Open Access

Anita Mentasti

• Artificial intelligence is a disruptive technology, offering increasingly more opportunities to companies. However, the low digital maturity of the private banking sector, makes it hard for private banks to take advantage of this opportunity. Simultaneously, customers are expecting more digital solutions, forcing companies to adapt their services. The aim of this paper is to provide an overview, drawing conclusions about whether the implementation of AI technologies is profitable in the private banking sector. This thesis is based on recent research about current possible applications and the respective benefits, risks and costs. Two use cases will be thoroughly analysed: the application of automated credit risk management systems and AI powered indexes. In the first case, the software NOLA 2.0 will be evaluated and used as a benchmark to highlight the positive and negative aspects deriving from AI credit risk management software. In the second case, the AI powered index AiPEXAR will be presented and compared to the most common ETF S&P 500, analysing the differences in their computation and their performance over time. The analysis concluded that, even though the benefits substantially depend on the individual company, AI chatbots, customers' engagement, credit risk management software and banking apps are advantageous for private banks. Yet, the implementation of AI powered indexes may be precocious and therefore not yet profitable. It can also be concluded that for private banks, whose core competitive advantage lies in the expertise of the relationship managers, the digitalization of advisory may lead to unsatisfied customers.