Generative AI in Banking: Real Use Cases & 11 Banks Using AI

Just as everyone possesses a unique thought process, Generative AI generates diverse outputs even with identical input data. Furthermore, the results may exhibit slight variations even when confronted with the same input query, evolving over time. AI can be used to provide personalized financial advice and recommendations to customers, based on their individual data and preferences. This can help customers make more informed financial decisions, and potentially improve their financial well-being.

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. At the end of the day, banks must learn to embrace Generative AI to survive. With Generative AI still in its infancy, now is the time to learn how to implement it in your business. Your business can then evolve with it to start with Generative AI step by step.

Most importantly, the change management process must be transparent and pragmatic. While Erica hasn’t yet integrated Gen AI capabilities, the bank is actively exploring its potential to further enhance the customer journey. 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, 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. These algorithms simulate human-like interactions, offering empathetic answers and solutions that resonate with debtors, thereby reducing hostility and improving collection outcomes.

AI algorithms deployed to monitor transactions for compliance violations, ensure data privacy, and enhance cybersecurity measures bolstered customer trust and loyalty as digital banking was gaining traction. Generative AI models analyze customer data, generating personalized marketing campaigns and product recommendations. This extends beyond generic offers, crafting targeted messages and content that resonate with customers’ preferences and needs.

Generative AI-driven chatbots engage customers in natural, human-like conversations, providing instant assistance 24/7. These bots understand context, sentiment, and language nuances, making interactions seamless and personalized. They handle tasks like checking account balances, explaining transaction details, and helping with account setup. This enhances customer satisfaction, reduces operational costs, and improves response times while collecting valuable customer generative ai banking use cases data. In the financial services industry, new regulations emerge every year globally while existing rules change frequently, requiring a vast amount of manual or repetitive work to interpret new requirements and ensure compliance. Developers need to quickly understand the underlying regulatory or business change that will require them to change code, assist in automating and cross-checking coding changes against a code repository, and provide documentation.

It will access a wider range of secure information sources, providing answers on products, services, and even career opportunities within the NatWest Group. Cora+ aims to be a safe, reliable digital partner, helping clients navigate complex queries with ease and improving accessibility to data. The tool is designed to assist with writing, research, and ideation, boosting productivity and enhancing customer service.

The companies envision using the technology to generate responses to internal inquiries, create and check various business documents, and build programs. A good example is Wells Fargo’s generative AI virtual assistant named Fargo. The assistant has reportedly handled 20 million interactions since it was launched in March 2023 and is poised to hit 100 million interactions annually. 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. For all GenAI applications in financial services, not just in banking, read our article on generative AI in financial services.

Generative AI in Banking: Use Cases, Ethical Implications, and More

By fostering a culture of integrity, schools can maintain the value of educational achievements and ensure that AI is used ethically. First, we must make sure schools follow the rules, like FERPA in the US and GDPR in Europe. Then, they need to get serious about security and have clear plans for managing data. Generative AI’s impact on education is broad, touching on various aspects of the educational experience. Issues such as data privacy, algorithmic bias, and academic integrity are critical concerns we have to deal with.

In this article, we’ll dive into how AI is changing education—the good and tricky parts. We’ll also examine how AI can aid students with disabilities, making learning more accessible. Plus, we’ll spotlight innovative startups pushing the boundaries in ed-tech and consider what the future holds for AI in education. The key is to establish ethical AI practices, which begins with understanding your institution’s risk tolerance, establishing ethical and governance frameworks and preparing for regulatory and compliance agreements. A critical aspect of this undertaking is establishing an ethical culture and holding your organization to a higher standard than the bare minimum expected from regulators. The Current Role of GenAI in Banking
Just because GenAI produces output that mimics that of humans doesn’t mean it’s going to replace them.

This refers both to unregulated processes such as customer service and heavily regulated operations such as credit risk scoring. Overall, the switch from traditional AI to generative AI in banking shows a move toward more flexible and human-like AI systems that can understand and generate natural-language text while taking context into account. This is instrumental in creating the most valuable use cases in both customer service and back-office roles. The use of Generative AI and machine learning in banking is not limited to the US or Canada. Financial institutions and banks in India are also utilizing enterprise chatbots and machine learning for AI-powered banking applications such as voice assistants and fraud detection. Global adoption of gen AI initiatives involves strategic road mapping, talent acquisition, and managing new risks.

AI helps to refine loan and credit scoring processes by generating detailed risk profiles for potential borrowers. Used in combination with data analysis tools and dedicated machine learning, it helps lenders make more accurate credit decisions and offer personalized loan terms. The adoption of AI in banking accelerated further with the integration of big data analytics and cloud computing technologies.

Large Language Model Evaluation in 2024: 5 Methods

The excitement kicked up by generative AI, or GenAI, has some banks exploring its uses. Knowing how AI and GenAI are being used by peers and fraudsters will help financial institution leaders and management vet potential solutions and watch for risks. By learning from historical financial data, generative AI models can capture complex patterns and relationships in the data, enabling them to make predictive analytics about future trends, asset prices, and economic indicators.

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. “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. And it’s a good summary of wholesale banking’s stance on AI and its subset machine learning.

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. This customized, proactive approach empowers users to take control of their financial health, reduce stress, and confidently achieve their goals. The success of interface.ai’s Voice Assistant at Great Lakes Credit Union is just one of many Generative AI use cases in banking that showcase the transformative impact of this technology. By significantly improving call containment rates, enhancing member satisfaction, and elevating employee roles, Voice AI has become a cornerstone of GLCU’s strategy to deliver exceptional member support.

At this very early stage of the gen AI journey, financial institutions that have centralized their operating models appear to be ahead. About 70 percent of banks and other institutions with highly centralized gen AI operating models have progressed to putting gen AI use cases into production,2Live use cases at minimal-viable-product https://chat.openai.com/ stage or beyond. Compared with only about 30 percent of those with a fully decentralized approach. Centralized steering allows enterprises to focus resources on a handful of use cases, rapidly moving through initial experimentation to tackle the harder challenges of putting use cases into production and scaling them.

Let’s explore more details and specific use cases of Generative AI in banking and financial services. This helps financial institutions and banks identify potential defaulters based on their past records, thereby preventing potential fraud. Just like GenAI, predictive AI models are trained on historical data and use machine learning to identify patterns and establish relationships within the data using statistical analysis. Generative AI, widely known as artificial intelligence capable of creating new content based on learned patterns, is akin to the human creative process.

Educational institutions should provide clear information about AI tools and obtain consent before implementation. This way, we respect privacy and make smart choices together—teachers, students, and tech providers working as a team. To address these concerns, educational institutions must draw a clear line in the sand. They should set strict guidelines for AI use, and educators should drill into students the importance of original work.

Whether it’s checking account balances, explaining transaction details, or helping with account setup, these chatbots can handle a wide range of tasks, freeing up human agents to focus on more complex issues. It enables them to offer loans Chat GPT to a broader spectrum of customers, including those who may have been previously overlooked or considered too risky. Gen AI takes into account a wide range of factors, including transaction history, social data, and economic indicators.

Generative AI, powered by advanced machine learning models, including gen AI models, is revolutionizing the banking and financial sectors. This technology is reshaping the landscape of AI and automation in banking by introducing efficient solutions to automate previously time-consuming tasks. Generative AI, leveraging advanced machine learning models, is revolutionizing the banking and financial sectors. This technology is reshaping the landscape of AI and automation in banking by introducing efficient solutions to automate traditionally time-consuming tasks.

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. A financial institution can draw insights from the details explored in this article, decide how much to centralize the various components of its gen AI operating model, and tailor its approach to its own structure and culture. An organization, for instance, could use a centralized approach for risk, technology architecture, and partnership choices, while going with a more federated design for strategic decision making and execution. 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. 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.

Additionally, AI-driven wealth management can reduce operational costs and increase the scalability of services. Generative AI models can analyze a vast array of financial data, economic indicators, market trends, and individual client profiles. Using this data, AI can generate predictive models that recommend optimal asset allocations and investment strategies.

Define clear objectives for integrating generative AI, identifying key stakeholders, and establishing governance frameworks. With IndexGPT, J.P. Morgan aims to revolutionize financial decision-making and enhance outcomes for individual investors in the region. In this insightful article, we explore eleven compelling use cases demonstrating how Generative AI benefits the banking industry. This, in my opinion, is where the ultimate potential of AI lies—helping humans do more work, do it better, or freeing them up from repetitive tasks. Each successive FinTech innovation that came along incrementally transformed banking across its multiple functions, one by one, until generative AI entered the scene to drastically reinvent the entire industry.

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. Morgan Stanley also introduced an AI assistant powered by OpenAI’s GPT-4, enabling its 16,000 financial advisors to access a repository of approximately 100,000 research reports and documents instantly. The AI model is designed to assist advisors in efficiently locating and synthesizing information for investment and financial inquiries, providing tailored and immediate insights. Drawing insights from approximately 125 billion transactions processed annually through its card network, Mastercard leverages this vast dataset to train and refine the AI model. 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. Now, the race is on to do so again with an even more transformative technology.

Generative AI helps you make new content, whereas predictive AI helps you make predictions. AI developers should focus on creating systems that are inclusive, unbiased, and respectful of user privacy. Getting consent from everyone involved is crucial when we bring AI into schools. Students, parents, and teachers need to know how AI will be used, what data will be collected, and how it will be kept safe. A survey by the National University showed that 80% of parents worry about AI invading their kids’ privacy, so educators and ed-tech providers need to be upfront and honest.

Empower edge devices with efficient Audio Classification, enabling real-time analysis for smart, responsive AI applications. Revolutionize enterprise creativity with Generative AI—unleash innovation, automate tasks, and enhance business intelligence. At its core, Enterprise Search is like a supercharged search engine for businesses. It allows organizations to quickly and efficiently locate data and documents stored across various platforms and repositories.

Making purposeful decisions with an explicit strategy (for example, about where value will really be created) is a hallmark of successful scale efforts. 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. By analyzing customer data and then making personalized product recommendations.

It should combine analysis of the user’s financial activity, their social environment and big data analysis on typical behavioral patterns, geolocation data and contextual analysis. The mobile apps and websites of many FIs are often loaded with redundant promotional information about the FI itself and the benefits of its products and services. But, if this specific information is not relevant to the customer, it just becomes annoying
and creates a feeling of pushiness. It requires true empathy toward the customers─getting to know them, feeling their pain like your own and delivering a solution that will make their lives better and easier. The banking industry has been pressured to adapt new technologies for some time now. The growing pressure from competition with Big Tech companies and the emerging number of Fintechs was largely accelerated by the impact of the pandemic, leaving no choice
but to take immediate action.

As a result of this study, it appeared that training GANs for the purpose of fraud detection produced successful outcomes because of developing sensitivity after being trained to identify underrepresented transactions. This is an especially important application for financial services providers that deal with enormous number of transactions. Without the right gen AI operating model in place, it is tough to incorporate enough structure and move quickly enough to generate enterprise-wide impact. To choose the operating model that works best, financial institutions need to address some important points, such as setting expectations for the gen AI team’s role and embedding flexibility into the model so it can adapt over time. That flexibility pertains to not only high-level organizational aspects of the operating model but also specific components such as funding. Banks and other financial institutions can take different approaches to how they set up their gen AI operating models, ranging from the highly centralized to the highly decentralized.

• Generative AI models analyze vast amounts of market data, historical trading patterns, news sentiment, and even social media trends.
• But scaling gen AI will demand more than learning new terminology—management teams will need to decipher and consider the several potential pathways gen AI could create, and to adapt strategically and position themselves for optionality.
• Financial institutions are already actively employing Gen AI in their operations, and the technology’s potential for transforming the industry is vast.
• 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).

Scale AI initiatives gradually across different banking functions, ensuring seamless integration with existing workflows and systems. As a major player in the Dutch banking sector, ING used to handle 85,000 customer interactions weekly, but their existing chatbot could only resolve 40-45% of these, leaving 16,500 customers requiring live assistance. For the past ten years, machine learning and AI in banking have undergone a myriad of changes. However, employing GANs for fraud detection has the potential to generate inaccurate results (see Figure 1), necessitating additional improvement. It can also be distant from the business units and other functions, creating a possible barrier to influencing decisions.

Challenges:

Generative AI also excels in creating educational content that is engaging and interactive. AI-driven tools can generate a variety of learning materials, including practice exercises, quizzes, and even multimedia resources like videos and simulations. This capability not only enriches the learning experience—it also saves teachers a ton of time and effort. Generative artificial intelligence (AI) is changing the game in many industries, and education is no exception.

While AI chatbots are indeed a common use case in the sector, there is much more behind the technology, and a number of large market players are already taking advantage of this promising potential. By analyzing large volumes of data at high speeds, AI algorithms provide actionable insights that enable faster and more informed decision-making. For instance, AI-powered risk assessment models can swiftly evaluate creditworthiness and detect fraudulent activities, reducing decision-making time and enhancing accuracy.

Generative AI will continue to attract investment dollars and attention from financial services companies and other industries as businesses continue efforts to use technology to improve efficiency, products and services, and performance. Understanding what genAI is, how credit unions and banks are using it now, and how to tap into additional resources on genAI will help leaders explore the potential for it within their own financial institutions. First, it can analyze customer data to understand their preferences and needs, and use this information to provide personalized customer service and support to users, addressing their queries and concerns in real time. It could include customized financial
advice, targeted product recommendations, proactive fraud detection and the reduction of support wait times to zero. Generative AI can guide customers through onboarding, verifying identity, setting up accounts and providing guidance on available products
and services. AI plays a significant role in the banking sector, particularly in loan decision-making processes.

Data leaders also must consider the implications of security risks with the new technology—and be prepared to move quickly in response to regulations. While implementing and scaling up gen AI capabilities can present complex challenges in areas including model tuning and data quality, the process can be easier and more straightforward than a traditional AI project of similar scope. BBVA is leading the charge in European banking by deploying ChatGPT Enterprise to over 3,000 employees, making it the first bank on the continent to partner with OpenAI.

Get started with the installation and configuration using Docker and you can skip all the complex steps to use PSQL in local development. Only 7% of US healthcare and pharma companies have gone digital and there is already a data explosion – EHRs, Physician Referrals, Discharge Summary, etc. The OAuth 2.0 authorization framework allows a user to grant third-party application access to the user’s protected resources without revealing their long-term credentials. The Internet of Medical Things (IoMT) represents medical devices and applications that connect to healthcare IT systems through the internet. The Autoprototype module automates the tedious rapid prototyping process for given data and selects appropriate hyperparameters.

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Is ChatGPT predictive AI?

It’s only been two months since the launch, but we can already see how much ChatGPT impacts our experience. The internet is full of examples of crazy prompts, to which ChatGPT provides accurate and competent answers. It has already become a personal AI assistant and advisor for millions of content creators, programmers, teachers, sales agents, students, etc. Learn how to forecast and mitigate patient appointment no-shows for improved scheduling and resource management. Learn how to create a compelling business case for AI/ML projects using first principles, 80/20 principle, and risk analysis to maximize ROI and avoid pitfalls. Electron JS is a runtime framework that allows a user to create desktop applications with HTML5, CSS, and JavaScript.

These records can enhance risk management, automate data collection, and streamline reporting, leading to further digitalization, end-to-end customization, better client segmentation, and retention. 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. This personalized approach helps customers make informed financial decisions, achieve their financial goals, and improve their overall financial well-being. The second factor is that scaling gen AI complicates an operating dynamic that had been nearly resolved for most financial institutions. While analytics at banks have been relatively focused, and often governed centrally, gen AI has revealed that data and analytics will need to enable every step in the value chain to a much greater extent.

This enhances customer engagement, drives conversion rates, and increases customer loyalty, leading to higher satisfaction and better return on marketing investments. The use of synthetic data has the potential to overcome the challenges that the banking industry is facing, particularly in the context of data privacy. Synthetic data can be used to create shareable data in place of customer data that cannot be shared due to privacy concerns and data protection laws. Further, synthetic customer data are ideal for training ML models to assist banks determine whether a customer is eligible for a credit or mortgage loan, and how much can be offered. 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.

Further, this paper also enumerates the limitations and challenges of Generative AI models and the areas of future work. A Word About Ethics and Regulations
One reason the leaders of community banks and credit unions are reluctant to embrace GenAI is a concern about compliance. 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. Second, generative AI can automate many routine tasks, such as account balance inquiries and password resets, freeing customer service representatives to focus on more complex issues.

In February 2024, Mastercard launched a cutting-edge generative AI model designed to enhance banks’ ability to identify suspicious transactions across its network. 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.

When it comes to generative artificial intelligence (GenAI), the prevailing attitude among some bankers is that they’re comfortable with AI but not so sure about GenAI. Like all other companies, Cigniti Technologies has its product on generative AI, which addresses different use cases. The model can also generate the required code for software application implementation.

Given the nature of their business models, it is no wonder banks were early adopters of artificial intelligence. Over the years, AI in baking has undergone a dramatic transformation since machine learning and deep learning technologies (so-called traditional AI) were first introduced into the banking sector. With the release of Python for Data Analysis, or pandas, in the late 2000s, the use of machine learning in banking gained momentum.

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. You can foun additiona information about ai customer service and artificial intelligence and NLP. 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 when dealing with sensitive customer data.

Generative AI models can predict market trends and identify potential risks by analyzing historical data, economic indicators, and market sentiment. These models generate scenarios and forecasts, helping banks make informed decisions about risk management and investment strategies. This proactive approach to risk management ensures that banks can mitigate potential threats and capitalize on emerging opportunities. Generative AI-driven fraud detection systems constantly monitor transactions, identifying irregularities. These systems employ machine learning models that analyze historical data and generate predictive models to detect fraudulent patterns. They adapt to new data, reducing false positives and ensuring legitimate transactions are not mistakenly flagged.

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. 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. 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.

A financial services firm, for example, might use AI to enhance its economic forecasting models. This would help them make better strategic decisions, optimize resource allocation, and anticipate market movements, leading to more resilient financial planning and identifying emerging opportunities or threats. For example, a commercial bank might use AI to monitor transactions for signs of money laundering and other financial crimes. In this case, the technology allows to analyze transaction patterns and generate alerts for suspicious activities, helping the bank comply with regulatory requirements and improve overall risk management strategies. While traditional machine learning and artificial intelligence have demonstrated efficiency across various aspects of financial management and banking, generative AI stands out as a true game changer for the industry.

It can identify subtle patterns and correlations that human analysts might miss, ultimately reducing default risks and improving loan approval rates. Pentagon Federal Credit Union (PenFed) provides the status of loan applications, product and servicing information, and technical support to members nearly 40,000 times a month using a Salesforce Einstein-powered chatbot. The chatbot generates answers to members’ questions and now resolves 20% of member cases on first contact, according to a report on CIO.com. The reduced pressure on its call center has allowed PenFed to cut its time to answer calls by a minute, to just under 60 seconds, despite increased membership. Despite being cautious, many financial institutions have already begun using generative AI and looking for additional uses that will improve client experiences and staff efficiency. Establish continuous monitoring mechanisms to track AI performance, data quality, and regulatory compliance post-deployment.

This tailor-made approach is not just a theoretical possibility—it’s already boosting educational outcomes by catering to diverse learning styles. Earlier this year, Q2 Executive Fellow Carl Ryden wrote an article about the 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. 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. The responsible implementation of ongoing monitoring and adaptability of generative AI models are essential for the security of banking operations and maintaining individuals’ data privacy.

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.

Additionally, the technology relies on market trends and economic forecasts to provide up-to-date investment insights. But manually sorting through, analyzing, and signing off on various financial documents and applications can take a lot of time and money. To cut operational costs, banks can have gen AI models comb through large volumes of documents to identify important data or summarize them for review. Generative AI models can identify patterns and relationships in the data and even run simulations based on hypothetical scenarios. From there, it can help banks evaluate a range of possible outcomes and plan accordingly.