Whilst the data indicates that AUM continue to grow for dedicated digital asset managers, it is also evident that traditional asset management firms are targeting growth derived from robotic and automated advice. So, what are some of the factors contributing to these market trends?
Incumbents catching up
Traditional asset managers fell behind the curve in relation to technological innovation and digitalisation. For example, compared to the banking sector, traditional asset managers were slow to adopt online and mobile-based services. This presented the opportunity for digital asset managers to enter the market, providing low-cost digital solutions, which became particularly popular with the younger retail investor.
After a decade of new entrants to the market, we are seeing signs that dedicated robo-advisers have reached critical mass as traditional asset managers seek to expand their digital advice offering. This is either through the launch of new service lines, or through acquisition of digital and robo-advisers. Examples include Barclays purchasing Scalable Capital in 2020, JP Morgan purchasing Nutmeg in 2021, and both Legal and General and M&G announcing plans to grow their digital advice offering in 2022.
The FCA has reacted to the growing trend of robo-advisers, either through the reiteration of existing requirements or the introduction of additional rules and guidance. We note that robo-advisers in the UK are still feeling the effects of the FCA’s review of the industry in 2018, which resulted in many needing to make “significant changes”. We are aware of follow-up reviews and questioning by the FCA into robo-advice, and so naturally this pushes up the costs dedicated to regulatory compliance within these digital asset managers.
In addition to compliance costs, dedicated robo-advisers have faced challenges scaling-up their businesses in order to maintain profitability.
Due to the saturated nature of the asset management sector, new entrants to the market have needed to incur relatively high marketing costs in order to gain market share. Given the digital asset management business model is centred around providing a low-cost solution for investors, high volumes are required to be profitable. However, given that at present robo-advisers typically attract retail investors whose advice needs are straightforward, they do not benefit from the higher investment values from more sophisticated investors and/or high net-worth individuals (HNWI).
Considering the above, it is likely the industry will continue to evolve over the coming years in respect of robo-advice and digitalisation of asset management services.
Despite the recent growth, robo-advice is arguably still in the experimental phase of development. Resistance remains within certain pockets of the wider advice market, for example, as mentioned digital asset managers have struggled to attract HNW clients. Through our experience, the current industry view is that HNWIs and sophisticated investors require a more specialised and individual service that a robo-adviser cannot currently offer. These investor-types have so far been reluctant to trust the arbitrary results and advice of a robotic service where they have been unable to explain their particular circumstances.
However, quality of advice and client service aside, increased digitalisation of asset management services are seemingly inevitable. Investors will in turn expect to benefit from innovation as the service they receive becomes more efficient through automation, resulting in either reduced costs or receiving more value from the fees they pay.
In the short to medium term, this is likely to result in the growth of hybrid advice services. This is where certain parts of the advice process are digitised, but an element of human involvement is maintained to provide the relevant oversight of the advice and ensure quality of service standards, especially for HNWIs and sophisticated investors.
However, the possibility for fully robotic advice to become the norm in the longer term remains high. We have seen evidence that machine learning and artificial intelligence has the ability to improve over time as it acquires more and more data and is therefore able to accommodate even more complex scenarios and provide valuable advice.