Creating Value in Private Banks by Successfully Using Recommender Systems
Horn Daniel, 2019
Betreuende Dozierende: Lionel Pilorget
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With the advent of emerging technologies in the banking industry and the rise of new competitors, banks are forced to become more innovative in order to overcome the ever-increasing margin pressure. In addition to new services recently introduced by private banks such as chatbot support and enhanced mobile capabilities, the industry is focusing on intelligent portfolio advisory robots. Whereas some Fintech solutions offer a fully automated approach, some banks have decided to adapt a hybrid approach which is led by a customer relationship manager and supported by a robo-advisor. Due to the importance of personal relationships between the customer and the relationship manager, private banks may not be fond of fully replacing the customer relationship manager by a robot. Accompanying a Swiss based private bank during the implementation of a machine-learning based recommender system which is generating the next best personalized investment recommendation for customer relationship managers (CRM), this master thesis aims to confirm that the successful use of this system increases the financial performance of the bank. Furthermore, the main goal of this thesis is to identify the critical success factors of a project dealing with the introduction of an AI recommender system as well as to assess, what impact the solution has on the banks' relationship managers and customers....
Studiengang: Business Information Systems (Master)
Fachbereich der Arbeit: Wirtschaftsinformatik & IT-Management