Disruptive Technologies (DTs) have been around for several years. But while they have been successfully deployed in various industries, in private markets, they are still in the early stages of delivering their potential benefits.
The core opportunities are around the automation of tasks, and rapid, efficient, and secure sharing of data amongst stakeholders. Applying these technologies should deliver lower costs and reduce operational, reputational, and regulatory risk and an improved client experience – all aligned with the broader trend we see of needing to enhance the robustness and scalability of private markets operating models.
The subset of DTs most relevant across the value chain are:
- Artificial Intelligence (AI)
- Robotic Process Automation (RPA)
- Distributed Ledger Technology (DLT)
AI is the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition and decision-making. In its most advanced form, it can be used for hypothesis generation and analysis, producing insights and analytics above human capabilities, for e.g. company valuations.
It can be applied to automate the processing of investment documentation, such as portfolio company prospectuses, accounts, capital calls and distributions. These are often delivered in multiple formats and layouts, requiring human judgement when processing, which is time consuming and prone to errors, leading to high operational costs and risk. AI offers the opportunity to automate these tasks. This leads to improvements in processing speed and accuracy, reducing errors in investment decision making and stakeholder reporting. Subsequently this can reduce headcount in these areas.
Robotic Process Automation (RPA)
RPA offers a lower cost way to improve productivity through automation. It’s a software technology that makes it easy to build, deploy, and manage software robots that perform routine, rule-based or repetitive business processes that are time intensive. Human involvement is limited to performing oversight and exceptions-based tasks only.
As my colleague Craig John, Global Director of Innovation at the Davies Group puts it:
“We have seen one robot replace the effort of 17 humans when performing basic, mandate, high volume/low value tasks, however human intelligence is still the greatest asset to any organisation, but only when used correctly.”
In our own work as asset management specialists, we see multiple use cases within operations, specifically in the calculation of capital calls and distribution notices, performing waterfall and investment performance calculations, and reporting (investor, management and regulatory).
If designed and implemented correctly this frees up time for more value-add work, leading to increased employee satisfaction and ultimately retention, while shortening calculation and reporting lead times, with less errors.
Distributed Ledger Technology (DLT)
A distributed ledger, such as blockchain, is a shared database that enables transactions without the need for a singular intermediary. Its non-fungible nature enables representation of items of value (investments, currency, identification, etc.) secured using cryptographic encryption.
We see DLT as having applications in client due diligence, including KYC/AML, as well as improve workflow efficiency and secure deal execution through smart contracts.
The opaqueness within private markets produces a recurring challenge: creating a shared, standardised industrywide securities master, DLT solves for this by providing a secure and efficient data repository. This in turn could aid the delivery of greater transparency around pricing and fund holdings as well as providing a way to share information among multiple interested parties (LPs, GPs, and portfolio companies).
Although these are promising technologies there are limitations:
- AI typically is more expensive than RPA, due to its increased sophistication and use of cutting-edge technologies and specialised skillsets required to implement and maintain.
- RPA can’t fix processes that are poorly designed.
- RPA is limited to uniform, rule-based tasks. Therefore, a suitability assessment is required up front to ensure the expected return on investment is realised.
- RPA success is dependent on data quality, e.g. when calculating capital calls if the input data is incorrect, then so will the resulting output be.
- DLT is the most immature and divisive of the DTs discussed, due to its decentralised architecture and the required shift in thinking around how the traditional financial system operates. As such, implementations of its many use cases are largely unproven.
- DLT presents concerns relating to reliability, data security, costs, and network integration, and its interoperability. For firms to reap the maximum benefits a critical mass of industry participants needs to be using the technology, which currently feels a long way off.
These technologies are relatively immature and there are a limited number of transformations to evidence successful use cases and real value delivered.
The early signs are DTs can help managers to simplify and automate the current fragmented, manual, and complex processes seen across their operating models, leading to increased robustness and scalability. In turn this can support delivery of strategic objectives, e.g. reductions in cost and risk, simplification and improving the client experience. We expect to see the pace of adoption accelerate in the future.
While new technologies are exciting and make promises to transform operating models for the better, firms need to carefully identify the problem first before jumping to conclusions on the solution. This avoids investing in expensive and resource intensive transformation programmes that fail to deliver the promised benefits and return on investment.
It is essential for firms to work with a trusted advisor who understands their unique operating model, and has the necessary knowledge and experience of when and how to apply these technologies to maximise their impact.
Read more in this series:
- Private Markets Investors: Are You Being Served?
- Private markets: data strategy and management challenges
- Private markets technology and data – great idea in theory, but where are we in practice?
- Is there real differentiation in the private markets technology space?