Data-driven insights are a real supercharger for a wealth advisory business. And the main ingredient, the data, is already inhouse and waiting to be uncovered. However, most banks and wealth managers are confronted with the complexity and incalculable difficulties of the task early on in their data science journey. With this blog, we help you bring your high-flying expectations into alignment with the sometimes-harsh reality of data science implementation.

Recommendation solutions are sometimes used by wealth management firms and banks to automatically suggest the product that a client may be interested in. They aim to increase client satisfaction and avoid wasting both the client’s and the relationship manager’s time with inappropriate offers.

After introducing non-bankable asset investing in the first article of this series, we here discuss some valuation approaches using machine learning to make nBA investing fit for total wealth management.

This is the first piece in a series of three short articles that will address investing in non-bankable assets and focus on critical considerations when one’s investment portfolio includes such alternative investment vehicles.

It may sound like an exaggeration, but there is hardly any person in the world that has never been part of a social network, or that has never used some sort of technical network. In a similar way, it’s almost impossible to come across any recent article about data science applications which does not mention the term “network”. Hence, it is only natural to ask, "what is a network?"

Most banks frequently face challenges caused by bad data quality and quantity-related issues. Distributed learning is an approach to leverage data across banks and delivers some concrete concepts that could mitigate these issues.