and require alignment across the enterprise. A great operating model on its own, for instance, won’t bring results without the right talent or data in place. 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.

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. Here at Aisera, we offer Generative AI tools tailored to different industries, including the financial services and banking industries. Like all businesses, banks need to invest in targeted marketing to stand out from the competition and gain new customers. It takes a lot of deep customer analysis and creative work, which can be costly and time-consuming. In short, Generative Artificial Intelligence can look to the past to help banks make better financial decisions about the future and create synthetic data for robust analyses of risk exposure.

As a bank, you don’t just want to gain new customers; you also want to retain existing ones, and gen AI tools can help you achieve this. And to do that, you must always improve customer service and invest in creating a good customer experience. The point is there are many ways that banks can use Generative AI to improve customer service, enhance efficiency, and protect themselves from fraud. Gen AI is a big step forward, but traditional advanced analytics and machine learning continue to account for the lion’s share of task optimization, and they continue to find new applications in a wide variety of sectors.

With Vertex AI Search and Conversation, even early career developers can rapidly build and deploy chatbots and search applications in minutes. 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. It allows users to ask math-related questions in a more conversational manner. For instance, one might inquire, “If I invest $X at Y% interest for Z years, what will my return be? ” Alternatively, they wish to clarify, “What would be the difference in my monthly mortgage payments if I choose a variable rate of X% or a fixed rate of Y%?

This strategic move aims to maximize performance, simplify procedures, and encourage out-of-the-box thinking across the organization. The bank envisions Gen AI empowering workers in numerous ways, including content creation, complex question answering, data analysis, and process optimization. Cora, NatWest’s virtual assistant, is getting a Generative AI upgrade with the help of IBM and their Watsonx platform. This enriched version, Cora+, will offer customers a more conversational and personalized experience.

Technology topics address if and how to leverage artificial intelligence, finding the right fintech partners, maintaining data and cybersecurity, if and how to leverage banking as a service, budget allocation for technology and more. AI can revolutionize financial services organizations with real value and cost savings — but only if you’re using the right data. One of the most powerful features that digital banking AI can provide is personalized promotions.

An app that provides a contextualized experience should be able to predict the exact moment when a user needs a specific product and provide it by combining big data with behavior-based predictive analytics. The data already available to the incumbents could
be used to provide personalized offers based on the user’s purchasing and financial behavior even before the user has requested it. It’s predicted that, in the upcoming years, AI will completely replace most of the jobs in banking and other industries.

If you are inspired by successful generative AI use cases in banking, let’s chatand schedule a discovery session where we could discuss potential applications and limitations for your specific scenario. Banks are expected to continue investing in generative AI models and testing them over the next 2-5 years. In the short term, banks will likely focus on incremental innovations—small efficiency gains and improvements based on specific business needs. Employees will maintain an oversight role to ensure accuracy, precision, and compliance as the technology matures.

There are already concerns among customers about how AI technologies will use their data and whether it is safe. According to The Economist Intelligence Unit & Temenos study, 34% of customers are concerned about the lack of clarity surrounding data use,
while 40% were concerned about the security of their personal financial information. For example, location-based push notifications about the location of local ATMs may appear when the user crosses the border. Purchasing a flight ticket could be a good chance to offer an insurance policy for travel. Child expenditures or maternity grants
detected by the banking AI could become an ideal reason to offer a loan on increasing the living space.

It’s showing up in music and entertainment, education, healthcare, and marketing. Sentiment analysis, an approach within NLP, categorizes texts, images, or videos according to their emotional tone as negative, positive, or neutral. By gaining insights into customers’ emotions and opinions, companies can devise strategies to enhance their services or products based on these findings. In this article, we explain top generative AI finance use cases by providing real life examples.

Gen AI can craft targeted messages, content, and even product offerings that resonate with each customer’s preferences and needs. This level of customization not only enhances customer engagement but also drives conversion rates and customer loyalty. 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. They can execute trades with unparalleled speed and accuracy, improving their market position and profitability. Algorithmic trading powered by Generative AI also allows for the exploration of new trading strategies that were previously unimaginable.

Wealth management is a critical area in banking, where clients entrust financial institutions to grow and safeguard their assets. 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.

Its ability to comb unstructured data for insights radically widens the possible uses of AI in financial services. A bank that fails to harness AI’s potential is already at a competitive disadvantage today. Many banks use AI applications in process engineering and Six Sigma Chat GPT settings to generate conclusive answers based on structured data. We’ve reached an inflection point where cloud-based AI engines are surpassing human capabilities in some specialized skills and, crucially, anyone with an internet connection can access these solutions.

This article explains the top 4 use cases of generative AI in banking, with some real-life examples. Business units that do their own thing on gen AI run the risk of lacking the knowledge and best practices that can come from a more centralized approach. They can also have difficulty going deep enough on a single gen AI project to achieve a significant breakthrough. 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.

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. Gen AI can help junior RMs better meet client needs through training simulations and personalized coaching suggestions based on call transcripts. Generative AI models analyze transaction data, customer profiles, and historical patterns to identify suspicious activities. They detect known money laundering techniques and adapt to evolving schemes. This results in accurate detection, reduced false positives, and enhanced compliance with regulatory requirements, safeguarding the institution’s reputation.

Understanding generative AI and how peers are using AI and GenAI helps financial institution leaders and management vet the technology and related risks. Moreover, generative AI models can be used to generate customized financial reports or visualizations tailored to specific user needs, making them even more valuable for businesses and financial professionals. 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.

Assessing the worthiness of merchants based on business performance helps Payment Service Providers decide which merchants to onboard. Chatbots can assist users in managing their accounts by arranging automatic payments, changing personal information and more. Chatbot can provide rapid and effective customer care by answering common questions and fixing simple issues. Users forget information but remember experiences, and experiences are created from emotions. What differentiates robots from people is the ability to feel emotions and empathy toward one another.

These generated examples can help train and augment machine learning algorithms to recognize and differentiate between legitimate and fraudulent patterns in financial data. 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. Generative AI-powered tools automate the creation of comprehensive financial reports by analyzing vast amounts of data and generating detailed narratives. For instance, a bank might use AI to interpret commercial loan agreements and generate financial summaries. This application saves time, reduces human error, and ensures that stakeholders receive accurate and timely financial insights, allowing financial analysts to focus on more strategic tasks.

GenAI use case for understanding financial institution data

Recently, Citigroup leveraged generative AI to assess the impact of new US capital regulations. 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.

And businesses are developing applications to address use cases across all these areas. In the near future, we expect applications that target specific industries and functions will provide more value than those that are more general. However, unlike generative AI, these models don’t use these patterns and relationships to generate new content. At LITSLINK, we are committed to helping you harness the power of generative AI to build cutting-edge educational tools. With our expertise in custom AI solutions, strategic consultation, user-centric design, and ongoing support, we can turn your vision into reality. Together, we can advance education technology and make a lasting impact on students and educators worldwide.

With cutting-edge Generative AI, they can now detect potentially compromised cards at twice the speed, safeguarding cardholders and the financial ecosystem. The intelligent algorithms scan billions of transactions across millions of merchants, uncovering complex fraud patterns previously undetectable. 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.

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.

The AI’s Impact on Education

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. Watch this video to learn how you can extract and summarize valuable information from complex documents, such as 10-K forms, research papers, third-party news services, and financial reports — with the click of a button. In capital markets, gen AI tools can serve as research assistants for investment analysts.

Generative AI models analyze market data, trading patterns, news sentiment, and social media trends, generating sophisticated algorithms for split-second trading decisions. These models update continuously, reacting to changing market conditions with precision. This results in more efficient trading strategies, maximizing returns and minimizing risks, improving market position and profitability.

After a soft launch in September 2023, this new AI-powered assistant quickly demonstrated its superiority over the traditional chatbot by assisting 20% more customers, reducing wait times, and improving overall customer satisfaction. Making part of an integrated solution, generative AI helps to analyze individual customer profiles, market trends, and historical data to offer tailored investment advice. Dedicated algorithms can simulate various financial scenarios and generate personalized recommendations, helping clients make informed investment decisions and enhancing portfolio management. There is a common misconception that generative AI applications in banking boil down to implementing conversational chatbots into customer service.

Integrating generative AI into existing workflows requires a thoughtful approach to ensure seamless integration with existing systems and meet compliance and regulatory needs. Generative AI contributes to more accurate credit scoring by analyzing many non-traditional and unstructured data sources. It can help mitigate biases and help simulate economic scenarios to check the impact of changing conditions on a credit portfolio. Chatbots can assist banks in preventing fraud by monitoring user transactions and spotting unusual activity. Chatbots can assist users in checking their credit ratings and provide advice on how to improve them.

You can foun additiona information about ai customer service and artificial intelligence and NLP. But high tech and banking will see even more impact via gen AI’s potential to accelerate software development. Yes, generative AI uses machine learning to process the training data, understand generative ai banking use cases human input, and then produce outputs based on what we request. Machine learning helps Gen AI models establish patterns and relationships in a given dataset through neural networks.

Generative AI brings precision and predictive power, analyzing vast datasets and generating sophisticated credit scoring models. It evaluates an applicant’s creditworthiness by considering transaction history, social data, and economic indicators, identifying patterns and correlations human analysts might miss. This reduces default risks and improves loan approval rates, enabling banks to offer loans to a broader spectrum of customers.

This powerful technology is reshaping how we learn and teach, offering tools that make education more personalized and effective. As reported by HolonIQ, the global ed-tech market is projected to hit $404 billion by 2025, mostly thanks to advancements in AI. Bank M&A topics will include balance sheet considerations for both the acquiring and acquired financial institutions such as deposits, capital adequacy, credit quality and more. Information around regulatory preparations and concerns as well as credit risks will also be addressed. To provide customized proposals for each customer, AI could be used for a more accurate customer credit scoring based not only on the user’s bank’s profile and credit history, but also social profiles and offline activity. This would allow the bank to generate
a personalized proposal even before the user has requested it.

Banks and financial institutions rely on AI-driven trading strategies to optimize their investments and stay competitive in the fast-paced world of financial markets. Banks can thus benefit significantly from Generative AI-powered fraud detection. It helps prevent financial losses, protects customers from unauthorized transactions, and maintains the institution’s reputation. The battle against financial fraud has taken on new dimensions with the integration of Generative AI in the banking sector. Detecting and preventing fraudulent activities in real-time is crucial to maintaining trust and security within the financial ecosystem. When a customer has a query or needs assistance, the chatbot uses generative AI to analyze the inquiry and provide relevant responses or solutions.

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. Many banks clearly know what they aim to achieve from AI, not only in terms of increased customer satisfaction but also in productivity and efficiency. AI will help to enable banking operations using alternative interfaces, such as voice, gestures, neuro, VR and AR in Metaverse. This will allow the implementation of banking solutions into different experiences. Discover the top DevOps challenges and solutions in this comprehensive blog. Generative AI shines in algorithmic trading thanks to its adaptability and ability to learn.

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Crucially, generative solutions play a vital role in providing a safer financial space for all. The combination of enhanced customer service and internal efficiency positions the technology as a cornerstone of modern retail banking. Risk management is essential to avoiding financial disasters and keeping the business running smoothly.

Generative AI models can handle data extraction tasks that are essential for building financial forecasting solutions. Using these solutions leads to more resilient planning and allows financial businesses to identify emerging opportunities or threats in the market, providing a competitive edge. 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. By generating alerts and providing actionable insights, such AI-driven systems help prevent fraud and mitigate risks effectively. For example, a wealth management firm could implement AI to provide tailored investment strategies and portfolio management for their clients.

As these technologies get better, they can create more engaging, inclusive, and effective learning environments. We need educators, technologists, and policymakers to work together to use AI in a fair and beneficial way. By teaming up, we can tackle the challenges that arise and make AI tools that really better service educational goals.

Writing complex lines of code is an intricate task that requires sharp concentration, and even then, there’s a high chance you’ll end up making a mistake. For example, when you instruct a text-to-image AI model to create an image of a cat smoking a pipe, it scans through all the training images it has been fed. Instead of handing over a manual, you use words around the child, who eventually picks those up from you and starts speaking. If you’re trying to wrap your head around generative AI vs predictive AI, you’re in the right place.

” or provide general recommendations on “How to boost your creditworthiness? ” Generative AI for banking could get even further, enabling customers to make informed decisions. It’s capable of instantly analyzing earnings, employment data, and client history to generate one’s ranking. Customer service and support is one of the most promising Generative AI use cases in banking, particularly through voice assistants and chatbots.

Predict ICU readmissions with accuracy using advanced algorithms and data analysis. IT Operations Analytics (ITOA) is the process of streamlining IT operations through Big Data analysis. Providing innovative solutions to clients endows Ideas2IT to burgeon as one of the leading software solutions and providers at GoodFirms. A Data Masking & Anonymization solution protects PII and can ensure compliance with data privacy regulations like HIPAA, SOC 2, and HITRUST. With data management at the forefront of enterprise evolution, organizations are continually challenged to harness the power of their data efficiently.

This way, organizations can ensure that the deployment of generative AI not only enhances efficiency and innovation but also prioritizes security and regulatory compliance. Last but not least, generative AI algorithms can analyze customer data and preferences to create personalized marketing content and campaigns. Moreover, the rise of regulatory technology (RegTech) solutions powered by AI helped banks navigate increasingly complex regulatory landscapes more efficiently.

AI voice synthesis has many applications—you can use an AI voice to create social media content or produce a song. This saves a lot of time, allowing developers to focus more on implementation. With these tools, you can generate marketing copy, essays, and even full-length novels with simple, short text prompts—and within seconds.