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AI and generative AI use cases in banking: 6 real-world examples

Mary Ellen Biery
May 8, 2024
Read Time: 0 min

How banks and credit unions use genAI today

Learn how generative AI differs from other forms of AI and see the ways financial institutions are using genAI today.  

You might also like this webinar, "The check's in the mail: Understanding and preventing check fraud."


Harnessing benefits

Generative AI use cases are increasing

The excitement kicked up by generative AI, or genAI, has some banks exploring its uses. Credit unions are jumping in too. Others are steering clear until the dust settles. Nevertheless, understanding the technology is crucial. 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.

This piece explains:

  • Generative AI and how it differs from AI in general
  • What’s behind the genAI hype and the concerns
  • Real-world use cases of generative AI at financial institutions and
  • Available resources for financial institutions to learn more about generative AI in banking.

Banks and credit unions want to serve their clients better and improve their services and products. They also want to simplify or eliminate mundane, repetitive tasks. Generative AI is expected to be able to help in these areas. Yet 30% of financial services leaders ban the use of generative AI tools within their companies, according to a recent survey by American Banker publisher Arizent.

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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.

Background for genAI use cases

Defining generative AI for banking

Generative AI is a class of artificial intelligence (AI) models that can create new content—text, images, audio, or video—from existing data. It’s showing up in music and entertainment, education, healthcare, and marketing.

A common example of a generative AI-driven tool that many in the financial services industry are familiar with is ChatGPT, which can produce coherent and diverse texts on various topics.

A more precise definition of generative AI is included in the Biden Administration’s recent Executive Order, which defines generative AI as “the class of AI models that emulate the structure and characteristics of input data in order to generate derived synthetic content. This can include images, videos, audio, text, and other digital content.”

Many financial institutions have been using artificial intelligence (AI) for years, particularly in supporting cybersecurity and anti-fraud efforts. But Boston 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.

Examples of AI use cases in banking 

Predictive AI, which can use machine learning techniques, addresses various prediction and classification challenges such as risk monitoring, optimal pricing, and product propensity modeling, BCG says. It is comparable to the left side of the human brain, which is wired specifically for logic, measurement, and calculation. “This left brain comprises algorithms that assign probabilities, categorize outcomes, and support decisions,” the firm says. Generative AI, on the other hand, “acts as the right brain, wired to excel at creativity, expression and a holistic perspective —the sorts of skills required to generate plausibly human-sounding responses in an automated chat.”Boston Consulting Group graphic on artificial intelligence and generative AI from a piece on genAI use cases

An example of a use case for predictive AI is Signature Bank of Georgia’s addition of AI-driven check fraud detection software that finds fraud faster. The software evaluates over 20 unique features of each check coming in to provide financial institutions with a risk score indicating the probability of a fraudulent check.

Another example of using predictive AI is small business lending software that incorporates AI-driven lending intelligence. Abrigo Small Business Lending Intelligence powered by Charm provides loan rating risk scores, the probability of default, and how the score was calculated. The engine leverages self-learning AI to continuously monitor a wide range of current and historical data, loan performance, accounting, and macroeconomic data from more than 1,200 institutions.

Predictive AI use continues to expand in financial institutions. But in recent years, generative AI has seen much fanfare – and investment.

GenAI impact and risks

The hype and concern around generative AI use in banking

One reason banking professionals have heard so much enthusiasm around using generative AI is its potential financial impact on the industry.

In “Capturing the full value of generative AI in banking,” McKinsey estimates that genAI could add the equivalent of between $200 billion and $340 billion in value annually across the banking industry. That’s equivalent to 9% to 15% of operating profits, the report notes. The greatest absolute gains forecast (largely from increased productivity) are tied to corporate and retail banking.

Understandably cautious

However, as noted above, not all financial institutions are jumping into genAI with both feet. The American Banker/Arizent survey found that nearly three-quarters (72%) of community banks (those below $10 billion in assets) and 54% of credit unions reported they either have no plans to use generative AI or are still learning and collecting information on the technology. That compares to 39% of global or national banks with more than $10 billion in assets.

Financial institutions have several reasons they may be reluctant to embrace generative AI. Here are some of the factors leading to caution, as well as additional measures to consider:

  1. Institutions take seriously their unique relationships with clients and the trust involved. They know consumers must be comfortable with the technology’s use. In addition, the institution must ensure adequate risk control to reassure them that client assets are protected.
  2. Another reason for reluctance about generative AI is the highly regulated nature of banking. For example, banks and credit unions must comply with strict data privacy laws and requirements for transparent AI. They must also ensure that generative AI solutions or models do not produce biased outcomes.
  3. Finally, many financial institutions have limited resources for technology, which could damp enthusiasm for generative AI. Many banks and credit unions are prioritizing investments for digital transformation. Financial institution leaders can leverage the AI capabilities of their existing tools even as they identify the budget, talent, and infrastructure to invest in additional generative AI solutions.

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.

Large language models

GenAI uses cases among banks and credit unions

Here’s a quick look at some generative AI use cases being employed at banks and credit unions:

GenAI use case for understanding financial institution data

Southwestern National Bank used to spend hours gathering data and working in spreadsheets to create a geographic concentration report for the OCC examiners. Using Abrigo Connect, a business intelligence solution, the bank can use natural language when searching for data to help with regulatory examinations, board reporting, or weekly management and risk reporting. Now, Southwest National uses Connect to generate a report in seconds to show examiners the loan concentrations across its markets. The same solution can help Southwestern examine efficiency within operations and improve credit and portfolio risk monitoring.

GenAI use case for resolving financial institution member or customer needs faster

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 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.

GenAI use case for fostering relationship banking

Abrigo client BAC Community Bank in Stockton, Calif. ($800m deposits) uses an app that answers customer questions and matches them with a BAC banker as their assigned contact.

GenAI use case for sniffing out fraud in emails and instructions

JPMorgan is reportedly using large language models to fight fraud and other attacks embedded in email and other financial communication. Its technology can detect signals for fraudulent emails or fraudulent instructions for a wire. MasterCard is reportedly trying to better protect from fraud its cardholders and the financial institutions using its network with its proprietary generative artificial intelligence model. The genAI model uses the 125 billion transactions on MasterCard’s network each year to identify fraudulent patterns so financial institutions identify more fraud while spending less time assessing specific transactions.

GenAI use case for training employees and making them more productive

SouthState Bank, another Abrigo client, trains its enterprise version of ChatGPT only on bank documents and data. No customer data is fed into the system and it's not available to anyone outside the bank, which has $45 billion of assets. Employees are asking the system questions about the bank's 400-page commercial loan policy and 600-page branch policies and procedures. A new teller who needs to reissue an ATM card can ask the system how to do it; employees summarize regulatory documents or sets of policies; marketers create copy and bankers compose emails with it. "In our couple months of rolling it out, we get a five to eight X boost in productivity just by saving time," Nichols told American Banker. "It normally takes an employee 12 to 15 minutes to figure out the correct answer. That gets reduced to seconds."

GenAI use case for resolving bank transaction/fraud disputes

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. The goal is consistency and transparency in resolving transaction disputes and improving retention by resolving employee frustrations.

See how Southwestern National Bank's Chief Credit Officer can quickly access past dues, upcoming maturities, and a report showing geographic loan concentrations.

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Learn about banking generative AI

Resources on genAI in banking

Bankers are under immense regulatory pressure, so learning more about genAI and how regulators view it can help in any plans to use it. Here are several resources on generative AI and regulators’ views:


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.

About the Author

Mary Ellen Biery

Senior Strategist & Content Manager
Mary Ellen Biery is Senior Strategist & Content Manager at Abrigo, where she works with advisors and other experts to develop whitepapers, original research, and other resources that help financial institutions drive growth and manage risk. A former equities reporter for Dow Jones Newswires whose work has been published in

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About Abrigo

Abrigo enables U.S. financial institutions to support their communities through technology that fights financial crime, grows loans and deposits, and optimizes risk. Abrigo's platform centralizes the institution's data, creates a digital user experience, ensures compliance, and delivers efficiency for scale and profitable growth.

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