Generative AI Use Cases in Banking: From Customer Support to Risk Management

Generative artificial intelligence (gen AI), a technology that can create a wide range of content, from text and images to music and voices, has already become widely used in the financial world. While many regular users are now noticing that their interactions with financial institutions are often handled by generative AI chatbots, the impact of this technology goes far beyond the mere improvement of customer satisfaction. Generative AI in the banking and financial sector is also transforming areas like risk management, compliance, and everyday banking operations.

In this article, you will learn more about the role of gen AI in the financial service industry and explore its future trends and associated potential risks.

Customer Experience and Personalization

One of the core roles of generative AI in banking is helping financial institutions connect with their customers in a faster, smarter, and more personalized way.

Enhanced Customer Service and Chatbots

Many bank customers have already experienced the generative AI revolution in customer support. When integrated properly, chatbots powered by generative AI can provide faster, smarter assistance, available anytime.

Unlike traditional call centres in the financial and banking industry, AI chatbots are accessible 24/7, answering questions and solving problems without the wait. Powered by natural language processing (NLP), a technology that helps computers understand human language, the bots in the banking industry are designed to respond in a smooth, human-like manner.

A major improvement in bank customer support is the chatbot’s ability to remember past conversations. This increases customer engagement and saves time for both individuals and financial organizations, as there is no need to repeat information. Thanks to context awareness and learning from each customer’s preferences, chatbot responses are more personalized and relevant.

Another key advancement provided by generative AI in banking is that chatbots can handle multiple tasks simultaneously. From checking account balances to paying bills and even offering basic financial advice, chatbots now cover a wide range of everyday banking needs. With bots that can chat with thousands of customers at the same time, AI makes it easy for customer support to grow. This scalability is something no human team could ever keep up with.

Personalized Financial Advice and Marketing

Smart data analysis powered by gen AI tools can also be leveraged to offer tailored advice and personalize marketing in the banking sector.

By analyzing customer data like spending, saving habits, and financial goals, AI now helps banks see the full picture. This deeper understanding lets banks offer highly personalised advice, like custom savings plans, investment tips, or loan options. Bank chatbots can now suggest smart moves in real time, such as adjusting a budget, highlighting a good investment, or reminding customers about upcoming bills. Thanks to AI, support feels more proactive and helpful, often stepping in before the customer even asks.

AI also supports smarter marketing and hyper-targeted campaigns. By grouping customers by behavior, income, or financial goals, banks can create highly personalized marketing, like offering the right credit card or loan to the right person at the right time.

This power of generative AI technology can also improve customer satisfaction and reduce churn. If AI spots that a customer might leave, for example, by noticing less account activity, it can trigger loyalty rewards or tailored incentives to keep them engaged.

Gen AI models can also be leveraged for creating marketing content to draft customized messages for emails, apps, or social media, adjusting the tone and offers based on past interactions.

Generative AI helps banks connect better with customers and boost their loyalty by offering advice that feels more personal. It also saves time by automating reports and marketing tasks, making operations more efficient. Plus, by sending the right offers to the right people, banks can increase their sales and grow revenue.

Risk and Fraud Management

Today, banks and financial companies are increasingly using generative AI to fight fraud, money laundering, and other financial crimes. Gen AI can create realistic examples of transactions and threats, helping improve the discriminative AI models that spot and stop fraud in real life.

Fraud Detection and Prevention

Although financial crimes keep changing, generative AI is also getting stronger, helping banks stay one step ahead. The flexible, real-time solutions powered by artificial intelligence can significantly outperform the traditional methods used in the existing banking systems.

Generative AI is helping banks strengthen their fraud detection by creating realistic examples of fake transactions, known as synthetic data. This gives fraud detection systems more training material without exposing real customer information. With large and varied training data, discriminative AI models, designed to spot unusual activities like strange spending habits or login locations, become better at detecting even rare and complex fraud patterns.

Abundant training data is necessary for improving the ability of the fraud detection systems to quickly tell the difference between harmless anomalies, such as a customer making purchases while travelling, and real threats. Additionally, adaptive learning, another generative approach, helps AI models evolve by learning from new fraud tactics, such as deepfake scams.

Together, generative and discriminative AI take fraud prevention to the next level. Real-time monitoring becomes sharper, instantly catching suspicious activities like large transfers or account takeovers. Generative AI also simulates new fraud scenarios to keep detection systems ready, while discriminative AI analyses live transaction patterns to stop real threats.

This teamwork has already proved to be successful. For instance, PSCU, a group that supports 1,500 credit unions across the US, uses an AI-powered fraud detection system for analysing patterns in transactions and spotting suspicious activity across different accounts, banks, and even merchants. With it, PSCU managed to stop $35 million in fraud over just 18 months.

Credit Risk Assessment and Loan Underwriting

Generative AI is helping banks assess credit risk by analyzing customer data, from social media activity to utility payments. This approach allows organizations in the financial and banking industry to evaluate the creditworthiness of individuals who may not have a traditional credit history, such as those without access to bank loans.

Thus, gen AI can potentially reduce bias in the credit approval process and make loan underwriting fairer and more inclusive. At the same time, by recognizing patterns that indicate a borrower’s likelihood to repay, generative AI models can improve decision-making and help create healthier loan portfolios.

By automating time-consuming tasks like analyzing tax returns and financial statements, AI helps financial institutions speed up loan processing and cut resources required by tasks like drafting reports and reviewing documents.

Risk Management and Compliance

Generative AI can significantly improve the way in which financial institutions manage risks and stay compliant with regulations. By quickly analyzing large amounts of data, such as emails, contracts, and news articles, generative AI can spot potential risks and generate detailed reports much faster than humans. This automation saves time, reduces errors, and ensures that risk managers always have the latest information.

AI helps banks stay ahead of potential problems by continuously monitoring data, which is necessary for taking action before issues become bigger. It also makes regulatory reporting simpler as generative AI can understand complex rules and automatically generate the required reports, helping banks keep up with changing laws and reduce manual effort.

Plus, AI can update internal policies to match the latest regulations, making compliance easier and more efficient.

Read more: Data Science in Finance – Why It Is Beneficial to Use It

Financial Strategy and Forecasting

Managing wealth and planning finances are most effective when they are proactive and based on data, rather than just reacting to market changes. With AI, which can quickly analyze huge amounts of past and live financial data, banks and investors now get faster forecasts, smarter investment strategies, and stronger risk protection.

Financial Forecasting

Modern banking leverages the ability of generative AI models to learn from historical data and make future predictions. This helps financial institutions to make their operations more flexible and dynamic, and prepare for potential risks more accurately and cost-effectively.

Generative AI enhances financial forecasting by creating realistic financial data to fill gaps in past records, especially for rare events like market crashes. It also updates forecasts in real-time by processing live market data, news, and trends, helping banks stay ahead of sudden changes. By uncovering hidden patterns in historical data, it can predict future market behavior with greater precision. Additionally, generative AI customizes forecasts for individual clients, taking into account their specific financial situations.

The high scalability of gen AI applications allows them to process the amounts of data that cannot be analyzed by humans. At the same time, automated data processing and analysis can also considerably reduce errors compared to traditional methods.

Scenario Simulation

Generative AI helps the financial and banking industry simulate potential financial scenarios to better prepare for risks. To stress-test a bank’s ability to handle crises, generative AI models mimic a variety of extreme situations from market crashes and hyperinflation to sudden interest rate hikes, geopolitical crises, and large-scale natural disasters.

Furthermore, generative AI can also simulate unpredictable events, like cyberattacks, that traditional data cannot capture.

Algorithmic Trading and Portfolio Optimization

More and more financial organizations and individual traders are turning to generative AI to enhance their investment and trading strategies with better, data-driven decisions.

A key AI tool in this area is algorithmic trading, where computers automatically execute trades based on set rules and data patterns, boosting efficiency and minimizing human error. Algorithmic trading analyzes vast amounts of market data, such as price changes and economic indicators, to identify patterns that humans might overlook.

For example, AI can spot small trends in high-frequency trading, allowing for quick trades that capitalize on brief opportunities. It also creates strategies by reviewing past successful trades, helping to remove human bias.

Generative AI is also an invaluable tool used for portfolio optimization to help investors find the best mix of assets. It looks at factors like risk, return potential, and economic trends to recommend smart investments.

AI can further personalize investment strategies, tailoring portfolios to individual goals, spending habits, and risk preferences. It even considers investor psychology to predict behaviors like panic-selling, helping to avoid poor decision-making during market stress.

Additionally, AI forecasts how portfolios might perform under different market scenarios, like political crises or climate change, offering a clearer view of potential risks and returns.

Internal Efficiency and Operational Automation

While automation of many back-office tasks often requires the use of robotic process automation (RPA) systems, which are typically rule-based systems, generative AI models also play a crucial role in streamlining the internal operations of financial organizations.

Employee Support

AI-powered assistants answer internal questions, help write and research, and automate replies to common queries. This saves employees time and keeps work flowing smoothly.
Document Handling

Gen AI automatically creates, summarizes, and translates reports, policies, and meeting notes. It pulls out key points from large documents, making information easier and faster to access.

Data Processing

Generative AI extracts important details from contracts, loan forms, and financial records, reducing manual data entry and helping to prevent mistakes.

Template and Report Drafting

AI drafts templates for emails, reports, and business documents by combining information from different sources. This speeds up reporting and decision-making.

Software and IT Support

For IT teams, AI helps write and check code, automating parts of software development and accelerating the creation of internal tools.

Knowledge Management

AI organizes and updates internal knowledge bases, such as central libraries of company information. This makes it easier for employees to find what they need.

Compliance and Regulation

Generative AI reviews and summarizes complex regulations and laws, helping compliance teams stay up-to-date and meet legal requirements more efficiently.

Meeting Summaries

AI transcribes and summarizes meetings and customer calls, providing clear records and action points for easy follow-up.

Read more: AI in FinTech. How AI & Machine Learning Can Innovate FinTech Product

Generative AI Models in Real-World Banking

While generative AI is becoming more common in financial institutions, not all banks are open about the specific models they use. Below are well-known examples of gen AI in the banking sector.

OCBC Bank

OCBC GPT, developed by OCBC Bank in Singapore, is an AI system built on Microsoft’s Azure OpenAI. It is designed to support employees across a wide range of tasks like answering questions, drafting emails, and even assisting with coding tasks.

SouthState Bank

In the US, SouthState Bank has customized ChatGPT with its own data to speed up tasks like fraud detection, summarizing policies, and creating marketing content. This has cut task times from minutes to seconds, boosting productivity by 20%.

Deutsche Bank

Deutsche Bank has also introduced GenAI chatbots for both employees and customers. Its voice and text assistants help with risk analysis and make workflows more efficient.

SouthState Bank

US-based SouthState Bank also trained ChatGPT on its own internal documents to help employees work more efficiently. The staff now uses AI to draft emails, create expense reports, summarize policies, and understand complex regulations. This has cut task times from 12-15 minutes to just seconds.

JPMorgan

JPMorgan is using generative AI to create synthetic data that improves credit risk predictions. This approach allows the company to simulate a variety of borrower profiles and market conditions, like rare market crashes, without compromising customer privacy.

Here, gen AI helps strengthen credit risk models, reduce bias, and test these models in different scenarios. This results in more accurate, fair predictions that help JPMorgan manage risk and stay compliant with regulations.

BlackRock

At BlackRock, generative AI is used to provide real-time market forecasts and make adjustments to investment portfolios. By analyzing large amounts of market data, news, and economic trends, the AI predicts potential risks and trends.

This allows BlackRock to automatically recommend or make adjustments like reallocating assets or hedging against risks, helping clients minimize losses and increase returns. The generative AI system also helps portfolio managers better prepare for unexpected market shocks by simulating rare events.

European Central Bank

The European Central Bank (ECB) is also making strides with Project Gaia, which looks at how climate change could impact the banking system. By automating the collection and standardization of climate data, Gaia speeds up the process of assessing climate-related risks across banks.

The technology not only assists the bank in compliance tasks as regulations change but also sets a new standard for how AI can be used in financial risk management.

Fujitsu and Hokuhoku Financial Group

In Japan, Fujitsu and Hokuhoku Financial Group are testing generative AI to improve their internal operations. It helps by automating responses to employee questions, generating documents, and even writing code, making their back-office workflows faster and more accurate.

Citigroup

Finally, Citigroup is using generative AI to handle the growing demands of regulatory compliance. The AI helps analyze and summarize lengthy regulatory documents, like the new US capital rules, making it easier for compliance teams to interpret and apply new laws across different regions.

The generative AI in Citigroup both reduces manual work and helps improve compliance.

The Future of Generative AI in Banking and the Financial Sector

Generative AI tools are set to become even more popular in the financial and banking sector. As they continue to evolve, they are expected only to get smarter and more powerful.

Hyper-Personalized Customer Service

In the near future, banks will likely use generative AI to provide even more personalized advice, investment strategies, and products tailored to each customer’s habits and needs. Virtual assistants will become more conversational and helpful, making the digital banking experience feel much more personal.

Voice assistants are anticipated to become even more widespread and fluent in understanding everyday language, picking up on context, and handling multiple requests in one conversation.

Their emotional awareness is also likely to be enhanced, so that they will be able to detect if a customer is frustrated or stressed and quickly transfer them to a human specialist for better support.

Voice assistants may also become a preferable form of customer support in banking due to potentially stronger security. With voice recognition known as voice biometrics and extra security steps like multi-factor authentication, AI-powered assistants will be able to better protect customers’ information while delivering personal service.

Improved Content Generation

Generative AI is expected to be further developed to create more complex and detailed content, such as policies and financial reports, that can adapt to changing regulations. This will help speed up processes and improve quality in the banking industry.

Financial organizations will further leverage automated software development for internal systems, with significantly reduced errors and accelerated modernization of outdated technology.

Generative AI will also save even more time for compliance teams by reading and understanding complex regulations, generating summaries and reports automatically.

Additionally, generative AI is expected to handle even larger datasets, helping to provide companies in the banking sector with clearer risk assessments and better strategic advice. This will allow decision-makers to make quicker, more informed choices.

Finally, Generative AI is likely to be used even more in the future, especially by being integrated into everyday tools like Microsoft 365 and Salesforce. This will help automate tasks such as writing reports, drafting emails, and summarizing meetings directly within these platforms, making work more efficient and reducing the mental strain on employees.

New Banking Models and Services

With the further adoption and evolution of generative AI in banking, companies will be able to quickly roll out new products that match the changing needs of their customers.

For example, there is a potential market for AI-driven loan systems that can approve loans in just a few minutes by automatically checking credit and generating the necessary paperwork, cutting down waiting times, and providing faster service. There is also a strong need for virtual AI advisors that will be available 24/7, offering personalized investment advice to everyone and making financial guidance more accessible than ever.

Moreover, generative AI will allow banks to test and refine new products before they hit the market by simulating how customers might react and how the market will respond. This means banks can offer more customized services, like savings plans and financial strategies designed around each person’s unique goals.

Generative AI also opens up new ways for banks to generate income, such as by offering AI-powered services to other companies or catering to niche customer groups.

All of these innovations will make banking more efficient, responsive, and focused on what customers really need.

Final Thoughts

Generative AI is opening up new, exciting opportunities for businesses in the financial industry to deliver quicker, smarter, and more personalized services.

By analyzing vast amounts of data, AI is now uncovering fresh ways to generate income, creating more tailored recommendations, and supporting the fast launch of new products. It also helps institutions stay ahead of risks by enhancing the efficiency of security measures and streamlining compliance monitoring and reporting processes.

Looking ahead, generative AI will play an even bigger role in the optimization of daily operations in the financial sector, from drafting complex reports and adapting to new regulations, to modernizing internal systems and supporting faster, smarter decision-making. With the future spread of voice assistants powered by generative AI, customer interactions are expected to feel even more natural and conversational.

However, these advances come with challenges, such as ensuring data privacy, meeting strict regulations, and avoiding bias in decision-making.

Banks must also manage risks related to AI errors, cybersecurity threats, and the integration of AI into older systems. At the same time, they need to ensure AI decisions are clear and fair, gaining trust from both customers and regulators.

To make the most out of AI, financial organizations need to start by understanding their unique needs and setting clear, specific goals. It is important to test new ideas first to see what is truly feasible before diving into big investments in modern technology. At the same time, building strong guidelines for responsible AI use is key. Done right, generative AI has the power to make banking faster, more accessible, and much more customer-friendly.

References

1. https://www.elastic.co/blog/financial-services-ai-fraud-detection
2. https://news.microsoft.com/source/asia/features/ocbcs-new-generative-ai-chatbot-is-boosting-the-banks-productivity-across-departments-and-locations/
3. https://www.jpmorgan.com/technology/technology-blog/synthetic-data-for-real-insights
4. https://www.blackrock.com/aladdin/solutions/aladdin-copilot
5. https://www.bundesbank.de/en/press/press-releases/project-gaia-enables-climate-risk-analysis-using-artificial-intelligence–927886
6. https://www.fujitsu.com/global/about/resources/news/press-releases/2023/0922-01.html
7. https://www.citi.com/ventures/perspectives/pressrelease/investing-in-norm-ai.html