The known risks of using AI in banking, and how to mitigate them

Artificial intelligence (AI) is constantly evolving and rapidly being adopted in finance functions. As a founder, you might feel caught between wanting to adopt AI tools that will help your team move fast, while also wanting to avoid costly mistakes. That’s understandable: There’s real risk when a startup relies on AI tools to automate financial transactions. And trust and security are non-negotiable, especially when money is involved. 

Take this situation as an example: A finance team starts using AI to manage their accounting workflow and automate payments. Within a month, they notice that productivity is up. But one day, the AI system follows a predefined rule and approves a batch of payments. However, one vendor’s email account had been compromised in a data breach, and using that email an attacker sent your company updated banking details. Unfortunately, the AI tool didn’t issue a fraud alert because everything looked “normal” according to the rule. No one on the finance team reviewed the auto-approved payments to catch this mistake, so the payment got routed to a fraudulent account. When teams use AI without proper oversight or judgment, mistakes like this can happen. 

Here, we’ll explore the risks of AI in banking, the advantages and disadvantages of AI in banking, and how to mitigate them in your startup’s daily operations.

Startups are increasingly adopting AI for financial operations. AI can automate repetitive work, streamline internal workflows, and improve how companies interact with customers and vendors — without requiring additional headcount.

The business impact is most visible in day-to-day execution. Tasks that once took hours can be completed in minutes, with fewer manual errors and lower operational costs. And AI can surface patterns in financial data that are difficult to catch manually, giving founders clearer visibility into performance and more reliable inputs for forecasting. This combination of speed, cost efficiency, and  insight can help startups make better decisions and operate with greater precision as they grow.

But these gains come with trade-offs. The same systems that increase efficiency can also introduce new risks, if they’re not properly understood or managed.

Here are a few of the inherent risks worth understanding when it comes to implementing AI in your finance function.

1. Data accuracy and hallucination 

When financial operators rely on AI for financial insights, they run the risk of generating inaccurate forecasts or summaries and having transactions misclassified under the wrong categories. AI tools are prone to “hallucinating” (producing fictitious answers supported by fabricated sources) and often sound confident, even when the work they’ve produced is inaccurate.

2. Lack of explainability (aka black box decisions)

You may decide to leverage AI to help you detect fraud or reject credit applicants. However, you must ensure the AI model you’re using is transparent and explains its actions. Some AI models aren’t able to detail how they make decisions. Therefore, relying solely on AI can be problematic for audits, trust-building, and compliance.

3. Data privacy and security risks

Data breaches are one of the top risks for fintech startups. So, managing your company’s financial data responsibly is imperative. When you involve third-party AI systems, there’s a risk that sensitive information could be leaked if it’s not secured properly.

4. Over-automation and loss of human oversight

Trusting the outputs of an automated tool without oversight can pose significant AI risks in banking and finance. For instance, if you make critical decisions without a thorough review, you may not notice if the AI model is providing outdated information or making false assumptions.

5. Integration and system fragmentation

If you don’t have an organized financial system, layering AI on top of it could create inconsistencies, instead of clarity. For example, if you have accounting software and financial reports that aren’t aligned, adding AI tools onto disconnected systems will cause more confusion.

6. Regulatory and compliance uncertainty

Regulators pay close attention to the financial operations of startups. Since AI models may not be up to date with regulatory requirements or reporting standards, financial leaders need to ensure these systems remain compliant.

When mistakes occur due to AI, they can cause significant damage to a company. Finance operators must be aware of the potential ramifications before exploring AI tools.

Here are some of the potential consequences of AI-related mistakes:

• Financial misstatements: Errors — such as incorrect financial projections, duplicate payments to a vendor, or missing assets and liabilities — could directly impact your cash flow.
• Poor strategic decisions based on bad data: If you have inaccurate data, it could have a ripple effect across your startup, since decisions will be made based on the wrong information.
• Compliance issues or audit failures: If your startup isn’t adhering to compliance and regulatory rules, you may face monetary penalties and fines. Regulators may enforce operational restrictions or bans.
• Loss of trust, both internally and externally: Your colleagues, customers, and investors may lose confidence in your company once your reputation takes a hit. Investors could see the mistake as a sign of having poor controls.

Here are some practical ways to reduce the risks involved with using AI in your startup.

1. Keep humans in the loop for critical workflows

Over-relying on AI can cause ethical, legal, and regulatory issues. The ground rule that founders should follow is to have AI assist with tasks, such as reporting, handling payments, and forecasting. However, always require the finance team to review and approve the AI model's output to ensure accuracy.

2. Start with low-risk, high-volume use cases

Don’t start immediately with decision-making algorithms, since they are high risk and more complex. Instead, ease into using AI by starting with low-risk tasks and gradually working your way up. For instance, tasks like categorization, data extraction, internal insights, and summarization are generally more straightforward to manage with AI.

3. Build on a clean, centralized financial system

If you’re not starting with a solid financial foundation, you’ll end up with the problem of “garbage in, garbage out.” If, for instance, you have fragmented systems, inconsistent data, or outdated reports, AI will only make matters worse. Consider streamlining your financial system to make it a single source of truth. Once you have cleaned up your financial system, you can consider adding AI to generate clean outputs. 

4. Prioritize transparency and auditability

Use AI tools that can precisely demonstrate how outputs are generated. Prepare clear and transparent logs. In the event that auditors or regulators ask you for proof, you should be able to confidently explain how every decision was made. 

5. Be intentional about data security

Implement rigid data controls when selecting a third-party vendor. Practice due diligence and understand where data is processed and stored. Consider using enterprise AI software and ensure that data is encrypted. To prevent data leaks, don’t enter sensitive financial information into public AI tools.

6. Layer AI, don’t replace (at first)

You may be tempted to fully automate your financial workflows from the get-go. However, it’s better to take small steps and start by augmenting your existing systems. Use AI to support your low-risk tasks to gradually build trust and confidence. This approach helps protect established controls and prevent major failures. 

This evolving technology must be used responsibly. Here’s a quick overview to help financial leaders determine how risky certain activities are.

AI is advancing at a fast pace. In the next few years, you’ll likely see improvements in model reliability and explainability as tools become better at making decisions. Additionally, more purpose-built financial AI systems will likely come into the spotlight, with automations that help businesses better manage risk. You can also expect tighter integration between banking and software, with agentic AI managing more-complex tasks and providing greater personalization for customers. Most likely, AI tools will gradually get embedded in compliance and regulatory frameworks to manage security risks. 

Overall, if you’re a startup founder or finance leader who’s establishing or evaluating innovative AI tools for your company, it’s imperative to build safe and trustworthy systems, to help your company  succeed in a competitive environment.

AI has the potential to transform business operations. However, to adopt it seamlessly, you’ll need human oversight and guardrails. This will ensure that the tech is well-controlled and that the proper infrastructure is in place to support your team.

Startups have to adapt quickly, but that doesn’t mean they should be reckless. Founders can find the right balance between moving fast while maintaining control. Doing so will help your business stay compliant and maintain accuracy. 

With a modern business banking platform, built-in controls, real-time insights into transactions, and automated tools help you manage your workflows. If you’re integrating AI into your financial operations, discover how Mercury’s security features can help you build your business safely.