How private equity firms can use and prepare for AI now

Artificial intelligence (AI) is being adopted across virtually all sectors of the global economy, including private financial markets. Amid geopolitical tensions, rising interest rates and other economic headwinds, private equity firms in particular are facing new pressures to create efficiencies and generate returns for their investors. Many firms, however, are not prepared to incorporate AI into their processes and don’t know where to start.

In this interview, Yvan De Laurentis, director of product and pricing at Vistra, discusses how private equity firms are using AI now, and gives advice to fund managers who are considering how they can use AI in the future to promote efficiencies and cut costs. Yvan spoke on these subjects as a panellist at the 12th Annual Private Equity New York Forum in May.

As someone with a service provider background, what insights can you offer on the private market’s adoption of artificial intelligence?

Speaking generally, AI technology has progressed significantly, moving from basic systems that store human knowledge to more sophisticated machine learning and deep learning techniques. These advances enable AI systems to process large volumes of data and uncover meaningful insights.

In some ways, AI is nothing new in private markets. Machine learning has been groundbreaking in the financial services industry for over 10 years, revolutionising how companies operate across various sectors. But the use of AI in finance has been uneven. Banks and institutions dealing with substantial data have successfully adopted AI, investing in and starting numerous projects.

The private equity sector, on the other hand, is just beginning to use AI to optimise operations, remain compliant with regulations and for other reasons. The sector will likely quickly adopt AI solutions on a wide scale. Many firms who previously relied on manual methods like Excel and email for data and communication now recognise the limitations of these methods. And firms are under increased pressure to promote operational efficiencies, reduce costs and achieve improved results for investors. This pressure is driving private equity firms to adopt AI now or get prepared to do so soon.

Can you give examples of AI implementations that have been successful or show promise in private markets?

AI is significantly affecting the financial markets through predictive analytics software and trading models. Traders have been using algorithmic models since the 1970s to identify investments and trade securities. Now, AI is pervasive and being used in call centres, legal agreements, compliance systems, robot-adviser platforms and algorithmic trading.

There’s a lot of discussion within private markets about machine learning transforming the investment process. For example, facial recognition is becoming more prominent in enhancing anti-money laundering and know-your-customer [AML/KYC] controls, ID verifications, and secure approval processes within workflows. It’s not just about iPhones; it’s about streamlining business applications and effectively communicating with investors.

It’s worth emphasizing the increasingly important role AI plays in fulfilling compliance obligations. It can effectively screen transactions across multiple systems, identify potential money launderers, and speed up document analysis, greatly reducing paperwork. To take an example close to home, Vistra’s VFunds platform uses machine learning when collecting investor information for digital fund offerings, dynamically keeping over 300 fields up to date and aligned with current regulations in various countries.

In the corporate secretary realm, AI is automating transcriptions, summarising minutes and suggesting action plans. There are other applications. For example, optical character recognition functionalities enhance loan statements, real estate leases and payslips, adding an extra layer of operational efficiency.

What’s the key to private equity firms using AI to generate operational efficiencies?

Preparing for the use of AI in your organisation is crucial for private equity firms looking to boost efficiency. Many firms already face challenges due to unreliable data systems, which makes AI integration difficult. To make AI work effectively, companies need solid data architecture and operational systems, whether managed in-house or outsourced.

Reliable, experience fund administrators can provide valuable support in this area. They can help implement secure, powerful data systems and standardised processes. Implementing best practices and third-party tech not only paves the way for AI adoption, it creates immediate cost-savings.

Private equity firms shouldn’t lose sight of the fact that these efforts are about more than just task automation — they also build AI awareness within the company. It’s also important to keep in mind that while AI can improve data processing, human judgement remains crucial, especially for ensuring the validity of AI responses.

Any final thoughts on how private equity firms can effectively use AI?

I’d emphasise that firms should get their data and tech platforms optimised to create efficiencies now and take full advantage of AI’s benefits in the future. Those benefits, particularly those related to productivity, will continue to evolve, so it’s best to be prepared for when those more advanced, more tailored AI solutions become available. Given recent exponential gains in AI, that could be sooner than later.

Discussing AI can sometimes seem theoretical or like something that will only affect us in the distant future. But it’s important for fund managers to keep in mind that these efforts we’ve discussed — creating efficiencies and getting ready to hit the ground running as AI solutions become available — can give a real competitive edge. In other words, the steps are practical, and having the right operational and support systems in place is crucial for rapid scaling and capital deployment now.