Generative AI in Finance: Opportunities, Risks & Use Cases

The global market for Generative AI (GenAI) in the finance sector is predicted to grow by $16.2 billion during 2024-2030, with more financial institutions becoming wise to the competitive advantage this new technology presents.

However, with legacy architecture, compliance concerns, and an uncertain future, even traditional AI is still far from being globally adopted. Generative AI may face even greater scrutiny as the production of completely novel inputs presents a host of new challenges, further complicating adoption.

On the other hand, there is no denying that GenAI is likely to be the jumping-off point for many new, innovative solutions.

Therefore, I want to take a closer look at GenAI and discuss some of the realistic opportunities, risks and real-world use cases this new technology presents to financial institutions.

Advantages of GenAI over traditional AI models

Currently, traditional AI models can be strategically used to enhance and streamline key banking processes. This is mostly seen in the way of intelligent process automation. For example, using software to automate your entire onboarding, CLM, and loan origination processes. 

Example: Take a look at Atfinity’s AI-powered rule engine to see how it handles all of the above.

Traditional AI models can also be used in other verticals, such as fraud prevention, market analysis, stress tests, marketing and sales pipelines. But the key fact is that these models only uses existing data.

Generative AI opens up completely new opportunities, as these models can create novel data, self-correct, and operate autonomously. This leads to a few key benefits:

• Less staff training is required
• Less manual work is required
• Less training data is required
• Services can be tailored to the individual customer
• GenAI models can be used to better other AI models

Key applications of Generative AI in finance

In theory, every banking process that requires some kind of input can be performed by GenAI. Because of this, we can already see generative models making their way into the front, middle and back office of some financial institutions.

Automated reporting

At the time of writing, Large Language Models (LLMs) such as ChatGPT get over 180 million daily users. Therefore, utilising similar GenAI models to automate reporting and internal communications across the front, middle and back offices was a natural step forward.

Generative AI can be used to create SARs, document fringe cases or escalations, justify certain risk ratings or even detail complex needs of UHNWIs. For example, a compliance office could just write in the main keywords regarding a given high-risk client; such as “off-shore account, RCA, recent large transaction” and the GenAI would scan the user account and these notes in order to create a full report.

This way the compliance officer in question would have to put in a lot less manual effort. This can then be taken one step further to also bolster internal communication, by automatically translating the generated report, pinging key persons about important details, creating a database of clients with similar circumstances, and so on.

Intelligent chatbots and virtual assistants

Another direct and popular application of GenAI is via internal and external chatbots and virtual assistants. As more customers move their banking activities to the digital space, these bots have shown to be incredibly useful and efficient.

Example: In 2025, NatWest reported a 150% increase in customer satisfaction due to the use of the GenAI chatbot Cora. 

In this instance, AI models were used to handle fraud reporting, making it so that endangered accounts can be secured a lot more quickly and efficiently. Furthermore, similar bots were used to help users better understand their finances without having to rely on an advisor.

These bots can also be used for internal communication, such as staff training, decision making, for example when making a judgment for fringe cases, or to just inform customers of important information, such as the working hours or available branch locations. 

Advanced financial modelling and risk assessment

A very important benefit of Generative AI is that it can be used to train other AI models. This is often referred to as a Generative Adversarial Network (GAN).

In finance, GANs are especially potent for financial modeling and risk management. Namely, one GenAI model can be used to strictly produce synthetic data, such as novel market conditions, fake documentation, or token customer accounts. 

Then the second AI model works on distinguishing whether the data is fraudulent or not. With this methodology, both AI models get significantly better over time, as they “bounce off” each other.

The end result: The generative AI model gets really good at creating synthetic data that can then be used for financial modelling, stress tests and forecasting while remaining compliant with data privacy regulations. And the detector model gets better at fraud detection and prevention, further amplifying AML/CFT efforts.

Risks and challenges for Generative AI in finance

For the most part, Generative AI models face similar challenges as traditional AI:

• Data privacy
• Explainability
• Oversight
• Regulations

There is also the question of public perception. While most people likely wouldn’t mind if an AI model handled risk modelling or similar backend processes, they might take issue with more personalised services. For example, if you learn that your private wealth advisor is actually an AI model, you might lose trust in their judgment or feel otherwise deceived.

Additionally, there are also the technical challenges of implementing GenAI in terms of hosting the software securely. However, both public perception and technical challenges vary greatly and are hard to accurately gauge. Therefore, I will only focus on the four challenges listed above.

Data privacy

While Generative AI models such as robo-advisors offer significant innovation in finance, they also raise serious concerns around data privacy and security. One of the primary fears among users is the potential for sensitive financial information to be accessed or leaked by malicious actors. Similarly, there's growing anxiety that personal data might be used without consent to train future AI models.

Example: in a study by the Financial Conduct Authority (FCA) in 2024, it was found that 4 out of the 5 most commonly perceived risks regarding the use of AI are connected to data; data privacy and protection, data quality, data security, and data bias and representativeness.

That said, the risk of personal data being directly repurposed for model training is being actively mitigated. Advancements in synthetic data generation and techniques like GANs are enabling institutions to simulate realistic datasets without relying on identifiable customer records. This significantly reduces the likelihood of exposing sensitive information during model development.

Moreover, most jurisdictions now enforce stringent regulations, such as the EU’s AI Act or the UK’s Data Protection Act, which place clear limits on how customer data can be collected, processed, and stored, especially in AI applications. Financial institutions are therefore required to implement robust governance frameworks and privacy-preserving techniques to remain compliant.

As a result, while data misuse remains a legitimate concern, it is increasingly being addressed through both policy enforcement and technical innovation, making it unlikely that customer data would be knowingly or recklessly put at risk.

Explainability

A major point of contention for both traditional and generative AI models is that it’s difficult or impossible to gain insight into how they make any given decision. 

This in turn means that if a given model, for example, offers harmful or bad advice or otherwise makes an error, it’s very difficult to understand how it got there and to correct the root cause. Furthermore, in order to stay compliant, banking processes need to be auditable, which may prove difficult for opaque AI models.

For this reason, more transparent models are often preferred when it comes to decision-making processes. These models are commonly labelled as Explainable AI or XAI.

For example, Atfinity uses an AI-powered rule engine to automate key banking processes. And while it makes hundreds of decisions with each step, we can always break things down into understandable, readable rules that guide the decision-making logic.

It’s possible that larger generative models will have to go down a similar route in order to be viable in the finance space, especially when interacting with customers or creating important data.

Oversight

Similarly to the previous point, financial institutions also need a way to catch GenAI models in their mistakes before it turns into a larger issue. For example, if their robo-advisors start offering potentially harmful financial advice. Or if their chatbots are no longer showing the correct information.

While these kinds of issues can be caught later on via a QA team or customer tickets, the damage is already done. For this reason, some argue that a human-in-the-loop design is essential. Alternatively, some have suggested using self-correcting AI models or specialised monitoring tools. 

Regulations

As with all emerging technologies, regulations regarding their safe and ethical use can drastically change their trajectory. This is especially important in finance as it’s an already heavily regulated industry.

Example: if a bank spans five jurisdictions, regulations concerning AI in any one of them can force the bank to drastically change their key workflows in order to stay compliant.

For this reason, many emphasise the importance of agile, no-code tech stacks that can adapt to larger market shifts and take advantage of best-of-breed APIs easily.

The future of Generative AI in finance

With generative AI making leaps and bounds at unprecedented rates, it’s difficult to accurately predict how these technologies will develop in the near future. I think it’s safe to assume that its current capabilities will be greatly enhanced with more computational power, clearer regulations, and more widespread adoption.

For example, we may see fully tailored robo-advisors that can completely adapt to the customer, reacting in real time to even minor changes, such as their mood or latest hobby. Similarly, we may see more powerful decision-making models; for example, models that can create entire investment portfolios based on a few criteria from the user.

Lastly, we are likely to see entire synthetic mini-economies, allowing financial institutions to run stress tests on a much larger scale and account for even the most novel of circumstances.

Conclusion

I would say that the future of Generative AI in finance is very bright and very exciting. This new technology has opened up completely new opportunities for many financial institutions and it can completely change the way we handle compliance and risk management.

With that being said, there are still hurdles in the way. And until both global regulators and the general public settle on how far GenAI is allowed to go, there will always be some uncertainty.

But if you want to gain the advantages of AI without having to worry about regulatory issues, explainable AI models such as Atfinity are your best choice. Book a demo to see how our AI-powered software can help your business stay relevant in the modern landscape.