Structurally, passive investment strategies are pushing down fees and increasing competition, at a time when active managers are struggling to generate consistent returns in an extended period of loose monetary policy and negative interest rates. Post-crisis regulation is hitting the industry too, with a bigger compliance and reporting burden raising costs, while more stringent data protection legislation demands a rethink on the sharing of client information between stakeholders throughout the fund value chain.
A recent McKinsey report argued that asset managers remain digital laggards, however, when it comes to using technology to streamline processes, both to reduce costs and deploy new data techniques to extract greater value from client management and investment strategies.
Creating digital alpha
A few asset management firms are investing heavily in their operations and technology functions, with the aim of achieving reduced long-term costs, incremental revenue gains and improved fund performance. McKinsey says the correlation between digital leadership and better performance is no accident.
Some firms surveyed by McKinsey are creating ‘digital alpha’, and they share common traits. First, they have eliminated the boundaries between their operational and technology units, combining their budgets and sharing long-term development strategies. Secondly, they are reducing costs in legacy areas, erasing duplication and shutting down outmoded systems, as well as investing in new data analysis, digital capabilities and tech talent. Finally, they are placing operations and tech capabilities at the centre of their competition strategies.
The decision to pool resources and tackle technology at a strategic level should come from senior management, with buy-in by all stakeholders, of course. But which technology should firms be investing in to generate digital alpha once that decision is made?
It may help to look at the issue from the perspective of what results the company seeks for different stakeholders. These can be categorised for simplicity’s sake as clients, the investment process, and compliance/back-office. For each, any technology should seek to reduce cost and boost productivity. The technologies may overlap but they may be deployed for different purposes.
Regarding clients, digitalisation and data management are key. Clients want a better user experience, less paperwork, easier onboarding and greater transparency, in real time, of the investments they make. According to McKinsey, digital leaders who invest well in the client experience and onboarding generate more rapid cycle times and dramatically lower costs. Onboarding of new institutional clients can be reduced to just nine days from an industry average of 23, it says, while costs can be reduced by 75%.
Faster onboarding also leads to quicker engagement by investors and accelerates the recognition of fees. When combined with customer relationship management tools, sales and marketing teams have a better insight into what clients want and how happy they are with their investments.
This can assist in product development, reducing the time it takes to tweak existing products or design new ones from scratch. For sales teams, data can reveal which customers need a bit more attention to boost retention and identify which clients may be interested in investing more or in other products.
Automated know-your-customer due diligence and anti-money laundering controls also benefit groups’ internal compliance and back office functions. Data analytics, machine learning and artificial intelligence can offer multiple benefits, making investment easier for clients, delivering faster and richer insights for market and product development teams, and relieving compliance resources from box-ticking administration and providing more time to scrutinise data and take a proactive approach to AML, financing of terrorism and other supervisory reporting requirements.
The tools available also include outsourcing of administration, software-as-a-service offerings that enable non-core functions to be bought in from outside suppliers, and cloud computing facilities to lower costs and build in instant scale and agility.
Similar technology can also boost the output of investment teams. The obvious example is algorithmic trading of securities, along with machine learning to enhance market impact assessment, increase the efficiency of trade execution, and conduct risk modelling. The sophistication of these tools is increasing rapidly, as fund managers respond to client demand for more consistent returns that require a greater range of investment strategies and manager capabilities, particularly in alternative assets and protection of returns via swaps and other derivative overlays.
Managers need to exploit technological solutions for deal management, pricing, risk allocation, book running, portfolio analysis and due diligence. Again, according to McKinsey, managers who invest in natural language processing – to ensure they are abiding by client guidelines and analytics to eliminate behavioural bias in investment decisions – can boost their portfolio returns by between 1% and 2%.
Any business transformation tends to be easier in theory than in practice. As ever, the tone set by senior management, chief operations officers or chief technology officers is critical, along with demonstrating the benefits. Automating trading functions may have a bigger impact on cost management but is often invisible to much of the rest of the company. Starting with end-to-end journeys whose impact is more visible to portfolio managers and client servicing teams, such as onboarding and client reporting, may win over more digital laggards.