Shadow AI in financial services: Why banks are losing control (and how to get it back)

Shadow AI refers to all AI tools used within an organization without proper approval, evaluation, or supervision. These can be chatbots, tools for generating or reviewing code, tools for creating presentations, or summarizing documents. Usually, they are used by employees without permission, and confidential business data is shared with them.

We have seen similar situations in the past, especially with the rise of cloud technology, when employees shared various documents using cloud storage systems like Dropbox, OneDrive, and similar. Back then, it was called shadow IT.

Today, since we are talking about the most advanced technology so far, capable of processing, analyzing, and remembering huge amounts of data in a short time - this phenomenon is called Shadow AI. And yes, it is much more dangerous for organizations.

What makes shadow AI so risky?

As we mentioned earlier, different AI tools can access confidential data. When legal and security teams review and approve the use of AI, this doesn’t have to be a problem. The problem is when data is shared without authorization from internal teams who have properly assessed the AI vendor. (If you want to learn how to evaluate AI vendors, we wrote about that in our article “Spotting red flags: How to evaluate AI vendors and avoid costly mistakes”).

When it comes to banks, shadow AI is risky for several reasons:

• it touches private and financial data of bank clients
• it often makes decisions without an audit trail
• it’s hard to detect how the AI makes conclusions
• it’s unclear whether the AI follows regulations that apply to AI (for example, the EU AI Act)

As we can see, Shadow AI is a serious risk for banks, and in the next part, we’ll try to understand how and in which departments it can become a trap.

As we wrote in the previously mentioned article, banks most often use AI for the following:

1. Fraud detection and prevention – because of the amount of transactions AI can process, analyze, and understand in a short time, it is a great tool for defending against fraudsters.
2. Credit scoring and credit risk assessment – something that used to be a manual and time-consuming process has now become quick and simple with the help of AI. However, this puts AI systems used by banks in the high-risk category under the EU AI Act, which requires the highest level of compliance.
3. Risk management and support in decision-making – used to identify, quantify, and avoid risks.

Now that we understand how banks use AI to improve general business processes, next we’ll look at examples of how individual teams within banks use AI to make their work easier — and how this can lead banks into the trap of Shadow AI.

‍

‍

As has long been said in the IT world: you can have the best security in the world, but the weakest link is still the human.

Still, banks can do a lot to reduce the risks connected to using unauthorized AI tools. Let’s find out what happens if they do nothing to prevent this.

Ignoring this fact about Shadow AI and hoping it won’t happen to you is not just a technological risk – it’s a serious business risk.

Besides the possibility of being fined up to €35 million or 7% of global revenue, imagine the reputational damage if it became public that your clients’ financial or credit data was leaked. That would be a major issue, one that could even threaten the bank’s existence.

And of course, while clients are at the center of every business, it’s the regulators who decide how banks must operate. With the EU AI Act coming into force, detailed rules have been introduced on how systems should work and how they must be protected from potential risks.

Any non-compliance could lead to serious operational issues that would further harm the bank’s business and survival.

As we wrote in our article “AI risk management in banking: Why traditional frameworks fall short in 2025”, most banks still use a manual approach to assessing AI risks. This includes manually checking AI vendors without a standardized process and creating reports by hand. Is that a problem? We believe it is.

Below are some of the problems we see with traditional solutions:

• they rely on manual, case-by-case processes
• they usually take a long time
• they don’t follow standardized frameworks
• they are done only once and don’t include continuous monitoring

Because of all these reasons, it’s clear that traditional solutions are simply not enough for today’s world and its technological advances. Banks need modern solutions - ones that allow instant assessment of AI vendors, follow standardized AI risk assessment frameworks, and continuously monitor AI systems to ensure the highest level of compliance.