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.

Hearing of a synthesizer, most people’s thoughts might wander to electronic music rather than to digital data. But the process of synthesizing a database is exactly what banks should have in mind when it comes to accelerating their software development and testing.

Blockchain has been a buzz word for a few years but the announced disruption seems to have disappeared. So where are we since then? Are the promises all gone? Is there really something beyond the hype?

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.