Data is key. Our businesses cannot function without it and the better quality data it is, the more likely that we will continue to thrive. We can target clients with content which we know will be of interest to them and invite them to events they genuinely wanted to go to anyway. It also enables us to offer more streamlined services and use analytics to assess profitability in novel ways. Importantly, in today’s digital economy, efficient information management enables us to build trust. Clients want to know that their data is protected, that businesses will not be selling it at the earliest opportunity and that once you have instructions to structure their investments in a certain way, it will be done, and at a competitive price.
Technology does have its constraints and risks. In many ways, it is still a largely immature market. However, it is primarily an enabler. In a world where margins are constantly being squeezed, the cost of regulation continues to increase and an alternative provider is available at the click of a mouse, the costs of doing business have to be managed with great scrutiny.
Routine data processing/analysis tasks can be automated as a way of reducing cost and facilitating the free time humans need to interact with their clients, or perform other high value tasks. Statistical analysis has been used for a number of years to inform investment decisions, based on historic performance modelling. The rise of so-called “robo advisers” has highlighted the progress that has been made in this area. Algorithms are used to assess historic performance, other market data and generate recommendations for investments based on pattern-spotting.
Complex structuring questions, ethical concerns and personal preferences are generally beyond the realm of most AI at this point, at least from a commercial viability position. There is also the vexed question of how machine learning accounts for the “market makers” or those who are in emerging technology space. The problem with machine learning is that it is doing just that – learning from the past, not necessarily breaking through new boundaries for the future.
Human relationships and the ability to understand a client’s motivations, needs and insecurities remain fundamental to the private wealth industry. It is also worth noting that not every problem requires a “tech” solution; there has to be a use case for its purchase, testing and installation.
The same goes for investments into the technology businesses themselves and “crypto” assets. There appears to be a degree of “fear of missing out” hitting the industry, given the number of enquiries we’ve received around investments into “crypto” assets. In order to take informed decisions in relation to this area, a detailed knowledge of the underlying technology is required. In addition, given the regulatory uncertainty and volatility of the assets, it is unlikely that many custodians will have a mandate to take such risks with client money, given the price movements even during a given day. Nevertheless, as the market matures and data on performance is gathered, they may be ones to watch in future, as part of a more diversified portfolio.
We have seen blockchain technology deployed in the private equity fund space, digital wallets becoming common and identity storage projects overhauling traditional “on boarding” processes. Some of these technologies operate by reversing our existing perceptions. Instead of visually verifying a passport, the business scans it to identify any potential flaws, for example. The potential for such tools to vastly reduce administration time, duplication and fraud is so significant, that we anticipate that these projects will become global in the coming years. Indeed, as with blockchain, it is likely that whilst specific use cases that exist today will evolve (or sink) over time, the underlying technology will remain.
A recent study by Accenture estimated that the use of AI could drive an increase in profitability of around 38%, significant by anyone’s standards. As the technology becomes commercially viable, then the costs of routine processes will become negligible – the value will be in the human relationships and the ability to use and analyse complex data to the benefit of the clients and the business itself.