AI risk management in banking: Why traditional frameworks fall short in 2025

The financial sector is known as one of the most critical and advanced when it comes to risk management. Where there is money, there is also fraud, and you are probably aware that fraudsters always try to trick banks and get as much financial gain as possible. This is not new - it has been happening for decades or even centuries.

However, as technology has advanced, so have the risks that come with it. Because of that, banks are under constant pressure to stay one step ahead of the market when it comes to risk assessment. Manual or semi-automatic evaluations and risk management are no longer an option. Besides taking a lot of time, they are inefficient and carry the risk of human error, and they are not in line with the technological age we live in.

Traditional banking risk frameworks were usually made for static systems where behavior can be predicted. But as we said, artificial intelligence brings a new paradigm - these are dynamic risks, meaning they change and grow, just like AI models that can quickly learn, adapt, and apply what they have learned. A similar situation happened when banks moved from on-premise to cloud solutions, but artificial intelligence is without any doubt, a much bigger threat to banking operations.

Let’s find out what are the key AI risk areas for banks in 2025.

As we already mentioned – modern times require a modern and comprehensive approach to risk assessment and management, leaving traditional methods behind. Here are some key areas that banks need to focus on when it comes to AI:

1. Data quality and governance

• Input data quality assessment and validation
• Systematic bias detection and mitigation strategies
• Continuous data drift monitoring
• Historical data validation protocols

2. Model behavior assessment

• Real-time performance degradation tracking
• Decision boundary analysis frameworks
• Edge case identification and handling
• Model explainability requirements

3. Integration point security

• Robust API security protocols
• Comprehensive data flow monitoring
• Third-party dependency assessment
• System interdependency mapping

AI regulations around the world are growing every day. Countries like the United States, China, Singapore, and Australia are quickly working on legal frameworks to regulate AI.

However, the European Union is the leader in this area and has created the EU AI Act. The EU AI Act is the first comprehensive regulation that controls the development, use, and application of AI, classifying AI systems by risk levels:

1. AI systems with minimal risk

2. AI systems with limited risk

3. AI systems with high risk

4. Prohibited AI systems

If you want to learn more about the EU AI Act, visit our guide to the EU AI Act or contact us.

As we mentioned in our article “AI in the Financial Industry: How Banks and Insurers Can Deploy AI Without Regulatory Risks,” banks often use AI systems for credit scoring. In that case, under the EU AI Act, these are considered high-risk systems, which means they must meet the strictest requirements of the EU AI Act—from creating detailed technical documentation to human oversight of the AI system and continuous monitoring.

As we have seen, the risks that AI brings to the financial industry are something banks have never faced before. But just as important is how to manage these risks and, in the end, reduce their impact—or avoid them completely. This is where the most important shift happens: moving from traditional to modern risk frameworks.

Traditional banking risk frameworks were designed for a different time—now in the past. They worked well when systems were static and predictable. Risks were assessed manually, usually only once, and the process was slow, subjective, and hard to scale. These systems simply cannot keep up with the present day, where technology is developing at an incredible speed.

To simplify:

• Traditional systems relied on a manual human review, done on paper and case-by-case
• Modern systems are automated, data-driven, and standardized for the types of risks created by new technologies like AI

Let’s give an example. A bank deploys an AI system to handle credit scoring for their customers. According to the EU AI Act, this system is classified as high-risk. With a traditional approach, risks are assessed once, usually during initial development. But what happens when the AI system learns new things and changes its behavior based on that learning? Problems can arise. So without real-time monitoring and continuous risk assessment, many things can go wrong.

Modern risk frameworks are made for situations like this. They help banks:

• Continuously monitor the performance of AI models
• Detect anomalies, such as bias and data drift, at an early stage
• Document all changes and decisions made
• Stay compliant with AI regulations

Now the question is: how should banks make this shift? Let’s talk about that in the next section.