AI Risk Management in the Banking and Financial Sector

There is no doubt that there is a current trend toward innovation in the banking and finance sector,where utilizingthe use of artificial intelligence (AI) hassignificantly in recent years. The tool is used to streamline the daily activities of operators, which were previously often handled manually using time-consuming systems. The significant increasein usage is largely due to the high risk of cyberattacks. This is therefore in customers’ best interest to understand how their banks handle AI and, consequently, the management of their customer data. Consequently, risk management becomes a central part of the banking sector’s operations.   

AI and automation can generate significant value for many industries. Among other things, they can help companies increase internal efficiency through improved user-friendliness or create a higher degree of personalization in their offerings to customers, thereby improving the customer experience and increasing sales.  

Many modern cloud applications come with either partially built-in AI features to improve user-friendliness, or the ability to further develop AI functionality on your own. The data on which the AI bases its rules and recommendations can either be your own data or data retrieved from an external third party. The possibilities offered by AI willareoften come with increased risk, as it may be necessary to open up the data feed. We have analyzed how organizations can mitigate these risks and how this impacts the risk management process.  

Examples of AI in the banking and finance sector 

The use of AI as a tool is relatively new in the banking sector. Common applications include, for example:  

• Data-driven recommendations for salespeople, where artificial intelligence uses historical data and trends to tell salespeople which customers to call or to whom to sell specific products.  
• For many years, banks have been using AI for fraud detection—that is, to identify irregularities that are likely to be fraud or money laundering.  
• AI is also used to predict the “right” price and exchange rate. This allows banks to know when it is the right time to sell or buy a security or currency. 

The desire and need to automate processes is high among most companies today. To automate processes, AI or other rule engines can be used. Due to the high complexity of AI, risk management requires both increased responsibility and expertise that are rarely available today. Those responsible for risk management need technical, legal, and financial skills in AI. Consequently, dependence on third-party providers increase to implement systems, solutions, combat cyber threats, and enable Open Banking applications. Contact us if you need help.

Tips on how to manage the risks associated with AI: 

1. As the need for knowledge grows, so does the need forgreater preparedness 

As a result, organizations need to clarify these needs, establish new roles, and expand the responsibilities of existing roles. This requires a clear commitment to internal and external communication and collaboration that permeates all teams and the organization as a whole.  

2. The understanding and approachregarding AI must be consistent 

FTo increase understandingof AI and to ensure that everyone works consistently, a joint decision is needed regarding when, where, and how AI should be used. Therefore, establish a common terminology and definition of AI within your organization.  

3. Risk management processes must be continuously updated 

FTo streamline your risk management process, you also need to update your risk classification systems. This process can be based on policies and regulations such as the upcoming AI Act.

Fact: The AI Act is a proposed European law on artificial intelligence (AI)—the first AI law from a major regulatory authority. The law classifies AI applications into three risk categories.  

(1) First, applications and systems that pose an unacceptable risk—such as government-run social credit systems of the kind used in China—are prohibited.  

(2) Second, high-risk applications, such as a resume-scanning tool that ranks job applicants, are subject to specific legal requirements. 

(3) Finally, apps that are not explicitly prohibited or classified as high-risk remain largely unregulated. Read more here 

4. Conduct training sessions and create a knowledge base 

Expand your knowledge base by educating the entire organization about the impact and value of AI-driven solutions. Users’ perception of the value of AI is key to a successful implementation. There is a risk that AI can make incorrect decisions, and it is important to educate the organization to be critical and to inform them that AI is constantly learning and becoming smarter over time.  

5. Streamline controls and work processes 

There are few established rules and regulations governing AI today, which makes it difficult for organizations to know how they are allowed to use these tools. But it will become easier over time asas new regulations such as the AI Act are established. By establishing internal work processes and structures across the entire organization, where you can translate rules into practice, you can avoid misunderstandings and strengthen the links between internal policy documents and regulations. 

Please contact us if you'd like to learn more