Top 5 Generative AI Use Cases in Fintech in 2024

From enhancing security measures with advanced fraud detection to personalizing the banking experience and enabling AI stock picking, generative AI in fintech is leading the way in this transformation.

GenAI can process and analyze huge amounts of data, automate tasks, and make predictions, which makes it an indispensable tool for numerous financial applications.

The collaboration between genAI and fintech enables financial institutions to make more informed decisions, better manage risks, and provide their clients with customized services.

Key Takeaways

  • GenAI has the potential to revolutionize various aspects of the financial industry by improving efficiency, accuracy, and customer experience.
  • Among the top generative AI use cases in fintech, experts have named fraud detection, transformation from digital into intelligent organization, augmenting human capabilities through automation, accelerating regulatory compliance, and mitigating economic crimes.
  • It’s important for fintechs and financial institutions to understand the dangers of genAI and the risks of artificial intelligence in financial services, including phishing, social engineering, and generating fraudulent data.

Top Five Generative AI Use Cases in Financial Services

Top 5 Generative AI Use Cases in Fintech

Enables Fraud Detection and Prevention

One of the top use cases for genAI in fintech is to detect and prevent fraud.

Generative AI can analyze massive amounts of transaction data in real-time, detecting any unusual activity that may indicate fraud.

Through the use of machine learning algorithms, these systems can continuously improve their ability to detect fraud by learning from past data.

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This not only helps to quickly identify suspicious activity, but it also reduces the number of false positives, ultimately improving overall transaction security and building trust with customers.

Changes How Financial Institutions Make Micro and Macro Decisions

Considering that digital transformation is focused on customer experience and operational processes, the use of AI will be pervasive, said Richard Berkley, head of data, analytics, and AI in financial services at PA Consulting.

He told Techopedia:

“It will fundamentally change how financial institutions make both micro and macro decisions, including in relation to investment strategy, staff enablement, risk management, and other decisions.”

Boards are realizing that they must lead the charge to transform from a digital organization to an intelligent organization to stay relevant and profitable in the modern world, according to Berkley.

“These organizations have been putting in place AI guardrails over the past year, and they are now starting to build generative AI muscle for 2024, establishing enterprise-scale AI platforms, and preparing their organizations for safe adoption,” he added.

Transforms How Financial Institutions Operate

“In financial services, AI will change where and how we invest as markets are transformed, what clients expect of institutions in terms of AI-driven innovation and agility, how firms govern suppliers as they adopt AI, and external reporting as they provide transparency on the use of AI,” Berkley said.

Many financial institutions would do well to use AI to augment human capability by solving user needs through enabling insight and automation, according to Berkley.

This focus on fostering a harmonious relationship can ensure AI complements rather than replaces human skills.

Accelerates Regulatory Compliance

For example, financial services clients are deploying genAI solutions that identify where changes in regulations will impact them in terms of policies, processes and responsibilities, and generating appropriate alerts, according to Berkley.

“They are also using generative AI to compare the regulatory reporting across multiple jurisdictions with the appropriate regulations to assist in ensuring completeness,” he added.

“Generative AI is being used to help the first line in the business understand obligations and policies, building on the knowledge store of questions that have previously gone to second line of defense.”

GenAI can help provide a consolidated view of what’s happening in the regulatory environment, including such areas as horizon scanning, regulatory engagement, policies, procedures, and associated change initiatives, according to Berkley.

“This can be used to inform change and even simplify compliance-driven controls,” he said. “We see genAI increasingly being used to streamline processes, such as the creation of risk reports and surface alerts in areas like complaints, consumer duty, and assuring other process improvements.”

Addresses Economic Crime

Some solutions are starting to employ genAI to address economic crime, Berkley said.

This includes integrating AI and machine learning into transaction monitoring systems, behavioral analysis to identify suspicious activity, and implementing biometric authentication in identity verification to tackle identity theft. Berkley added:

“Nonetheless, solutions such as these necessitate careful consideration of ethical values, such as data privacy management, algorithmic inherent biases, and the assurance of reliable outcomes from genAI systems, as well as societal concerns regarding genAI’s potential to displace jobs.”

Real-World Generative AI Examples in Fintech

Here are some real-world examples of generative AI applications that highlight how the fintech industry is using the technology:

JPMorgan Chase

Last May, JPMorgan Chase filed an application for a trademark for a financial advisory tool called IndexGPT, a ChatGPT-like AI service to help customers better determine where to invest their money. 

IndexGPT will leverage cloud computing and AI to analyze and organize securities according to the specific requirements of their customers.

NatWest and IBM

NatWest and IBM are collaborating on Cora, NatWest’s virtual assistant that will use GenAI to enable its customers to access a wider variety of information via conversational interactions.

Wendy Redshaw, chief digital information officer of the NatWest Group’s retail bank said:

“Building on Cora’s success over the last five years, we’re working with companies like IBM to leverage the latest generative AI innovations that will help make Cora feel even more ‘human’ and, most importantly, a trusted, safe and reliable digital partner for our customers.”

OCBC Bank

Last fall, the OCBC bank in Singapore rolled out a GenAI chatbot to its 30,000 employees worldwide to enhance their productivity and enable them to improve customer service.

The bank deployed the chatbot in collaboration with Microsoft’s Azure OpenAI.

Square

Payment processing company Square is using generative AI capabilities to help sellers automate their operations, streamline their workflows, and save time.

For example, Square’s menu generator lets restaurants easily generate complete menus in a matter of minutes with minimal effort, providing them with a valuable time-saving tool when they use Square to expand their food and drink offerings.

Bank of America

One of the real-world generative AI use cases in banking revolves around Bank of America’s use of genAI to detect fraudulent credit card transactions.

Bank of America’s AI system analyzes billions of transactions daily to identify patterns that indicate fraud. For example, its AI system can detect transactions made for abnormally large amounts of money or that are conducted from unusual locations.

Hokuhoku Financial Group and Fujitsu

Last September, Hokuhoku Financial Group and Fujitsu began trials to explore using GenAi to improve the bank’s operations.

The trials include an AI module for conversational AI to help the bank generate and check various business documents, respond to internal inquiries, and create programs.

The Future of GenAI in Fintech

In the fintech space, the big opportunity – and where the most competition will take place – is around how banks can use their customer data alongside other sources of data in ways that can directly benefit customers, said Dom Couldwell, head of field engineering EMEA at DataStax, real-time data for AI company.

“For banks and fintech providers, this will be where they challenge each other, i.e., who can make the best experience for customers, and how can they bring that data to bear within the experience?” he said. “Already, company teams have started building chat services that can provide more personalization using each customer’s own data.”

Banks are also thinking about what comes next. The iPhone caused apps such as Instagram, Uber, and Spotify to become ubiquitous, and now the race is on to be the first ubiquitous app for genAI, according to Couldwell.

“Alongside this, I think we’re just at the start of this journey,” he said. “Putting a new technology at the heart of what you do will take time.”

The potential is there for genAI to improve the efficiency with which organizations can comply with regulatory needs by analyzing their data, collating it, and presenting it in the correct format. Couldwell said:

“This is not the sexiest use case, but there are significant benefits in improving operational efficiency of organizations over and above the more talked about use cases, such as enhancing Know Your Customer operations or fraud monitoring.”

However, there are some downsides to using generative AI in fintech. PA Consulting’s experts have seen some emerging economic crime risks within genAI, including phishing, social engineering, and generating fraudulent data, as it allows new and more sophisticated methods to carry out illicit activity, according to Berkely.

He concluded:

“It’s important for fintechs and financial institutions to understand the dangers of generative AI and the risks of artificial intelligence in financial services as misusing generative AI to commit fraud is becoming an increasing risk for these institutions and their customers.”

The Bottom Line

As these examples show, generative AI can be applied in various use cases within the fintech industry.

As such, the technology has the potential to revolutionize various aspects of the financial industry by improving efficiency, accuracy, and the customer experience.

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Linda Rosencrance
Technology journalist

Linda Rosencrance is a freelance writer and editor based in the Boston area, with expertise ranging from AI and machine learning to cybersecurity and DevOps. She has been covering IT topics since 1999 as an investigative reporter working for several newspapers in the Boston metro area. Before joining Techopedia in 2022, her articles have appeared in TechTarget, MSDynamicsworld.com, TechBeacon, IoT World Today, Computerworld, CIO magazine, and many other publications. She also writes white papers, case studies, ebooks, and blog posts for many corporate clients, interviewing key players, including CIOs, CISOs, and other C-suite execs.