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First issue of the newsletter: AI in banks: Useful use cases in the times of the EU AI Act
Artificial intelligence (AI) is changing the banking world. It helps to automate processes, manage risks and detect fraud. The EU AI Act ensures that AI is used safely and ethically. The two together are an interesting combination. As a software manufacturer for banks we want to look at it. We summarize useful use cases and explain the challenges.
Background
The banking industry is undergoing dynamic change, which is partly influenced by the introduction of artificial intelligence (AI). There are a wide variety of use cases: machine learning makes it possible better to assess risks and detect fraud more quickly; chatbots improve customer service, and algorithmic trading strategies redefine financial markets. Overall, the integration of AI is leading to a more agile and customer-oriented banking landscape. The EU AI Act, passed by the European Parliament last March 2024, laid out guidelines for the responsible use of AI. Restrictions on AI – whether through laws or financial market supervision – aim to minimize the risk of misconduct and systemic risks in the financial sector and to strengthen public confidence in the use of AI. We have discussed the topic of AI in banks with some of our customers. WE would like to share realistic and sensible use cases and their limitations.
Use Case 1. Increasing efficiency through automated processes
Increasing efficiency in banks through robots?
The implementation of AI in internal banking processes enables a significant increase in efficiency. Automation allows quicker processing of transactions, loan applications, document analysis and customer inquiries. This leads to more efficient use of resources and sustainably improves the customer experience. Banks can reduce their operating costs while improving their competitiveness. This automation of routine tasks frees employees from time-consuming activities and allows them to focus on the more complex tasks that require human expertise. This helps to increase efficiency and increases employee satisfaction and productivity.
Financial institutions implementing automation using AI must ensure that their AI applications comply with applicable laws and regulations. This is especially true regarding data and consumer protection. The EU AI Act requires AI systems to undergo a conformity assessment to ensure their safety and ethical compliance. Regulatory authorities require regular monitoring and testing of AI systems to ensure that they are functioning properly and do not have any unwanted effects. For certain AI applications, the EU AI Act requires human supervision to minimize potential risks.
Use Case 2. Risk management and fraud detection
Security and credibility in banking are an asset.
AI-powered algorithms can identify complex patterns in huge data sets. This enables banks to identify potential risks early and respond appropriately. Increasing security measures helps to strngthen customer trust and ensure banks’ financial integrity. Precise analysis of large amounts of data also enables banks to identify and combat fraudulent activities. Timely detection of fraud not only minimizes financial losses but also protects the banks’ reputation and credibility.
Regulators often require that the risk management and fraud detection decision-making processes of AI systems are transparent and traceable. This aims to ensure that they are not discriminatory or misleading. The EU AI Act provides for appropriate regulations on transparency and traceability. Banks must therefore implement robust risk management processes to identify, assess and manage potential risks associated with the use of AI, including operational, legal and ethical risks.
Use Case 3. Personalized interaction
Data as the basis for personalized interaction.
Another key benefit of AI for banks is personalized interaction with customers. By analyzing large amounts of data, banks can better understand the behavior and needs of their customers. On this basis, they can provide tailored offers and recommendations that meet the individual financial requirements of each customer. This personalized support strengthens customer loyalty and improves long term customer satisfaction and loyalty. As regards direct interaction with customers, the integration of chatbots and virtual assistants enables offering customers high-quality service around the clock. These digital assistants can answer customer enquiries quickly and accurately, further increasing customer satisfaction.
Sensitivity of financial data requires financial institutions to ensure that the data used by AI systems is secure and protected from unauthorized access. In addition, the EU AI Act prohibits certain applications of AI, such as biometric identification and categorization of natural persons and the management of critical infrastructure. Therefore, the interactions will be useful but only in certain parts of the banks’ offering.
Conclusion
The integration of AI into the banking industry enables a wide range of opportunities to improve efficiency, risk management and interaction with customers. Within the EU AI Act, banks can exploit the potential of AI while ensuring compliance with ethical and legal standards. Continual development and adaptation to new technologies will be crucial to ensuring the future viability of the banking industry.
The big problem with AI, as we and our customers see it, is not the use cases and possible restrictions imposed by laws. The biggest challenge lies in the implementation of the projects and in the framework that the use cases allow. This means: Is the financial market supervisory authority cooperating and does it have the same interpretation of the laws? Which of the expensive US licenses do banks use and is there an implementation partner who has the know-how of AI models and can apply it to banking? How can banks train a data model and which data are used for this? and Who is responsible for the decisions made by the AI? The answers to these questions will then be a little more extensive than this overview.
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Note: A basic version of the article and the images were created with AI (GPT-4 and DALL-E-3).