Why Financial Firms Can’t Afford to Ignore AI Governance in 2025

This article from RFA explores how financial firms can use AI responsibly, highlighting the need for strong governance to manage risks like bias, data privacy concerns, and regulatory compliance issues.

The use of AI in financial firms is at an all-time high, thanks to recent advancements in generative AI. Financial firms are now using AI for threat detection, log analysis (for cybersecurity), document summarisation, report generation, and much more.

While these use cases are greatly improving efficiency, AI also comes with certain risks. One major concern that comes with using AI is data privacy, which can lead to regulatory scrutiny if not properly managed.

That’s why it’s crucial for financial firms to take the right steps to use AI responsibly - both to avoid regulatory fines and to protect their reputation. In today’s article, I’ll walk you through practical ways firms can implement AI responsibly, and why ignoring AI governance is no longer an option in this era. Let’s kick things off with the basics of AI governance.

What Is AI Governance?

AI governance is the process of setting rules, guidelines, and checks to make sure AI is used responsibly and safely within an organisation. The goal of AI governance is to ensure that AI is used in a manner that complies with regulations and does not cause any harm to the organisation’s stakeholders.

Key Areas of AI Governance

The key pillars that firms must focus on when implementing AI governance include:

Policies: Set clear rules and guidelines on how AI should be used within the organisation. These rules should cover what AI can and cannot do, data privacy, fairness, and how to handle mistakes or misuse. Good policies help avoid legal and ethical issues.
Oversight: Firms must assign specific people or teams to regularly check how AI systems are working. Their job is to catch problems early, make sure the AI is following the rules, and update systems when needed.
Accountability: Make sure there is a clear person or group responsible for each AI system. If something goes wrong, there should be someone to explain what happened and take steps to fix it. This helps build trust and prevents people from blaming the AI alone.
Transparency: AI decisions should be easy to explain, even to non-technical people. This means being clear about how the AI works, what data it uses, and why it makes certain choices.

The Risks of Poor AI Governance

Financial firms that don’t use their AI systems correctly may face these risks:

Data privacy concerns: The quality of output from most AI systems largely depends on the quality and quantity of the data used to train them. To improve the reliability of AI systems, feeding the models with more data during training is a necessary step. However, if this data includes personal or sensitive customer information and it's not handled properly, it can lead to privacy violations and data leaks.
Regulatory compliance risks: Governments and regulators in different sectors, including finance, are starting to introduce strict rules around how companies use AI. If a firm doesn’t follow these rules, it could face legal penalties or massive fines that could amount to millions of dollars.
Bias in AI models: If the data used to train an AI model is biased, the AI might make unfair decisions. For example, it could favor certain types of clients or ignore others based on unfair factors such as gender, race, religion, and more. This can cause significant backlash, especially for financial firms that deal with diverse groups of stakeholders.
Lack of transparency or explainability: Many AI systems are like “black boxes” where it’s hard to understand how they make decisions. In finance, where decisions must often be explained (e.g., loan approvals), this can be a big problem. Fortunately, several AI system providers are beginning to integrate features into their AIs that guide users through the decision-making process.

8 Steps to Use AI Responsibly

Now that we are aware of the many risks associated with not using AI responsibly, let’s explore the various ways financial firms can practice the responsible utilisation of AI solutions:

1. Establish Internal AI Policies

Firms need to set clear, written guidelines about how AI should be used across the company. The AI policies should focus on answering the following questions:

What kind of AI tools are allowed?
What data sources can be used?
Who is responsible for developing, approving, and monitoring AI tools?

Having clear answers to the above questions helps avoid confusion and ensures everyone in the firm is on the same page.

2. Compliance, Legal, and IT Teams

AI decisions shouldn’t be left to just the tech team. All key departments within financial firms must be involved in determining how to responsibly utilise AI. Get input from:

Compliance teams: To make sure AI follows the ever-changing financial regulations
Legal teams: The legal team reduces the risk of lawsuits or rule violations
IT teams: The IT and cybersecurity teams ensures that the AI systems are secure and work correctly

Having input from the different teams in your financial firm ensures that your AI tools meet ethical, legal, and technical standards.

3. Audit and Monitor AI Models Regularly

AI systems can start to "drift" over time. This means there is a possibility that their decisions could become worse or less fair as data changes. Financial firms must ensure that the technical teams handling their AI systems schedule regular reviews of the AI’s behavior. They should look for signs of bias or unfair treatment in the results.

If any unacceptable variations are found, they should adjust or retrain the models as needed. This step ensures that the AI continues to perform its intended functions correctly and fairly.

4. Ensure Data Used is Clean, Legal, and Unbiased

As stated earlier, the quality of results from AI systems depends on the data used to train the models. When training AI models:

Use data cleaning tools to remove duplicate, outdated, or wrong data
Avoid data that includes sensitive information unless you have legal permission. The firm’s legal team needs to be involved in the process.
Check if your data might lead to biased results (for example, leaving out minority groups). This can be done during beta testing with internal teams.

Using clean and fair data helps firms build AI that treats everyone equally and gives accurate results.

5. Train Staff on Responsible AI Use

Employees in the financial sector are among the primary beneficiaries of AI tools, especially user-facing ones like ChatGPT and Gemini. That’s why it is crucial for them to understand how to work with AI tools properly. Firms should offer regular training sessions covering the basics of AI, potential risks, and guidelines for safe usage.

Employees should also be taught how to report suspicious or unusual behavior exhibited by AI tools. This approach builds awareness and reduces the chances of misuse due to ignorance.

6. Use Explainable AI (XAI)

Financial firms must choose AI systems that can explain their decisions in simple terms. This helps teams understand how and why decisions are made. Knowing how your AI systems produce their results is also useful for audits, customer inquiries, and regulatory checks. Overall, explainable AI builds trust and makes it easier to identify problems early. Firms can use third-party tools such as SHAP and LIME to further understand how AI systems make decisions.

7. Set Up a Human Oversight Process

AI shouldn’t make big decisions independently. Financial firms must make sure there is a human who can review or override AI decisions, especially for sensitive matters like loan approvals or fraud detection. There should also be clear records of who approved what for accountability purposes. Having human oversight helps avoid automated errors that could directly or indirectly hurt customers.

8. Keep Up with AI Regulations and Industry Standards

AI regulations are constantly changing, especially in the finance sector. With generative AI expected to evolve rapidly over the next couple of years, we anticipate regulatory requirements will adapt alongside these developments.

That’s why it is crucial for financial firms to stay updated with laws from regulators such as the SEC, FCA, and the EU AI Act. Firms can utilise automated tools that track regulatory changes in real time, enabling them to understand promptly how these changes will affect them. This helps protect your firm from legal issues and keeps you ahead of the curve.

Final Thoughts

AI governance in the finance industry is more important today than ever, as AI is now being integrated into almost every part of a financial firm’s operations. This makes it critical for firms to be cautious and responsible in how they use AI - especially in areas that involve handling sensitive data or making high-impact decisions.

Of course, using AI responsibly may require taking a few extra steps, but the long-term benefits make it worthwhile. Firms that fail to use AI carefully risk facing heavy fines and damaging their reputation among clients and the public.

For firms that don’t have the internal capacity to integrate AI solutions in their operations while staying compliant, outsourcing to a managed IT provider like RFA is a smart move. At RFA, we provide tailored AI governance solutions that allow financial firms to unlock the full potential of AI without exposing themselves to regulatory penalties and other risks of AI misuse.