Key Trends in Wealth Management

Journey May, Director - Wealth Management, Associated Bank

Wealth management funds' earnings are slipping, making it difficult to keep clients who turn to passive index funds. In order to recognise cognitive biases and recommend corrective steps, fund managers respond by using machine learning.

If they are to capitalise on business opportunities, wealth management companies must stay on top of current trends. One of the most important developments in this sector in recent years is the change in investor demographics.

Another field where investment managers need to stay competitive is technology. Big data will help them gain a deep insight into the goods and services they can sell, providing their customers with greater value

Artificial intelligence (AI) can also push new technologies to be built that can significantly boost both front and back office operational performance. Some of the most important changes happening in wealth management during 2019 are listed in the following ten trends.

Techniques of Debiasing

Wealth management funds' earnings are slipping, making it difficult to keep clients who turn to passive index funds. In order to recognise cognitive biases and recommend corrective steps, fund managers respond by using machine learning.

See Also: Top Artificial Intelligence Companies

A wide range of data can be analysed by these techniques to demonstrate where emotions influence investment decisions, causing warnings to the manager. Debiasing techniques can assist fund managers to build long-term winning strategies based on their intuition rather than one-time wins based on justification.

This strategic change would raise earnings and increase the value for owners in actively managed funds

Digital Channels

To build services that meet changing consumer expectations by optimising the value of customer data, advisors need to leverage technical approaches. In order to develop value propositions aimed at each customer, many companies are beginning to use predictive analytics.

To predict potential client actions, this approach should involve the analysis of unstructured data, such as investor expectations, lifetime values, and investment history. The process of up-selling and cross-selling new products to customers can be facilitated by these forecasts.

Artificial Intelligence

Machine Learning

Cognitive

Big Data

Predictive Analytics

Blockchain