Future special: How automation will affect operations

AI and other automation technologies will offer plenty to the operational side of private equity firms, but these cutting-edge solutions will still need in-house expertise to live up to their potential

These days, most predictions about technology’s impact on working life boil down to two certainties: greater efficiencies and fewer jobs. Some tech gurus caution that half of the world’s jobs will vanish due to AI in the coming decades, and that entire industries will shrug off all but a handful of human employees.

In the near term, the operational staff at private funds can breathe easy. Digitisation and automation technologies are busy taking the most tedious tasks off their desk, and in the next five years, AI will only allow their work to be done more efficiently and accurately. Over the next few years, CFOs may actually need to add staff to cope with major IT projects. The next wave of the tech revolution will require experts that can translate the cutting-edge solutions into systems that improve the firm’s investment business.

And rest assured, that next wave is coming. “The disruption on the finance function is inevitable,” says Jason Bingham, the managing director of product development for the fund administrator Sanne. “GPs and LPs have an incredible appetite for data, and the faster and the more accurate finance teams can deliver that, the better.”

That wave may already have begun to hit the industry. “Advances in technology mean that there are software solutions today that allow us to automate the more mundane and routine processes, create efficiencies as well as make significant improvements in more value-add items such as budgeting, forecasting and data analytics,” says Lance Taylor, the CFO of HGGC.

For the last three years, Harbourvest’s CFO Karin Lagerlund has been working on upgrading her team’s processes. “We looked at where we had cumbersome manual processes, or a lot of Excel spreadsheets, and began prioritising how to digitise and use technology to streamline those activities.”

Digitise, digitise, digitise

Bingham says that digitisation is now as much a necessity for the industry as a choice, with early adopters driving the innovation of their technology platforms. “We digitised our journal entries and created a system that pulls the back-up data automatically and stores it for us in a cloud solution,” says Lagerlund.

That sounds wonderful, but digitisation can be labour intensive, as some service providers note that the initial cost in time and resources can make the legacy processes seem more cost-effective and simply easier than the upgrade.

It helps that new tools, such as optical character recognition technologies, allow firms to scan and populate data easily, instead of relying on manual inputs which not only take time, but are susceptible to human error. For obvious reasons, OCRs are a priority to CFOs with a backlog of legacy data. But they remain a relatively young technology.

Even without OCRs, the time it takes to digitise data and adopt new platforms can pay off in the long run. “The ability to turn this data into usable information and the speed in which you can do that is transformational,” says Richard Butler, the COO of ESO Capital.

“We’ve recently implemented a powerful platform called Ipreo, enabling us to better compare our portfolio company KPIs, even for businesses across different industries.” Butler explains that just three years ago, he’d need a team of people to manage this information, and now he can do so at the touch of a button.

Automation will pay a huge part in the next generation of improvements. EisnerAmper’s Jay Weinstein, managing partner of markets and industries, explains how his firm is investing the time and allocating capital into producing ‘bots’ that are capable of automating major processes on the operational side of private equity firms.

As an example, a fund may have month-end reporting for thousands of investors that they are currently recording on Excel spreadsheets. These spreadsheets include numerous calculations, which they spend hours producing, whereas a bot can automate the spreadsheet, and eliminate the manual inputs and reconciliations to validate those investor calculations.

“Those tedious reconciliations can take two or three days to complete,” says Weinstein. “And with the bot, the staff is freed up to handle more interesting and productive work, while also enhancing accuracy by limiting input errors.” These bots can be implemented quickly, providing an immediate benefit to the client. Weinstein says EisnerAmper has a dedicated bot lab, devoted to building bots to automate these kinds of manual processes.

These technologies allow firms to migrate to an exception-based review and reporting process, so that staff are not looking at every number or calculation, only those that represent aberrations, say when a data point communicates a loss, when the market for a particular portfolio company is booming.

Automation may be a priority now, but it’s been part of the industry’s tech strategy for some time. “We’re starting to see more of a shift of focus from automating routine processes towards technology that creates operational leverage to produce big information advantages from machine learning,” says Bingham.

The limits of AI’s IQ

Of course, given the manual process of LP communications, AI could play a role there, but that might not be a question of technology.  “Unless the industry progresses to a set of agreed-upon standard reporting templates at a much faster pace than today, there’s only so much we can automate in reporting,” says Taylor.

And the trick about machine learning is that someone needs to take the time to teach that system. “Watson works by being taught your unique experiences and preferences,” says Weinstein. “And if you teach Watson incorrectly, it will continue to learn incorrectly, so you need to keep a tight rein on who’s educating the machine.”

AI also requires an enormous amount of data to learn. “To be effective, machine learning needs millions, if not billions, of data points,” says Bingham. “And many firms are still in the process of structuring all their data into a ‘single source of truth’. Much of the ‘big data’ captured in the past five years does not as yet have a long enough time series to be properly validated and may be more commonly used by firms five years from now.”

That means that CFOs may not have the luxury of relying on legacy practices for much longer. And that may require adding more expertise, instead of cutting any existing staff. “If anything, the pressure is on expanding my team to adequately utilise the sophisticated tools we now have,” says Butler.

A new breed of IT manager

In response, some firms are bringing aboard a special class of IT project manager to make the most of today’s solutions. These staffers have skills in both IT and project management, and will lead a given initiative. They collaborate with business users and the IT team to decide if a project could be handled by a consultant or in-house, and whether a solution is available off the shelf or would need to be built from scratch. And then they see the project through to completion.

For firms with less tech in their DNA, the revolution may lead to their first CTO, or a different expert altogether. “Data management is only going get more critical and complex,” says Taylor. “So, there may be the need for a kind of data scientist, or data expert, who can think about how best to organise and manipulate data for value.”

A recurring theme among CFOs and service providers is that this next wave of technology is about maximising what current staff can do. The asset class tends to staff leanly as a rule and every firm aims to do more with less.

“We are looking for how many hours automation can save, not just now but during future growth as well,” says Lagerlund. “So, we fully expect to be able to increase efficiency, without necessarily increasing our accounting staff.”

And firms don’t just want to improve efficiency. “We can do more, and have to do more, with the team we have – it’s not about cutting headcount,” says Butler. “The biggest weapon in my armoury is information. The more information I have and the sooner I have it, the sooner I can get it in a usable format to act upon it. Better understanding the numbers means we can better influence the investment decision-making process and improve returns.”

Not quite HAL 9000 (yet)

Today’s AI developments are easily misunderstood, often conflated with science fiction terrors. The reality is, machine learning at the moment is a lot like digitisation a few years ago. It will take an enormous amount of time and expertise today to get the most out its capabilities.

But the initial investment today might prove worth it. For one, AI’s ability to read, translate and determine sentiment from documents or human speech, can revolutionise contract analysis. EisnerAmper uses IBM’s Watson for its AI solutions: a contract which could take a human staffer two to three hours to review may only take Watson a few seconds.

“It can ingest the contract and then produce a summary of important issues, such as revenue recognition trigger points, along with any other issues that auditors and internal accountants identify,” says EisnerAmper’s Jay Weinstein. He also suggests that this type of contract analysis could work wonders with company pension plan documentation; those 400-page doorstops. AI can now be trained to look for key matters in those documents.

Bingham notes that the application of machine learning data analytics and its predictive capabilities will play a substantial role in other areas including valuation techniques, fraud detection and identifying suspicious activity across bank accounts or any series of data.

“For the finance function specifically, machine learning through software functionality will automate bookkeeping transactions as they happen across bank accounts,” says Bingham. This means recognising transactions, even new transactions, posting them, and with machine learning, classifying that data and pulling it through to general ledgers.