This blog aims to capture the key trends that are shaping the wealth management space, even as the world struggles to find a way out of the pandemic, and banking habits get constantly redefined.
The competitive context
The most dominant trend is digitalization, including the willingness to receive advice and services over mobile and the web, sometimes through video chats. This has, at least partially, been spurred by the pandemic. The onset of compelling digital customer experiences, powered by Ddesign Thinking, has spearheaded this trend. It is already not uncommon to have financial wellness parameters and achievements against goals being tracked on wearables and mobile devices by the use of infographics.
There has also been a proliferation of Robo advisory, with global assets under management by Robo advisors exceeding US$ 1 trillion in 2020. However, a significant section of all customers (including 46% of millennials, according to a recent survey by ThoughtLab) retains a strong preference for human interaction for advisory.
As an increasing number of millennials become wealth management customers, the preference shifts to themes such as ethical investing and ESG, which require significant investments in data management and analytics. However, these preference shifts are not unique to millennials. There is also a considerably higher tendency to switch providers, particularly with diverse options being available in the form of new wealth tech players (including Robo advisors).
Overall, the economics of private banking and wealth management has, of late, been characterized by increased competition, expectations of lower fees in the wake of the pandemic, higher costs of regulatory compliance, and margin pressures due to historically low interest rates.
Broadly speaking, the wealth management providers have two responses to the changing competitive scenario.
The first is to augment the depth of their offerings. Alpha-seeking customers are turning to specialized products, and providers are scrambling to meet that need. A study by ThoughtLab has found that 67% of investors are planning to use, and 61% of providers are planning to offer alternative investment products in the next two years. In this context, crypto and ESG investing are the current game-changers. An example of a company addressing this trend is Vestrata (www.vestrata.com), who have collaborated with leading investment solution partners to provide access to a wide range of alternative investment solutions.
Advisory models are also undergoing innovation, with goals-based advisory, a style that leans heavily on behavioral finance, becoming popular, particularly for advising high net worth customers.
The second response is to increase market breadth by embracing new segments – viz., expansion to the mass, affluent market and, demographically, the millennials. This strategy is powered by technology, elements of which are discussed in the following sections, that drastically reduces the cost of acquisition and servicing, while delivering economies of scale.
Service innovation, differentiated solutions, and the ability to target a broader market are the key attributes of the evolving wealth management space. These attributes are powered by technologies such as analytics, AI/ML, and the API economy. A few use cases are:
Digital customer onboarding: Frictionless, low-touch and rapid KYC, and onboarding, primarily through integrations with diverse internal and external data sources, including open banking feeds. The experience can be entirely digital, with biometric scans for identity verification, and digital document submission and signatures.
Hyper-personalization of content, medium, and frequency of advice: Proactive, anticipatory and relevant investment advice, delivered on a customer’s preferred channel(s), taking into account the customer’s demographic and financial profile, behavioral patterns, stated and extrapolated preferences and ethical concerns, and peer group vectors. This use case is served by big data and analytics, coupled with AI/ML, offering deep and actionable customer insights.
Investment portfolio design and monitoring: Balanced thematic portfolios created by scanning the market and evaluating companies against pre-defined factors, followed by ongoing monitoring of the candidate companies for continued adherence to the investment principles. For example, Math Labs (https://www.mathlabs.co.uk/) employs a proprietary ESG information network and automated algorithmic due diligence to build and monitor ESG portfolios.
The hybrid advisor
According to Statista (https://www.statista.com/forecasts/1262653/robo-advisors-managing-assets-worldwide-by-region), global assets under the management of Robo advisors are expected to grow to approx. US$ 2.9 trillion by end-2025. While the robotic advisory is expected to find significant uptake among millennials, mass market, and mass affluent customers, high net worth individuals are likely to continue to prefer in-person advice. A quick analysis of the parameters that determine the comparative merits of the two forms of interaction is given below.
Cost to acquire: Cost of new customer acquisition is higher for stand-alone Robo advisors, due to the higher marketing costs involved, while, for human advisory, this is typically an extension of an existing relationship.
Cost to serve: Due to lower people costs and overheads, break-even assets under management are 52% less for Robo advisory than human advisory (Source: Cost-Income Ratios and Robo Advisory ~ Deloitte). As expected, the Robo advisory is most cost-efficient for mass market customers.
Human element and customization: Depth and complexity of investment advice needed for high and ultra-high net worth segments merit human advisory. However, the latest developments in AI go some way in addressing behavioral elements like goals-based advisory.
As a summary, the graphic captures the respective sweet spots of human advisory and Robo advisory.
The diagram also indicates a fairly large demographic segment where the two methods overlap, creating a target market for the hybrid advisory model. This model is likely to grow in usage as providers find the best ways to leverage robotic and human elements selectively and contextually, combining the merits of both.