Generative AI in Banking: Benefits, Use Cases, Examples

calendar Updated March 22, 2024
Maryna Bilan
Former Marketing Manager
Generative AI in Banking: Benefits, Use Cases, Examples

Did you know that a whopping 77% of banking executives believe AI holds the key to their success? Or that over half of all industry leaders are already harnessing AI’s potential? But the real eye-opener: within just three years, Generative AI use cases are projected to deliver a 9% reduction in costs and an impressive 9% increase in sales. Imagine what that means for your bank.

Additionally, take note of how forward-looking companies like Morgan Stanley are already putting Generative AI to work with their internal chatbots. With OpenAI’s GPT-4, Morgan Stanley’s chatbot can now search through its wealth management content. This simplifies the process of accessing crucial information, making it more practical for the company.

Ready to explore the future of banking? Read on and discover how Generative AI can revolutionize your success.

Benefits of Generative AI in Banking Chatbot

Benefits of Generative AI in Finance and Banking

Generative AI chatbots offer a multitude of benefits for the banking industry, including:

  • Human-Like Responses. Generative AI enables banking chatbots to have more human-like, natural, and conversational responses. This helps to create a more engaging and personalized customer experience, which can lead to increased customer satisfaction and loyalty.
  • Multilingual Support. Generative AI enables chatbots to communicate with customers in multiple languages. This allows banks to expand their reach to a global audience.
  • Cross-Selling and Up-Selling. Generative AI enables chatbots to suggest relevant products and services to customers. This is based on their preferences and behavior and increases the likelihood of cross-selling and up-selling.
  • Increased Accessibility. Generative AI applications in banking makes banking services more accessible to people with disabilities. Chatbots can communicate through text, voice, or other means. This approach promotes an inclusive customer experience.

Overall, Generative AI in banking can add significant value to chatbots by improving communication, providing better customer service, reducing fraud, completing risk assessment, and increasing accessibility to banking services. This is done with the help of various technologies including the machine learning model and natural language processing (NLP).

Thinking of incorporating Generative AI into your existing chatbot? Validate your idea with a Proof of Concept before launching. At Master of Code Global, we can seamlessly integrate Generative AI into your current chatbot, train it, and have it ready for you in just two weeks.

Request POC

How Generative AI Is Transforming Banking Sector

In our fast-changing tech era, Generative AI is profoundly transforming banking:

  • Banks now use Generative AI with deep learning models to make lending decisions more precise. They also reduce the chances of people not paying back loans.
  • It also helps automate everyday jobs like checking transactions and spotting fraud, which makes banking cheaper and safer.
  • And AI is changing the way banks help customers. AI-powered chatbots and assistants are available all the time. They quickly answer questions and make customers happier.

Generative AI Use Cases in Banking

Let’s explore the top Generative AI chatbot use cases in banking, along with the advantages of incorporating such chatbots.

Generative AI Finance & Banking Chatbot Use Cases

Fraud Detection and Prevention

Generative AI for banking can assist chatbots in learning from past fraud cases and detecting fraudulent activity by monitoring transactions. If suspicious activity is detected, it can alert customers.

Some examples of how Generative AI banking chatbots can help with fraud detection include:

  • Tracking user behavior, such as location, device, and operating system.
  • Performing additional verification, such as security questions and passwords.
  • Analyzing regular patterns and monitoring deviations.

“In the context of changing global banking landscape, where the demand for face to face banking is decreasing, volumes of digital payments are increasing and payments are being processed in seconds, fraudsters are creatively finding new ways to steal from banks and their customers. Banks need to be agile to respond to threats and embrace new approaches and technologies to predict and prevent fraud. ” – Natalie Faulkner, Global Fraud Lead, KPMG International

Credit Score Questions

Research has shown that between 21% to 33% of Americans check their credit scoring on a monthly basis. A credit 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. A simple chatbot for FAQ could answer questions such as “What is credit scoring?” or provide general recommendations on “How to increase your credit score?” Generative AI banking chatbot could instantly analyze income, employment data, and credit history to generate a credit scoring.

Personalized Investment Options

Forrester reports that nearly 70% of decision-makers in the banking industry believe that personalization is critical to serving customers effectively. These efforts are also backed by company executives. They recognize that personalization is essential for business success. However, a mere 14% of surveyed consumers feel that banks currently offer excellent personalized experiences.

Generative AI has the potential to enhance the personalized customer experience. For example, it can help chat bots give better investment advice to customers. This is because Generative AI can analyze a customer’s investment goals and risk tolerance. It then suggests investment options that are a good fit for them. The chatbot can also look at the investment choices of other customers with similar financial goals and risk tolerance to get more ideas.

Generative AI Investment Banking

Generative AI applications in banking are also making a significant impact on investment operations. With AI, investment bankers analyze data faster, identify opportunities, and assess risks better. This can lead to better decision-making and higher profits for both investors and clients. These improvements can potentially lead to higher profits for both investors and clients.

Financial Literacy

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. A Generative AI banking chatbot powered by deep learning models can be a valuable resource. It offers multi-language customer support and promotes dialogue diversity. Clients can request documentation in different languages. This improves their understanding of important financial concepts, banking products, and services.

Dialogue Example of Generative AI Finance Chatbot

Credit Card Recommendation

Generative AI chatbot could be helpful for customers looking for the right banking card. The chatbot could provide personalized recommendations based on the customer’s spending habits, financial goals, and lifestyle. It could also explain the features of different cards, compare them, and guide customers through the application process.

With this support, customers could make informed decisions and choose the card that best suits their needs. Ultimately, the AI chatbot could provide a convenient and efficient way for customers to find the right banking card.

Conversationalize Mathematics

Investing, regulated cryptocurrencies, stock trading, and exchange-traded funds can be needlessly complex. However, the mission of banks is to ensure the accessibility of financial services. This should be true regardless of one’s net worth or financial literacy.

With Generative AI chatbot technology, banks can simplify financial services. The result is financial services that are easy to understand, transparent, and low-cost.

It allows users to ask math-related questions in a more conversational manner. For instance, one might ask, “If I invest $X at Y% interest for Z years, what will my return be?” Alternatively, they might ask, “What would be the difference in my monthly mortgage payments if I choose a variable rate of X% or a fixed rate of Y%?”.

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. However, when applying Generative AI for payments, you may find that these complexities become more manageable.

Financial Recommendations

Understanding and determining customer needs in order to recommend solutions specific to those needs while exercising discretion in confidential matters is key to building perfect customer relationships and loyalty. A Generative AI banking chatbot can make savings recommendations for certain accounts based on previous user activity. For example, if you add $XX more to your retirement savings plan (RRSP), you could receive a higher return of $$.

Another use case for a Generative AI chatbot is to provide financial product suggestions that can help users with budgeting. For instance, the chatbot could automatically transfer a certain amount of every pay cheque into a savings account and potentially set alerts for when a certain amount of money is spent.

3 Real-Life Banking Generative AI Examples

Organizations and banks, such as Swift, ABN Amro, ING Bank, BBVA, and Goldman Sachs, are experimenting with Generative AI. These financial leaders are exploring automation, improved customer interactions, and behavior analysis. They set the stage for exploring specific examples of Generative AI’s potential in the sector.

Morgan Stanley’s Chatbot

Morgan Stanley has introduced a clever chatbot, powered by OpenAI, to assist its financial advisors. This chatbot has been tested with 300 advisors and is set to be used widely soon. It’s designed to help Morgan Stanley’s 16,000 advisors tap into the bank’s vast research and data resources.

This chatbot is like a super-smart helper. It uses advanced technology to quickly provide answers based on the bank’s research, which reduces mistakes. The bank even has humans checking the answers to make sure they’re right. This chatbot is all about helping advisors provide the best service possible to their clients.

ChatGPT-Style CFO Tool from Brex

Brex, the finance technology company, is getting ready to launch a powerful tool in collaboration with OpenAI to help CFOs and their teams. This tool uses clever AI technology and is designed to give instant answers to financial questions. It’s similar to having a clever financial assistant. It can inform you about your spending, show trends, and even compare your business to others in the same industry.

Through the Brex Empower platform, CFOs will be able to chat with this AI assistant and get insights about their budgets and spending. This makes it easier for finance leaders to make important decisions and help their companies grow. These AI features will be available later in 2023 through the Empower platform.

ChatGPT-Style CFO Tool, Brex
ChatGPT-Style CFO Tool, Brex

JPMorgan Chase’s AI Investment Assistant IndexGPT

JPMorgan Chase is a big name in finance, and they’re known for being really good at what they do. Now, they’re using the power of AI to make investing easier. They’re creating a smart software service, kinda like ChatGPT, to help people decide where to invest their money.

It’s called IndexGPT, and it looks at a lot of data and uses artificial intelligence to pick investments that fit what each customer needs. It’s like having a personal advisor who’s really good with money. The bank has even applied to trademark this new product, so they’re serious about it. So, JPMorgan Chase is taking a big step into the future with AI, and it could change how we all manage our money.

Limitations of Generative AI in Banking

Generative AI offers promise for the banking industry. However, it’s equally crucial to acknowledge the LLM limitations associated with its implementation:

  • Data Quality: The effectiveness of Generative AI relies on high-quality training data. Financial institutions need access to vast, precise, and relevant data. Flawed or incomplete data can lead to inaccurate AI predictions, emphasizing the importance of data quality.
  • Privacy and Security: The effectiveness of Generative AI relies on high-quality training data. Financial institutions need access to vast, precise, and relevant data. Flawed or incomplete data can lead to inaccurate AI predictions, emphasizing the importance of data quality.
  • Lack of Sufficient Data to Train On: Generative AI for banks may struggle with bias and accuracy if training data lacks diversity. This can lead to unfair AI-driven decisions, posing ethical concerns and reputational risks. Ensuring comprehensive data representation is vital for fairness.
  • Numerical Accuracy: Numerical accuracy can be a concern with Generative AI models. Implementing safeguards, such as human validation, prevents AI errors in financial decisions.
  • Hallucinations: Generative AI applications in banking can produce biased or unfair results due to training data biases. It’s important to address these ethical issues for fair and transparent financial AI applications.

Financial organizations must adopt a cautious, responsible approach to integrate Generative AI. With proper mitigation strategies, banks will be able to maintain customer trust and data security.

Future of Generative AI in Banking

The future of Generative AI in banking and finance looks incredibly promising. Generative AI can be used to create virtual assistants for employees and customers. It can speed up software development and make lots of customized content. It’s expected that Generative AI for banks could boost productivity in the industry by 2.8% to 4.7%, adding about $200 billion to $340 billion in revenue.

Moreover, statistics suggest that it could boost front-office employee productivity by 27% to 35% by 2026. This will result in up to $3.5 million in additional revenue per employee. Financial institutions are already beginning to use Generative AI, and the future looks promising.

Starting internally and trying it out with employees is the first step. Still, there’s optimism for wider use. As these pilot projects succeed, we can expect this technology to spread across different parts of the industry. This will bring positive change to banking and finance.

Master of Code’s Generative AI Solutions for Banking

At Master of Code, we specialize in integrating Generative AI with your existing chatbots. This gives your finance and banking operations a significant advantage. Our customized solutions enable your chatbot to create human-like and contextually relevant responses. Such integration allows your financial institution to automate intricate tasks, improve customer service, and remain competitive.

Our expertise doesn’t stop at integration. We work closely with you to understand your specific banking and financial service needs. This way we ensure the Generative AI-powered chatbot aligns perfectly with your objectives.

Whether you already have a chatbot or are considering developing a new one with Conversational AI for finance, our experienced team is ready to guide you through the process. By choosing Master of Code, you’re not just investing in technology. You’re investing in a partner dedicated to helping your institution thrive in the digital era.

Request a Demo

Don’t miss out on the opportunity to see how Generative AI chatbots can revolutionize your customer support and boost your company’s efficiency.

    By continuing, you're agreeing to the Master of Code
    Terms of Use and
    Privacy Policy and Google’s
    Terms and
    Privacy Policy

    Also Read

    All articles