Generative AI in Banking - Benefits and 6 Top AI Use Cases in Finance

Do your banking operations have difficulty with manual and slow processes? 

If so, Generative AI in Banking can help manage these challenges better.

In this article, we will go over what you need to know about Generative AI in banking. Also, we will cover the top use cases of Gen AI in banking worth looking into. 

What is Generative AI in Banking

Generative AI in banking is the use of advanced artificial intelligence models. These models can create new content. They can also automate complex work inside the financial services industry.‍

For example, traditional AI might only analyze existing data to sort or predict outcomes. Generative AI in banking is different because it can produce completely new outputs. 

As a result, it can come up with human-like text, data examinations, and even complex financial plans. 

For financial leaders, the main challenge is balancing the potential of these tools with the high demands for data security and privacy for compliance. 

How Generative AI Works in Banking

A major advance of generative ai use cases in banking is its ability to copy and support high-level thinking tasks. These tasks were traditionally carried out by finance experts. The entire process can be broken down into a few main steps. It goes from understanding a difficult problem to creating a solution, and it all happens in moments.

1. Learning from Data and Understanding Context: First, the AI model is trained on a large dataset specific to the industry. This dataset can be made up of a bank's complete set of internal information. Generative ai use cases in banking includes procedural documents, compliance manuals, product details, and past client interactions.
2. Understanding Difficult Questions: Next, a user interacts with the system. This user could be a customer, a compliance officer, or an investment analyst. When this happens, the AI uses advanced Natural Language Processing (NLP). For instance, a search for top-line expansion will also bring up documents that talk about revenue growth or sales acceleration.
3. Combining Information and Creating New Content: After that, the AI understands the request. It then combines information from thousands of sources to create a new output. This could be a short summary of a 100-page research report. It could also be a personalized email to a client about a new mortgage product.
4. Running Automated Processes: Finally, the most advanced systems can perform actions, not just create content. An AI that can act on its own can be set up to complete multi-step tasks by itself. For example, it could handle a customer's first question. It could then check their identity, give the requested information, and log the interaction in the CRM. 

Benefits of Generative AI in Banking

For financial institutions, generative ai use cases in banking is more than just getting new technology. It is about gaining important benefits that can solve the industry's biggest problems. These advantages create a clear and strong reason for planned spending in AI.

• Improved Work Output and Lower Costs: First off, AI in banking automates many manual, repetitive, and slow tasks. This can include answering routine customer questions. It also includes creating compliance reports and carrying out first checks for investments.
• Highly Personalized Client Experiences: In addition, Generative AI in banking permits a degree of personalization that was not possible before on a large scale. It can look into a client's history and financial goals.
• Advanced Risk Management and Following Rules: In an environment with many rules, AI banking is an effective tool. It can go over all customer interactions to check for compliance with regulatory scripts. On top of that, specialized AI systems can automate and make governance, risk, and compliance (GRC) work more effective.
• Better Investment Information and Analysis: For asset management and investment banking, AI can read and combine information. It can do this from thousands of documents in just minutes. These documents are made up of broker research, regulatory filings, and expert call transcripts.
• 24/7 Smart Customer Support: In the end, automated agents and chatbots can give out instant and accurate answers. They can respond to a wide variety of customer questions around the clock. 

Market Dynamics and Investment Trends

The use of Generative AI in finance represents a major economic shift. The market is defined by a fast growth path, caused by the planned need to innovate. Consensus forecasts project a compound annual growth rate (CAGR) of about 33%.

The market is expected to grow from about $1.26 billion in 2024 to over $21.8 billion by 2034. This growth greatly outpaces typical IT spending. This points to a planned shift of funds away from older systems and toward AI-centered projects.

The Adoption Gap: Large vs. Small Banks

The use of this technology is not uniform across the industry. A clear difference in usage is appearing between larger and smaller institutions. A survey showed that 79% of banks with over $250 billion in assets have Generative AI projects either live or in development.

This is compared to only about 40% of institutions with less than $10 billion in assets. This difference creates a noteworthy competitive divide.

The large amount of capital needed to set up sophisticated models naturally favors larger companies. This situation could widen the gap among market participants and put smaller firms at a disadvantage over time.

Top 8 Use Cases of Generative AI in Banking

Generative AI in banking is not a single solution. Instead, it is a versatile technology. It can be applied to solve many specific challenges within a financial institution. Let's look at eight of the most effective use cases.

1. Using AI to Stop and Find Fraud

First, AI systems in banking can analyze transaction patterns in real time. They can also look at customer interactions and network data. This helps to find unusual patterns that might point to fraud. 

• By analyzing huge, real-time data streams, one of the generative AI use cases in banking is that it can identify subtle and complex patterns of fraudulent behavior. Traditional rule-based systems would miss these patterns.
• Mastercard, for example, used a proprietary AI model to lower false positive alerts by 200%. It also cut the time needed to detect fraud in half. This protects both the bank and its clients.

2. Credit Risk Examination with AI Support

Another key area is the credit risk review process. Generative AI in banking can improve this process. It does this by analyzing a much wider set of structured and unstructured data than older models. 

• This set of data includes not just financial statements. Generative ai use cases in banking also includes market trends, industry news, and even patterns in bank account activity.
• Bankwell, a community bank, uses an AI lending assistant. This assistant automates up to 90% of the loan application process. This includes data collection and pre-eligibility checks. This makes certain that lending decisions are based on a more complete and correct view of a borrower's financial health.

3. Custom Chatbots for Customer Service

Next, let's look at one of the clearest and most common use cases of AI in banking. Banks can use advanced conversational agents. These agents can do more than just answer simple questions.

• These AI agents can understand context. They can also handle complex, multi-part conversations. For example, they can explain the differences between mortgage products. They can also guide a customer through a loan application.
• With advanced abilities to use many languages, these chatbots can give 24/7, personalized support. This support can be for a varied and global client base. This greatly improves service quality and effectiveness.

4. More Correct Financial Predictions

In addition, AI-powered market intelligence platforms can completely change financial forecasting. By training models on historical market data and economic indicators, institutions can use Generative AI to generate early-warning alerts for new risks. 

• By using Generative AI in banking, financial analysts can instantly summarize and combine thousands of data points. This information comes from expert interviews, news, and regulatory filings. As a result, analysts can create more detailed and correct predictions.
• Generative AI use cases in banking help financial institutions to make better decisions about how to allocate assets and plan for resources.

5. Better Investment Examination and Risk Management

For investment professionals, Generative AI in banking is a game-changing development. Market intelligence platforms use industry-specific AI. They search through a special collection of financial content. This includes broker research and expert call transcripts.

• This allows financial analysts to carry out deep checks and find new deal opportunities. They can also find special information much quicker than by hand.
• Generative ai use cases in banking can also conduct sophisticated stress tests. It does this by simulating a wide range of economic conditions. These could be interest rate shocks or market downturns. This helps to model how portfolios would perform.

6. More Coordinated and Steady Customer Success Programs

By putting Generative AI banking directly into a Customer Relationship Management (CRM) platform, banks can create very effective and personalized messages for clients.

• For instance, Gen AI in banking can help group clients. It can also automatically create customized marketing emails and in-app messages with specific financial recommendations.
• This confirms that each message to a client is on time and relevant. This process notably improves customer engagement, retention, and cross-selling opportunities.

7. Employee Copilots for Knowledge Management

One of the most effective internal uses of Generative AI is as an employee copilot. An institution can train a model on its large internal knowledge base.

This base includes policy manuals and product details. This creates a conversational tool that makes information instantly available.

• For example, SouthState Bank trained a tool on its internal documents. This lets employees get instant answers to complex questions about its 400-page commercial loan policy.
• This resulted in a five- to eight-fold increase in productivity for these tasks. The time needed to find information fell from an average of 12-15 minutes to just seconds.

8. Legacy System Modernization

Many established banks face a noteworthy challenge with older software. This software is often written in outdated programming languages like COBOL. Generative AI use cases in banking present a new solution to this problem.

• AI models can be trained to be fluent in almost any programming language. Because of this, they can automatically translate older code into modern, maintainable languages like Python or Java.
• This ability can greatly speed up modernization work. It helps lower technology costs and technical debt that often slow down innovation in established banks.

Dealing with Risks and Ethical Challenges

The potential of generative AI use cases in banking is huge. However, the path to using it is filled with noteworthy risks. These risks cover technology, ethics, and operations.

A poorly managed rollout can lead to regulatory penalties, financial losses, and serious reputational damage. A proactive way to manage risk is a prerequisite for success.

• Data Security and Privacy: Sending sensitive customer data to third-party AI models creates a noteworthy risk. This data includes personally identifiable information (PII). This can lead to data leakage and privacy breaches. Institutions must set up strong data governance and consider self-hosted models for high-risk uses.
• The Black Box Problem: This refers to a set of ethical and performance risks. It includes algorithmic bias, where AI learns and scales past discriminatory practices from historical data. It also includes AI hallucinations. This is when the model produces confident-sounding but factually incorrect outputs.
• Lack of Explainability: The complex nature of advanced AI models makes it extremely difficult to understand why a particular conclusion was reached. This lack of clarity is a major barrier to regulatory compliance. It is especially true when explaining a negative decision like a loan denial.
• The Human Capital Bottleneck: The single biggest challenge is often people, not technology. Over 90% of financial services firms report they do not have employees with the needed skills to use AI successfully at a large scale. An estimated 34% of the banking workforce will need noteworthy retraining in the next three years.

How to Start Using Generative AI in Your Bank

In the market today, there are specialized platforms made for specific needs. The correct choice is based on your main goal. This could be compliance, research, customer experience, or internal automation. Below are five leading generative AI solutions for banking and finance.

1. Thunai AI

Thunai AI is a flexible and adaptable platform for coordinating AI agents that can act on their own.

This generative AI banking tool is very good at changing a bank's separate internal information into a group of custom, automated AI agents. 

This makes Thunai a good choice for IT and innovation teams that want to build custom internal tools. For example, these tools could be a compliance chatbot trained on internal policies or an HR assistant for new employee setup.

Also, its clear, tiered pricing and strong support for many languages make it an affordable way to automate support and internal work processes.

• Centralized Information Source: First, Thunai's Brain feature takes in many of a bank's internal documents and data. It then uses them to create a centralized information hub. This gives internal teams the ability to get instant, correct answers to complex questions about policy and procedures. This promotes consistency and makes searches faster.
• AI Voice, Email, and Chat Agents in Many Languages: In addition, Voice and Chat agents with advanced support for many languages can be used. These can be set up to automate Level 1 customer support questions.
• Affordable AI Customer Support: Finally, this is an economical method for community banks and credit unions. It helps them to handle common questions. It also helps to effectively serve varied customer groups.

2. Quantivate

Next, Quantivate is a dependable system for governance, risk, and compliance (GRC). It is made specifically to deal with the special rule-based challenges of banks and credit unions. To be clear, it is not a general AI. 

This generative AI in banking has a special system for managing risk from outside suppliers, getting ready for regulatory audits, and centralizing internal controls. 

For the Chief Risk and Compliance Officer, Quantivate supplies a system that is organized, checkable, and supportable. It is designed to satisfy the high demands of regulators like the FFIEC and FDIC. This makes it a necessary part of a bank's plan to lessen risk.

• Vendor Management Ready for Regulation: This module supplies an organized and checkable process for the entire third-party supplier lifecycle. This is necessary to help institutions satisfy the high demands for risk management.
• Centralized GRC & Audit Readiness: In addition, its specialized GRC modules centralize compliance processes. They also make preparations for regulatory audits more effective. This creates one correct source of information. This results in large gains in productivity. It also lowers the chance of getting expensive penalties from regulators.
• Planning for Operational Steadiness: The platform also includes a dedicated part for creating, managing, and testing out plans for recovery after a disaster.
  

3. Google Customer Engagement Suite

When it comes to a big change in the customer experience, Google's platform has a high level of ability to grow. It also has complexity for businesses. It is an end-to-end Contact Center as a Service (CCaaS) solution. This solution works smoothly on all channels.

Its most important feature for banking is Quality AI. This feature automatically analyzes all customer interactions. It checks them for compliance and quality.

While the pricing is complex and can be expensive, this generative AI tool in banking can be ideal for updating a contact center that deals with a high volume of calls.

• Complete Compliance Checks: This feature automatically analyzes all customer interactions against set business and rule-based standards. This makes it a new tool for complete monitoring of compliance.
• Help for Agents in Real Time: Agent Assist shows information and creates summaries. It also gives out instant smart replies during customer calls. In wealth management, for instance, this gives an advisor immediate client portfolio information. This then saves time and improves service quality.
• Customer Service Automation That Can Grow: The platform's advanced conversational agents can be used to handle the large number of routine customer questions. This then lets human agents work on more complex, relationship-building interactions.

4. AlphaSense

AlphaSense is the leading market information platform. It was made by and for finance experts. Its main value is found in its large, special collection of content. This collection includes private broker research and expert call transcripts that general AI tools cannot access. 

This is combined with its industry-specific AI, which is trained to understand financial language. Because of this, it gives an unmatched advantage in information for investment analysts, portfolio managers, and corporate planners.

While it is expensive, this generative AI for banking has a return on investment that makes research teams much more productive.

• Special Financial Content Collection: The AI platform includes aftermarket broker research. It also has a special library of expert call transcripts. This gives a large information advantage to investment professionals. This is especially true for those trying to find information that the rest of the market has not seen.
• Financial Search with AI: Its AI search engine uses Smart Synonyms. This helps it understand financial ideas, not only words. This greatly speeds up the process of careful checking for investment analysts. As a result, they can complete work in minutes that would take days.
• Summaries from Generative AI: This allows high-value professionals to understand the main points in seconds. This makes them much more productive and effective.

5. HubSpot AI

For client-facing teams working on marketing and sales, HubSpot AI presents the smoothest way to add AI into daily work. Its strength is the deep, built-in connection of AI features into its market-leading CRM platform. This makes a single system for client data and communication. 

This is ideal for wealth management, insurance, and retail banking teams. This is especially true for teams looking to make the client's experience personal. 

This AI in banking is also for teams that want to automate lead development and grow their content marketing work with tools that are simple and easy to use.

• Connected CRM and AI Marketing: AI abilities are connected. This allows for the creation of personalized marketing campaigns and automated client communication. For example, a wealth management firm can use this to send customized messages to clients. These messages can be set off by specific financial milestones or life events.
• Predicting Lead Value: The platform uses AI to predict the value of leads. This helps to find people who are valuable leads. This allows an investment firm or insurance agency to automatically enroll promising leads into a series of communications. This series then helps them through the sales process.

Creating Large Amounts of Content: Its Breeze Content Agent can be used to efficiently create a large library of financial learning content. This includes blog posts and informational articles. This helps a community bank or credit union grow its content marketing. This helps it show that it is a dependable resource for the community.

Comparison Table: Generative AI Solutions for Banking

To make the choosing process easier, here is a breakdown of the top Generative AI in banking. Note that pricing can change and is different for each plan, so this is a general reference.

Change Your Banking Experience with Generative AI

Thunai’s agents can be trained on your specific compliance documents, product details, and client data.

As a result, they can deal with internal support questions, automate customer service in over 80 languages, check up on leads, and make compliance work processes more effective around the clock. 

Specifically, Thunai's agent-building platform lets you start out with low-risk internal projects. This means you can grow your AI projects at your own speed. 

Are you ready to see how a custom AI agent can change your bank's productivity and client service?

You can try Thunai for free and build your first test model in minutes.

FAQs for Generative AI in Banking

What are the biggest risks when using Generative AI in banking?

The main risks include data security and privacy breaches from sending sensitive customer information to external AI models. Other major risks involve the black box problem. This includes algorithmic bias learned from historical data. It also includes factually incorrect outputs known as hallucinations. A lack of explainability also complicates regulatory compliance.

Will Generative AI replace jobs in finance?

There is widespread concern about job displacement. However, the common view is that generative ai in banking will assist human expertise rather than replace it. The technology is expected to handle routine, data-heavy tasks. This will let professionals work on higher-value activities like making planned judgments and managing client relationships. An analysis by Accenture found that 39% of banking tasks have a high potential for automation. A nearly equal 34% have a high potential for assistance.

How fast is the Generative AI market in banking growing?

The market is experiencing very fast growth. Multiple research firms project a compound annual growth rate (CAGR) between 33% and 39.1% over the next decade for generative AI in banking. For instance, Precedence Research forecasts the market will grow from about $1.26 billion in 2024 to over $21.8 billion by 2034.

Why is there a usage gap between large and small banks?

A usage gap exists because the large capital investment needed to develop and set up sophisticated AI and Large Language Models (LLMs) naturally favors larger companies. A recent survey showed 79% of banks with over $250 billion in assets have active AI projects. This compares to only about 40% of banks with less than $10 billion. This difference creates a competitive divide that could put smaller firms at a disadvantage.

How is generative AI used in investment banking?

Generative ai in banking helps investment bankers work faster by automating routine tasks like creating financial reports and summarizing long documents. It also analyzes market news and trends to help bankers provide smarter advice and make better investment decisions. 

How is AI used in the banking industry?

In the banking industry, AI is used to improve security by detecting fraud and unusual activity in real-time. Generative AI in banking powers chatbots that provide 24/7 customer support and helps banks make faster, more accurate loan decisions.

How is Goldman Sachs using generative AI?

Goldman Sachs provides its employees with an internal AI assistant to help with daily tasks like summarizing documents and drafting emails. The firm also uses generative AI tools to help its software developers write code more efficiently. Their main goal is to increase productivity and allow their staff to focus on more complex, client-facing work.