Q&A: The Impact of big data and robot automation in Private Equity

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olivier coekelbergs january 16

Q&A: The Impact of big data and robot automation in Private Equity

by Olivier Coekelbergs, Private Equity practice leader, EY Luxembourg | As featured in Real Deals (November 2018)

EY’s Olivier Coekelbergs discusses the impact big data and robotic process automation is having on private equity and how the asset class can best harness its potential.

What does big data mean for private equity and why has it become a focus area?

Data management has always been a priority for private equity firms, but  the focus on data has intensified as demand for more transparency from regulators, investors and other stakeholders has increased.

Furthermore, many processes, such as those related to portfolio companies reporting, have been excel-manual based for too long. That simply was not efficient or effective enough. The pressure on finance functions in private equity firms to manage their costs is a major call for automated solutions to capture data and produce accurate reporting on a timely basis.  It is crucial to have the proper tools  and processes on hand for the maximum benefit. We see many RegTech startups emerging with new intelligent solutions that support the entire private equity funds value chain.

What are the opportunities and risks that big data poses for private equity?

Data presents a great opportunity for private equity. Private equity firms have a long history of investment and  divestment so there is a huge amount  of data available to them that they can leverage.

Multiple analysis, such as trend analyses and assessments for future investment, can be run to satisfy and influence decision making on many levels. Applying an algorithm to a dataset can support an investment or divestment case, identify risks and track deal targets.

With the right and focussed usage of big data, fundamentals of the private equity business can be supported and enhanced. The job of the investment professional will become even more valuable as more information will be available to create value.

I think the big risk to the industry from big data is that it is used in isolation of a solid investment/divestment process. Reliance on big data only without further segmentation appropriate to the business and proper incorporation of the human factor can lead to bad decisions.

What are some of the specific ways that private equity firms can implement big data and digital in their day-to-day business? 

Data from portfolio companies and historic investments can help to identify new investment opportunities. In general digital is also becoming an increasingly important part of the due diligence process, providing a basis to confirm or challenge assessments of a deal target. Data can provide scenario analysis too. Managers and regulators have placed a high priority on strengthening the asset class’s risk management processes.

Certain data analysis tools can help all stakeholders to run stress tests and be more predicative about the kinds of scenarios that portfolio companies could face in the future.

What about robot process automation? What does this mean for private equity ?

The biggest impact of robot process automation (RPA) will be on the routine tasks that are important but time consuming. We have already seen the application of RPA to processing know your client (KYC) and anti-money laundering (AML) documentation.

What we have also observed, is how RPA can help firms to meet investor reporting requirements. The reporting requested by LPs is becoming more bespoke and RPA can definitely help to automate the process of feeding different reporting requirements. RPA can also help to keep costs down for both firms and portfolio companies. Reporting requirements for private equity firms are increasing. Rather than hire in more headcount to do that work, you can set up an RPA process, ensure it is well maintained, and keep budgets on track.

Where does big data and robotics leave human intervention?

It’s a good question and it speaks to some of the themes we have been discussing. If you look at where private equity is today, it is receiving more capital from investors, which is great news for the industry, but that comes with greater reporting requirements and regulation. Firms have to do more to satisfy higher expectations.
Private equity is also operating in a very competitive environment.  Valuations are high and when you are paying big multiples the margins between a good decision and a bad one are narrow.

Big data and RPA can help to manage these two dynamics. RPA can manage the increasing reporting needs by automating repetitive tasks to be more efficient while reducing the error rate of humans. This will free up resource for higher value work. Data helps decision-making on deals by providing more reference points and accuracy of information, like never before. As data and RPA become more
embedded across private equity it will change the way private equity operates in general.

People will shift their work on more value added tasks and can provide more benefits along the process. Big data and digital technologies will form the foundation of future success. I believe the underlying business model of private equity will remain much the same, but the way it is operated will change. Firms that are unable to adapt will be at a large disadvantage.

If a firm is exploring digital from scratch, where does that process begin?

It is crucial not to see technology in isolation, as something that you need to develop because everyone else is doing it. The starting point should be an assessment of what a firm’s needs are, where it is encountering more demands and then how it needs to adapt its operations. The next step is to build a digital agenda that addresses how technology can meet the organisation’s requirements. The selection of the appropriate technology will follow. Where digital goes wrong is where it is seen as a quick fix to one task without looking at the wider implications. When implemented, digital needs to be at the centre of building new operating models and revising old ones.