Analyzing and interpreting stored data to make more accurate decisions is the new scenario for private banking in Switzerland. Artificial intelligence and data science can help reduce risks and costs, according to academic studies conducted in Switzerland.
The rate at which new technologies are being adopted has soared in recent years, and everything suggests that this situation will continue in the near future. Like many other stakeholders in different sectors, banks are not oblivious to this trend. Many banks are focusing their efforts on reacting quickly and efficiently to adopt these new technologies and avoid falling behind in the race to digitalization.
For Swiss private banking, adapting to a new data-driven scenario (decision-making based on analyzing and interpreting stored data) is crucial to maintaining its leading national and international position. And there is one technology in this scenario that is gaining increasing prominence: Artificial intelligence (AI).
Data science—key to the financial sector
Data is the foundation of our knowledge of the world. In recent years, the science of analyzing vast amounts of data has revolutionized how organizations across all sectors analyze their current situation, make decisions, and plan for the future.
Data science combines mathematics, statistics, programming, and various other disciplines to discover practical information contained in the data that can be difficult to identify without the help of technology.
As highlighted in the study Data-driven Banking in Switzerland, conducted by experts at the Institute of Financial Services Zug (IFZ) at the Lucerne University of Applied Sciences and Arts (Switzerland), banks can use data science to reduce costs and risks and increase profits.
According to this study, digitalization has made a significant and positive impact on Swiss banks in recent years. These banks have become more efficient and have optimized many processes. However, the authors point out that "most Swiss banks are yet to make a serious commitment to data-driven banking." Consequently, there is plenty of potential to increase the penetration of various tools and technologies and to make the most of data science.
The authors add that doing this could further improve the position of the Swiss financial sector by generating revenue growth and delivering enhanced customer experiences.
AI in Swiss private banking
For years, Big Data has dominated the headlines in data science. However, AI is gaining increasing prominence today. A multitude of tools are derived from this technology. The most widely used tools in sectors such as banking leverage the power of machine intelligence to perform tasks in seconds that otherwise could take hours. Some examples include interpreting, writing, summarizing, and translating texts.
For workers in the banking sector, AI tools for performing tasks can translate into improved productivity and efficiency. A study carried out by the Swiss Financial Market Supervisory Authority (FINMA) lists the areas in which AI is most commonly used in the financial sector. These include:
- Monitoring of customer activity and transactions. For example, AI is being used to detect money laundering, credit card misuse, and fraudulent payment transactions.
- Perform portfolio and suitability analyses.
- Develop business strategies.
- Automate processes related to document processing and creation.
As Microsoft explains, workers can access information quickly, create personalized emails with the help of AI, and summarize conversations with customers. This process optimization results in improved service.
According to a study carried out by IFZ experts, while many Swiss banks already use AI-based tools, few consider it a critical element in their strategic planning. Despite the benefits, its penetration is relatively low. However, the main forecasts indicate that the use of AI will increase in the coming years due to the high rate of innovation in this field and pressure from fintechs.
As for BBVA, a partnership with Open AI to deliver 3,000 ChatGPT Enterprise licenses to the Group's employees has made it the first European bank to pilot this AI tool in a secure and responsible way.
However, this also poses some challenges. AI models need to be fed high-quality data, and the tools need to be subject to reliable access controls to comply with both Swiss and international financial services standards and regulations.