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2 ways Artificial Intelligence can save traditional asset management firms

Robo Advisers and digital-first fintechs are disrupting traditional asset management firms. AI might just save them.

Undeniably, the world of asset management has been thoroughly disrupted these last few years. Robo advisers and digital-first banking services, developed by both startups and large banks, are attempting to automate the work of the personal banker. Clients are becoming less willing to work with a single personal banker, fearing a risk that their bank might default (a trend that is reinforced by the memories of the financial crisis of 2008 and ongoing headlines of looming recessions and negative interest rates). Finally, many clients have started questioning the fees that their asset management firms are charging them, due to the availability of low-cost funds that track popular indexes. With all of these challenges, you might be forgiven for having a gloomy outlook at the asset management industry.

Most traditional asset management firms have responded to these challenges through two primary measures: cutting on costs by reducing their staff and pushing for growing digitization. In both of these challenges, asset management firms are starting the race very late. The most succesful ETFs have purposefully been incredibly lean, and startups don't have the weight of ancient, legacy systems holding them back.

A third, promising measure is slowly picking up speed: some enterprising asset management firms are doubling down on building and improving their privileged relationship with their customers. One of the ways they are accomplishing this is by unleashing Artificial Intelligence on the troves of data that they have accumulated. Below, we explore two promising areas where asset management firms are applying AI to improve their customer relationships.

Predicting customers' financial planning needs

Wouldn't you love a banker that knew your needs before you did? Asset management professionals have long attempted to better understand their customer's needs, often through regular contact, and more recently, through the use Customer Relationship Management software (CRMs) to keep track of their interactions with their customers. However, few asset management firms leverage this data to anticipate their customers needs.

This is clearly a waste of valuable information: machine learning algorithms can use this data to generate rich, hyperpersonalized and accurate predictions of future needs of customers, in a way that a human asset manager simply wouldn't be able to. More specifically, while human asset managers learn through their own interactions with their clients, machine learning algorithms can learn based on all of the firms'customer's experiences. This means that the machine learning algorithms are capable of having a global understanding of all clients' needs, find similarities, and make detailed predictions for each specific client. Furthermore, the insights generated by these algorithms can be used to facilitate knowledge sharing between asset managers.

While this approach is promising, two challenges remain. Firstly, the algorithm's ability to learn is limited by the amount and quality of information that is codified in CRMs. Firms that are the most diligent in capturing information stand to win the most of applying AI and machine learning in this area. Secondly, there exists a real fear, and sometimes resistance from asset managers to use these predictive algorithms, fearing that these might replace them. In practice the algorithms generally end up symbiotically enhancing the asset manager's daily work, much to both the customer's and the asset manager's advantage. Nevertheless, change management remains an important focus in these projects.

Understanding friction points that cause customers to churn

The cost of churn for asset management firms is very large, as customers tend to both have a very high cost of acquisition, and a very high lifetime value. As such, making sure customers don't leave the firm is an important challenge that often doesn't attract enough attention and support from top management. Once again, machine learning and AI are perfect candidates to leverage the rich history of asset management firms to predict which customers are likely to churn, and when this might happen.

Furthermore, beyond the individual prediction, these algorithms can help understand the general reasons why customers churn, allowing asset management firms to not just proactively deal with at-risk customers, but to act on the root causes that have caused customers to churn in the past.

And beyond the core challenges...

AI also shows great impact in a number of fields that are shared by many business, such as pricing, reducing personnel turnover, etc. At this point, it's clear that AI has an important role in the asset management industry, and is quickly transforming the industry, both in its core and in general business challenges.

If you're curious to find out more about AI and asset management, feel free to contact us!

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