close_search
{{searchMessage}}
More
Financial services
ICT

Zühlke develops data solution for VP Bank

6 Oct 2022
Schlieren/Vaduz - Zühlke is developing a data-driven investment recommendation solution for VP Bank in Liechtenstein. It is based on machine learning and helps investment advisers to select the right product for their clients.

Newsletter

So that we can send you our newsletter, we need your express consent to process and pass on your personal data. By providing us with your email address and clicking the check box, you give your consent that we may send you news about the Bankers Association and our newsletter, and that we may process your personal data for that purpose.

By clicking the check box, you also confirm that you have taken note of our web notice and general data protection information. This consent is voluntary.

You are entitled at any time to request the LBA for information on the data we store about you. You can also exercise your right of withdrawal at any time without giving reasons and withdraw your declaration of consent with effect for the future by unsubscribing from the newsletter either by email (info(at)bankenverband.li) or directly via the link in the newsletter message.

According to a press release, the innovation service provider Zühlke based in Schlieren in the canton of Zurich has started a project with VP Bank, which has its headquarters in Vaduz, to develop a data-driven investment recommendation system. This aims to help client advisers with decisions related to investment recommendations based on the individual customer profile.

According to the press release, during the discovery phase, Zühlke worked with the bank’s management to identify the model for a business case with huge potential. Client advisers rely on the investment recommendations of the research team and are often supported by specialized product experts and portfolio managers. This requires in-depth knowledge of the financial markets and client requirements, such as their risk appetite. The more customers the bank has and the more products it offers, the higher the complexity. The aim of the Zühlke project was to support client advisers with the recommendation process and improve specific targeting of customers, according to the press release. Artificial intelligence and machine learning are used for this purpose.

The data science team at Zühlke has developed and assessed various machine learning models, working together with specialists at the bank. Furthermore, it has built a compliance engine, which automatically filters out clients that do not suit the investment idea. This process automation takes a tiring, repetitive task away from the client advisers. They are now able to directly contact the most suitable clients with the investment idea.