The private equity industry has faced a challenging backdrop over the past 12 months, with macroeconomic uncertainty and rising interest rates cooling fundraising and dealmaking activity after a bumper 2021.
In line with the increasing volatility, the process of valuing existing portfolios has become more complex, while the pricing of new deals has been made trickier without steady EBITDA forecasts on which to model.
These developments have in turn intensified LP scrutiny of GP activities, with investors seeking comfort on valuations processes and more detailed, real-time information on portfolio company performance.
As such, tech-enablement has been at the centre of the private equity industry’s response to higher expectations for information and insight into how managers are navigating market dislocation.
But integrating technology into investment processes in the front and back offices is not without its challenges, even when taking into account recent strides in adoption.
Upending the status quo
Private equity firms haven’t always been the quickest to digitalise their operations, despite the asset class’s penchant for investing in software and technology businesses.
Personal relationships, networks and dealmaker contact books have long been the drivers of success in the industry, while private equity’s illiquid structure has, until relatively recently, seen limited demand for providing portfolio reporting beyond quarterly updates.
“Our industry, which is very good at transforming its investments, is only just starting to appreciate what you can derive from data if you have the ability to create actionable information,” says Joe Giannamore, group executive chairman at AnaCap Financial Partners, one of the first managers to place data science at the centre of its operations.
The covid-19 pandemic and current market disruption have, however, upended the status quo and obligated all firms to accelerate the adoption of technology within their own businesses.
“The pandemic saw a step change in investor expectations around GP response times to information requests,” says Yuriy Shterk, chief product officer at Allvue Systems, a technology provider for investment managers in the private capital and credit markets, backed by Vista Equity Partners. “LPs couldn’t wait for two weeks for reports on exposure to new market risks. The dynamic has changed, and investors now demand access to real-time information, at their fingertips.”
At the same time that investor expectations have increased, so too has regulatory oversight, spurring on even greater tech adoption as managers look for tools to help process increasing volumes of filings and data.
Bridget Deiters, senior managing director and international lead at contract automation platform Ontra, says: “In the last decade we have seen tightening regulation, heightened competition and increasing complexity in private markets. Ten years ago, for example, the average side letter would have had three terms on average. That has mushroomed to more than 32 terms.
“As the complexity of obligations that GPs owe their LPs has increased, GPs have moved to maximise efficiency through tech-enablement.”
A single source of truth
For most GPs, the first step on the tech-enablement journey has been to warehouse all firm data in a central ‘data lake’. This infrastructure has proven crucial for streamlining reporting workflows and speeding up response times to LP queries.
“When data is in one place, response time to LP requests accelerates,” says Lucie Mills, value creation and ESG partner at NorthEdge Capital. “You don’t have to go around to the investment team and all the portfolio company CFOs to gather data that is sat in a spreadsheet somewhere. You can answer questions in close to real time with data that is sat in a central system.”
“Data analytics makes portfolio company management more proactive, more data-driven and less anecdotal”
Justin Partington
IQ-EQ
Although most managers are in the early stages of data warehousing, those that have already built up their resources in this area are taking advantage of automation and artificial intelligence technology – with the latter heavily relying on large data pools – that can be layered over data lakes to enhance the collection, reporting and analysis of portfolio company data.
This information can then be used to enhance the valuations and reporting process.
Justin Partington, group head of funds and asset managers at fund services group IQ-EQ, adds that tech-enablement and data warehousing is also enabling managers to use increasingly sophisticated predictive analytics to run scenario analysis.
“Rather than just getting the board pack every month and seeing how a portfolio company has done, a GP can tell right away that a sales pipeline needs to be 30 percent bigger than it currently is today in order to achieve budget,” he says.
This is particularly powerful in the current environment, as it provides deal teams and LPs with deeper and earlier insight into portfolio exposures to macro trends such as rising interest rates or cost-of-living pressures.
“What is the interest rate coverage across the portfolio?” Partington asks. “At what point do interest rates put an asset into distress? How many days does it take a portfolio company to convert a lead into revenue? Does the current number of leads support the sales required to meet budget? Data analytics makes portfolio company management more proactive, more data-driven and less anecdotal.”
This capability also reinforces the importance of data-led insight when it comes to valuations.
Data lakes, for example, can overlay third-party valuation data from private markets data research consultancies to compare the multiples they have paid for assets in particular countries and sectors to other deals. Meanwhile, portfolio company earnings and growth can be benchmarked against those of similar companies.
From back office to front office
While the main focus of tech-enablement in private equity has been on back-office efficiency and output, forward-thinking GPs are also developing technology that is driving differentiation and competitive advantage in front-office functions other than valuations, such as deal origination, pipeline management and due diligence.
EQT, for example, has invested significantly in building Motherbrain, a proprietary technology platform that handles multiple workflows, ranging from market analysis and monitoring to recruitment and benchmarking. The technology also has powerful deal origination and pipeline applications, and has fully sourced nine deals for the firm’s venture business, EQT Ventures.
The Motherbrain project was initiated in 2016 by EQT Ventures to support a data-driven investment strategy and has now been rolled out for other investment strategies at EQT.
“As the complexity of obligations that GPs owe their LPs has increased, GPs have moved to maximise efficiency through tech-enablement”
Bridget Deiters
Ontra
Alexandra Lutz, who heads up Motherbrain, says the platform captures every EQT deal, every advisory and management team relationship, and every interaction the firm has had with a company, building a single source of institutional knowledge and insight that would otherwise be squirrelled away in personal notes, inboxes, spreadsheets and CRMs.
An EQT dealmaker who has encountered a company of interest can instantly see if any colleagues know the business, have met the management team, or worked with any specialised advisers who know the business or sector, Lutz says.
Motherbrain also filters large swathes of company data to identify deal targets that meet investment criteria, builds detailed financial profiles of companies that can be tracked over time, assesses the strength of management teams using ratings from public sources like Glassdoor, and benchmarks these businesses relative to their peers.
“Centralising the firm’s collective knowledge and making this accessible to all sector teams within each business line is immensely powerful. It makes deal teams smarter and faster,” Lutz says. “The platform expedites an origination process that would have taken up significant amounts of deal team resource and time.”
Triton has also developed a custom-built system that supports deal teams with company and market data that help to filter targets.
“Our internally developed technology provides investment professionals with a single unified interface that combines a host of financial and non-financial data, M&A activity, public equity data and market statistics,” Triton’s head of technology Lyndon Arnold says. “Once potential targets have been identified, technology helps in the screening process with an investment assessment tool, which helps capture deal characteristics, investment themes and value creation potential in a structured way.”
Human insight is still critical
As rapidly as tech-enablement in private equity operations has accelerated since the first pandemic lockdowns, and as important as data and technology have become for setting valuations and reporting to LPs, there is a general consensus that artificial intelligence and data analytics are still a long way from disrupting the prominence of human insight and personal interaction in the industry.
LPs do not want to engage with chatbots when making queries, and GPs still have an important role to play in explaining and interpreting financials and data to investors. Managers are unlikely to give LPs full and unfettered access to data warehouses any time soon.
When it comes to the implementation of technologies driven by AI, machine learning and natural language processing, IQ-EQ’s Partington says that the most direct applications at present are primarily to replace simple, manual tasks like data entry document processing.
For EQT’s Lutz, the core objective of a tool like Motherbrain is to support the firm’s deal teams, with smart dealmakers crucial for its effectiveness. “A key focus is for Motherbrain to produce data that is useful to deal teams and provides surface signals for teams to follow up on,” Lutz says. “This is an ongoing process. We are constantly looking at what is useful and what is not, and training the system to refine the information it collates based on human interaction with the data.”
Arnold adds that while AI tools can help to identify potential targets faster, current technology still lacks the capability to cover all aspects of deal origination with sufficient accuracy in practice.
“We believe the optimal environment for critical decision making, which is key to the success of all deals, is achieved when human knowledge, experience and expertise is augmented with accurate, relevant and actionable data insights provided through technology,” Arnold says.
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