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

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. I compare GPT’s appearance with the launch of the internet, in terms of impacting the future of humanity. It enables machines to understand and generate language interactions in a revolutionary way. GPT (Generative Pre-trained Transformer) AI has the power

to disrupt the way we engage with technology, much like the internet did. Algorithmic trading has become a cornerstone of modern finance, and Generative AI is at the heart of its evolution.

Banks started harnessing vast amounts of data from internal and external sources to gain deeper insights into customer behavior, market trends, and regulatory compliance. AI-driven recommendation engines personalized product offerings, while automated wealth management platforms provided tailored financial advice to clients. Too often, banking leaders call for new operating models to support new technologies. 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.

Potential applications of gen AI in wholesale banking

The advanced machine learning that powers gen AI–enabled products has been decades in the making. But since ChatGPT came off the starting block in late 2022, new iterations of gen AI technology have been released several times a month. In March 2023 alone, there were six major steps forward, including new customer relationship management solutions and support for the financial services industry. 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. In the context of conversational finance, generative AI models can be used to produce more natural and contextually relevant responses, as they are trained to understand and generate human-like language patterns. As a result, generative AI can significantly enhance the performance and user experience of financial conversational AI systems by providing more accurate, engaging, and nuanced interactions with users.

It helps banks and financial institutions assess customers’ creditworthiness, determine appropriate credit limits, and set loan pricing based on risk. However, both decision-makers and loan applicants need clear explanations of AI-based decisions, such as reasons for application denials, to foster trust and improve customer awareness for future applications. When banks expand or work with new client categories, it’s crucial that they provide excellent customer service. This is achieved by addressing FAQs and offering clear guidelines on how to proceed. The information provided should be communicated clearly, using understandable language.

Let’s examine the top applications where this technology is making the most significant impact. Additionally, take note of how forward-looking companies like Morgan Stanley are already putting artificial intelligence to work with their internal chatbots. 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. As a rule of thumb, you should never let Generative AI have the final say in loan approvals and other important decisions that affect customers.

Within a month of the rollout, the generative AI agent did the work of about 700 full-time human agents. The rate of repeat inquires dropped by 25% and resolutions took less than 2 minutes, versus 11 minutes. “Our customers want their systems to take actions,” said Abhi Maheshwari, who is the CEO of Aisera. For enterprises, the first phase of generative AI has been about content creation and answering questions. A series of graphs show predicted compound annual growth rates from generative AI by 2040 in developed and emerging economies considering automation. This is based on the assumption that automated work hours are reintegrated in work at today’s productivity level.

At the same time, the general flow for developing and successfully deploying a generative AI solution in production often consists of 5 foundational steps described below. Automation, Cloud, AI-driven Insights – more than “Dreams of the Future” these have become the “Demands of the Present”, to set the stage for a business to be truly digital. With this archetype, it is easy to get buy-in from the business units and functions, as gen AI strategies bubble from the bottom up. You can start implementing these use cases using Google Cloud’s Vertex AI Search and Conversation as their core component.

Financial institutions have been beta testing Salesforce’s genAI-powered Transaction Dispute Management in “human in the loop” or “copilot” mode with human agents. Fraud dispute resolution is often a huge expense for banks and credit unions and one that causes a lot of client frustration, Tech Target notes in a recent report. The technology is a bot that helps with dispute acknowledgment, case opening, resolution, and closure by invoking policies, procedures, history, and knowledge bases.

When that arrives, it will bring incredible opportunities for banks, including in KYC/AML and anti-fraud work. 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. The 125 billion or so transactions that pass through the company’s card network annually provide the training data for the model.

Answering your top CFPB 1071 compliance questions

To secure a primary competitive advantage, the customer experience should be contextual, personalized and tailored. And this is where generative AI will become the breakthrough technology to ensure it. According to Temenos, 77% of banking executives believe

that AI will be the deciding factor between the success or failure of banks.

Here at Ideas2IT, we offer Generative AI solutions tailored to the banking and financial sectors. Even if a financial institution isn’t yet using the technology, it can learn from peers. Seeing generative AI use cases can help bankers, risk managers, and financial crime professionals better understand it. They can more easily consider how to harness GenAI’s power to enhance their operations, compliance, risk management, and member or customer experience. These models can simulate different market conditions, economic environments, and events to better understand the potential impacts on portfolio performance. This allows financial professionals to develop and fine-tune their investment strategies, optimize risk-adjusted returns, and make more informed decisions about managing their portfolios.

These examples illustrate how generative artificial intelligence is revolutionizing the field by automating routine tasks and analyzing historical finance data. If your focus is just banking, a subset of these use cases are listed in generative AI use cases in banking. At Trinetix, we provide comprehensive technology guidance and end-to-end AI implementation support, so that financial companies can focus on their business priorities and scale market impact.

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. Leveraging gen AI to reinvent talent and ways of working, the top banking technology trends for the year ahead and the mobile payments blind spot that could cost banks billions. Follow him for continued coverage around banks’ tech transformation efforts. Banks also can’t overlook that bad actors have access to these same tools and are moving quickly. Thinking about how your cybersecurity operations centers can leverage generative AI, while recognizing and preventing malicious use cases such as voice replication, will be vital. Banks should prioritize the use of multiple authentication factors to enhance their cyber resilience.

While both use machine learning, there’s a lot more to these AI models than it seems. Stick around to learn the key differences and how they’re reshaping industries worldwide. Partner with us to create transformative GenAI Ed-Tech software that enhances learning and leads the industry. Students, parents, and educators should be fully aware of how AI tools are used https://chat.openai.com/ and their potential implications. Transparency about data usage, the nature of AI interactions, and the goals of AI applications help build trust and ensure that all stakeholders are comfortable with the technology. These systems use natural language to understand and respond to students’ questions, offering explanations and guidance on lots of different topics.

According to the McKinsey Global AI Survey 2021, 56% of respondents report AI usage in at least one function. For banks, generative AI-powered AML practices result in more accurate detection of illicit activities, reduced false positives, and enhanced compliance with regulatory requirements. Banks can safeguard their reputation, avoid hefty fines, and maintain trust with both customers and regulatory authorities.

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. 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. Sometimes, customers need help finding answers to a specific problem that’s unique and isn’t pre-programmed in existing AI chatbots or available in the knowledge libraries that customer support agents can use. That kind of information won’t be easily available in the usual AI chatbots or knowledge libraries. A successful gen AI scale-up also requires a comprehensive change management plan.

The goal is consistency and transparency in resolving transaction disputes and improving retention by resolving employee frustrations. By leveraging its understanding of human language patterns and its ability to generate coherent, contextually relevant responses, generative AI can provide accurate and detailed answers to financial questions posed by users. Specialized transformer models help finance units in automating functions such as auditing, accounts payable including invoice capture and processing. With deep learning functions, GPT models specialized in accounting can achieve high rates of automation in most accounting tasks. However, enterprise generative AI, particularly in the financial planning sector, has unique challenges and finance leaders are not aware of most generative AI applications in their industry which slows down adoption. This unawareness can specifically affect finance processes and the overall finance function.

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. 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. 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. 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.

We expect this space to evolve rapidly and will continue to roll out our research as that happens. To stay up to date on this topic, register for our email alerts on “artificial intelligence” here. Researchers are working on ways to reduce these shortcomings and make newer models more accurate. AI concepts can be complex to understand, so we work hard to present them in a way that’s easy to understand so that anyone can keep up with this dynamic industry.

GenAI use case for understanding financial institution data

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. 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.

For example, it can recommend a credit card based on a customer’s spending habits, financial goals, and lifestyle. When powered with natural language processing (NLP), enterprise chatbots can provide human-like customer support 24/7. It can answer customer inquiries, provide updates on balances, initiate transfers, and update profile information. With the advent of new mechanisms of fraud, which go hand in hand with the advancement of payment technologies, ways to detect and prevent fraud need to be invented. You can foun additiona information about ai customer service and artificial intelligence and NLP. So generative ways of identifying and preventing fraud are a must to adapt to the evolving fraud patterns.

By the way, to learn more about deep learning and its future, read our article. 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. To unlock the real power of generative AI, your organization must successfully navigate your regulatory, technical and strategic data management challenges. Chatbots can provide investment advice and assist users in making informed investment decisions. Banks need to ensure that customers are aware of the chat interface and its benefits, and are comfortable using it. It requires additional product design and education efforts to provide an easy-to-use chat interface

to demonstrate its benefits to customers.

Thanks to generative AI, you can generate new content such as blog posts, websites, music, art, and videos within seconds with just a few prompts. Our team of experts at TechReport has over 12 years of experience testing and reviewing various security products. We’ve tested some of the leading AI products for our AI guides, reviews, and comparisons. Cybercriminals have also taken a liking to AI tools, and new methods such as data poisoning, speech synthesis, and automated hacking are emerging.

So, how far can AI in banking and finance take businesses, and how to implement the technology in practice considering existing limitations, specific business constraints, and the changing market landscape? In this article, we look at the areas where gen AI has the most potential for corporate and investment banks, and the risks that banks need to watch for. We conclude with an outline of the capabilities that banks will need if they are to thrive in the era of gen AI. AI’s impact on banking is just beginning and eventually it could drive reinvention across every part of the business. Banks are right to be optimistic but they also need to be realistic about the challenges that come along with advancements in technology.

These are key essentials you may want to focus on for a successful Gen AI implementation strategy. To establish a solid foundation for building robust generative AI solutions, banks need a comprehensive implementation roadmap to include yet more strategic steps. As a highly experienced generative AI company, ITRex can help you define the opportunities within your business and the sector for generative AI adoption. The integration of generative AI solutions into banking operations requires strategic planning and consideration. One more example is the OCBC bank, which has rolled out a generative AI chatbot for its 30,000 global employees to automate a wide range of time-consuming tasks, such as writing investment research reports and drafting customer responses.

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. Reach out to our AI experts for a tailored generative AI solution for banking. Think about modern infrastructure and systems capable of supporting Gen AI technologies.

With AI-powered tools, educators can plan better lessons, track student progress, and give more helpful feedback. AI can analyze it to find areas where students struggle and suggest ways to help them catch up. Generative AI is making waves in education, thanks to deep learning and machine learning (ML), fundamentally altering how students learn and how educators teach. These AI algorithms can look at tons of educational data to create quizzes, lessons, and feedback that fit each student’s needs. GenAI is a subset of AI technologies designed to create new content, ideas or data that resemble or enhance original human-generated work. Unlike other forms of AI, GenAI produces content based on prompts and directions from a person.

• According to the McKinsey Global AI Survey 2021, 56% of respondents report AI usage in at least one function.
• AI can be used to analyze historical data and make predictions about future customer behavior, which can be used to optimize products and services.
• AI-enabled banking solutions detect unusual patterns and potentially fraudulent activities by analyzing transaction data in real-time.
• 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.
• These generated examples can help train and augment machine learning algorithms to recognize and differentiate between legitimate and fraudulent patterns in financial data.

And Citigroup recently used gen AI to assess the impact of new US capital rules.8Katherine Doherty, “Citi used generative AI to read 1,089 pages of new capital rules,” Bloomberg, October 27, 2023. For slower-moving organizations, such rapid change could stress their operating models. 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. 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. Generative AI can be used to create virtual assistants for employees and customers.

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). According to a study by Forrester, 72% of customers think products are more valuable when they are tailored to their personal needs. Banks can also use Generative AI to require users to provide additional verification when accessing their accounts.

Moreover, it reduces false positives, ensuring that legitimate transactions are not mistakenly flagged as fraudulent. One reason banking professionals have heard so much enthusiasm around using generative AI is its potential financial impact on the industry. For example, Bloomberg announced its finance fine-tuned generative model BloombergGPT, which is capable of making sentiment analysis, news classification and some other financial tasks, successfully passing the benchmarks. Banks want to save themselves from relying on archaic software and have ongoing efforts to modernize their software. Enterprise GenAI models can convert code from old software languages to modern ones and developers can validate the new software saving significant time. Deploy validated AI solutions into operational environments, starting with pilot implementations to mitigate risks and optimize performance.

This view can cover everything from highly transformative business model changes to more tactical economic improvements based on niche productivity initiatives. As a result, the institution is taking a more adaptive view of where to place its AI bets and how much to invest. As per research, 21%-33% of Americans regularly check their credit score, a critical factor in financial health. The score is a three-digit number, usually ranging from 300 to 850, that estimates how likely you are to repay borrowed money and pay bills. An intelligent FAQ chatbot is able to answer questions such as “What is credit scoring?

They use AI to create custom textbooks and learning aids that adapt to students’ needs. By handling content creation, AI lets teachers focus on teaching instead of admin tasks. For example, platforms like DreamBox and Knewton use AI to adjust lesson difficulty on the fly. This means that students receive content that is just right for their current skill level, keeping them engaged and motivated. Research by McKinsey & Company shows that personalized learning can significantly improve student performance—up to a 30% increase in academic achievement and a 60% boost in student engagement.

Generative AI is changing the education game, offering transformative possibilities that promise to enhance learning experiences, personalize education, and increase accessibility. AI’s impact spans personalized learning, enriched educational content, improved teaching methods, and scalable support. However, with these advancements come important Chat GPT ethical considerations, including data privacy, bias, and academic integrity, which must be addressed to ensure responsible AI use. 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.

Another limitation of Generative AI is that it can produce incorrect results if it’s fed with poor or incomplete data due to AI hallucination. First, you must train the Generative AI on your customers’ financial goals, risk profiles, income levels, and spending habits. From there, you can use it to make personalized budgeting and saving recommendations. 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.

It can evaluate not only traditional financial metrics but also alternative data sources such as social media activity and transaction behavior. This holistic view enables more accurate risk assessments, faster loan approvals, and the ability to serve a broader range of customers, including those with limited credit history. 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. Nevertheless, not only decision makers, but also loan applicants require explanations of AI-based decision-making processes, such as the reason why their applications were denied. The reason for such a need is to ensure user trust as well as to increase customer awareness so that they can make more informed applications in the future.

For instance, Morgan Stanley employs OpenAI-powered chatbots to support financial advisors by utilizing the company’s internal collection of research and data as a knowledge resource. 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. 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. Generative AI can analyze customer behavior patterns to predict future actions and preferences.

This personalized approach not only improves client satisfaction but also builds trust and loyalty, as customers feel their unique needs and goals are being addressed. JPMorgan Chase has filed a patent application for a gen AI service that can help investors select equities.3Kin and Carta Blog, “6 enterprise GenAI applications making a big impact,” August 17, 2023. Still others are hung up on concerns about computing cost or stalled because of intellectual-property constraints. Generative AI can transform the loan underwriting process by analyzing vast amounts of data to assess creditworthiness.

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. Discover how to leverage this powerful tool to optimize your AI models with Ideas2IT. Know how organizations can leverage a cloud, while being secure and compliant. Begin your journey here and Be a part of the cloud development industry predicted to grow beyond USD 300 Bn.

Generative AI-driven chatbots are becoming the new face of customer service in banking, enhancing the overall experience for customers while boosting operational efficiency. Many financial institutions have been using artificial intelligence (AI) for years, particularly in supporting cybersecurity and anti-fraud efforts. But Boston generative ai banking use cases Consulting Group (BCG) says generative AI serves a fundamentally different purpose than predictive AI, which is the powerful tool with which many financial institutions are already familiar. Generative AI is a class of artificial intelligence (AI) models that can create new content—text, images, audio, or video—from existing data.

A predictive AI model processes historical data and identifies trends and patterns within that data to make predictions about the future. However, generative AI uses these patterns and relationships to produce new content, such as text, images, voice, and videos. Schools and educational technology providers should be open about how AI systems work, including their data sources, decision-making processes, and potential biases. According to a study by the UKG, 78% of educators believe that transparency in AI tools is crucial for maintaining trust and ensuring effective use in the classroom. Now is the time for community banks and credit unions to get off the sidelines and leverage the power of GenAI. The winners will be the banks and credit unions that are starting to strategize for the future but are now focusing early investments on high-potential and lower-risk applications.

According to the McKinsey Global Institute, generative AI has the potential to generate an additional $2.6 trillion to $4.4 trillion in value annually across 63 analyzed use cases globally. Within industry sectors, banking is poised to benefit significantly, with an estimated annual potential of $200 billion to $340 billion, equivalent to 9 to 15 percent of operating profits. This growth is primarily driven by increased productivity.In today’s landscape of banking and finance, Generative Artificial Intelligence (Gen AI) has emerged as a game-changing catalyst for transformation. Far beyond traditional data processing, Generative AI possesses the remarkable ability to generate insights, solutions, and opportunities that are redefining the financial sector. 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.

GANs are capable of producing synthetic data (see Figure 2) and thus appropriate for the needs of the banking industry. Synthetic data generation can be achieved by different versions of GAN such as Conditional GAN, WGAN, Deep Regret Analytic GAN, or TimeGAN. Swedbank used GANs to detect fraudulent transactions.3 GANs are trained to learn legal and illegal transactions in order to detect the fraudulent ones by creating graphs that reveal their patterns. MSCI is also partnering with Google Cloud to accelerate gen AI-powered solutions for the investment management industry with a focus on climate analytics. For example, today, developers need to make a wide range of coding changes to meet Basel III international banking regulation requirements that include thousands of pages of documents.

In short, generative AI in education makes learning more personal, improves teaching methods, and provides support that scales. As these technologies keep getting better, they’ll make education more effective, engaging, and accessible to all students. Imagine a classroom where you get learning materials that fit you like a glove. AI can whip up customized study guides, interactive lessons, and even real-time feedback that helps both students and educators.

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. Banking leaders appear to be on board, even with the possible complications. 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. Another powerful application is using Generative AI in customer service, for elevated satisfaction.

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. In other ways, a gen AI scale-up is like nothing most leaders have ever seen. As these pilot projects succeed, we can expect this technology to spread across different parts of the industry. Moreover, statistics suggest that it could boost front-office employee efficiency by 27% to 35% by 2026. Financial institutions are already actively employing Gen AI in their operations, and the technology’s potential for transforming the industry is vast.

Furthermore, investment and mortgage calculators tend to utilize technical jargon. This can hinder one’s ability to accurately estimate payments and comprehend the nature of the service. When applying Generative AI for payments, you may find that these complexities become more manageable.

By keeping all information within the bank’s secure environment, OCBC ensures data privacy while empowering its workforce with advanced AI capabilities. Instead, they turned to Gen AI, a powerful tool that swiftly parsed the dense regulatory document, distilling it into key takeaways. This AI-powered analysis empowered risk and compliance teams, ensuring rapid understanding and informed decision-making. A testament to Citigroup’s innovative approach, this move showcases how AI is disrupting the domain in the face of complex regulations. Discover more examples of how Generative AI in banking is transforming the landscape, along with strategic insights to realize its maximum capacity for your organization. Unlike traditional IVR systems, and even many basic AI voice solutions, which often frustrate members with inaccurate information and repetition loops, Olive offers a more personalized and intuitive experience.

However, predictive AI can make predictions and recommendations about the future based on the trends and patterns within its input data. Predictive AI helps businesses, especially retail businesses, understand their market through customer behavior and sentiment analysis. However, predictive AI models not only process this much data but also ensure you get detailed analysis and predictions from the data. As we become a more developed, techno-savvy world, businesses increasingly adopt generative AI to their processes. It goes beyond usual combinations of current information, creating original content customized for the user…. Generative AI can adapt learning materials and experiences to suit various learning styles by analyzing student data and tailoring content accordingly, providing a personalized approach to each student’s preferences.

• Financial institutions must ensure that their AI systems are transparent, secure, and aligned with industry standards to maximize the benefits of this transformative technology.
• This paper also presents several applications and scenarios where the mixture of different Generative AI (GAI) models benefits the Metaverse.
• For the majority of banking leaders, the question of how and where generative AI could deliver the biggest value still stands.
• 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.
• 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.

Additionally, AI-driven algorithms generate detailed financial models and forecasts, providing bankers with a clearer picture of likely consequences. This blend of efficiency, accuracy, and insight is reshaping the landscape, ultimately leading to better outcomes for both investors and clients. Generative AI for banking is a game-changer in the battle against fraudulent activities. By training on past instances of scams and continuously scrutinizing financial operations, it swiftly pinpoints unusual behavior and promptly notifies clients. Generative Artificial Intelligence can also educate on other financial tasks and literacy topics more generally by answering questions about credit scores and loan practices—all in a natural and human-like tone. The online payment platform Stripe, for example, recently announced its integration of Generative AI technology into its products.

This includes lower costs, personalized user experiences, and enhanced operational efficiency, to name a few. When it comes to technological innovations, the banking sector is always among the first to adopt and benefit from cutting-edge technology. The same holds for generative artificial intelligence (Gen AI), the deep-learning technology that can generate human-like text, images, videos, and audio, and even synthesize data for training other AI models.

ChatGPT has shown the benefits of using generative AI in terms of user experience, and the big names are already declaring the launch of rival AI GPT solutions. And the main question for me, as a financial UX strategist, is how AI technology will impact

the banking and financial customer experience. There’s no doubt about the huge potential and possibilities of ChatGPT alike generative Artificial Intelligence (AI) in digital banking and conversational banking in particular.

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.