Episode: Leading AI Transformation Across Private Markets

Prasanna Vaidya (0:05)

Welcome to GydeBites, I'm Prasanna, your host. Private markets are built on complex workflows, huge volumes of information and a lot of manual work. And that's where AI starts to make a difference, actually. But the question isn't simply where AI can be used, it's where AI can create a lot of value, what leaders should prioritize, how it can scale it across the business and so on.

So today, we are exploring what it takes to lead AI transformation across private markets. I am joined today by Saurabh Mukherjee. He is a global head of AI strategy and Enablement for Private Funds at Epics Group. Saurabh has over 20 years of experience in alternative investments and fund operations. He focuses on how AI can drive greater efficiencies across private markets, what his experience spans across private equity, private credit, real estate, operational due diligence and investor relations. Saurabh, welcome to GydeBites. It's so great to have you here.

Saurabh Mukherjee (1:07)

Thank you. I'm happy to be here.

Prasanna Vaidya (1:09)

All right. So, Saurabh, let's get it going right away. Where do you think AI is creating impact in private markets as of today?

Saurabh Mukherjee (1:17)

Let's see. AI is able to generate an exceptional level of efficiency in terms of turnaround times and quality of deliverables. The capabilities that AI bring to the table with practical use cases in private markets are data extraction, transformation, and data validation.

I'm leaving out data integration. I'll come to that. So to understand this a little more simplistically, let's divide private markets into three primary functions, right? We say there's a front office that's responsible for investor outreach and fundraising, investment selection, and due diligence process, right? Now, AI is transforming the due diligence of portfolio companies by comparing company performances and derive insights efficiently. So it's reading the scores of documents and finding out how the companies have performed and maintaining or seamlessly giving their KPIs, right? Moving on to middle office, which is basically compliance and trading and settlement, you know. Now, that's where integration comes into play, because these are proprietary trading platforms or data records are there. So that's where integration is not allowed. Right. Then the biggest data heavy function in the case of AI is accounting and investor reporting. That's the back office and financial reporting. I would say AI is making the back office operations more efficient, with noticeable improvements in the quality of the deliverables. So in a sense, data extraction from memorandums, from limited partnership agreements, are seamlessly done. The data is then turned into consumable formats, like reconciliation reports, exceptions, and data is validated against source of truth, example, accounting entries.

This is assuring that 100% review is done by the agentic bots. Visibly what humans used to do as a sample check back in the day when we started our careers.

So this is where I see AI is making a meaningful impact in private markets.

Prasanna Vaidya (3:27)

Sure. So you touched a little bit about on the workflows, but can you help our audience understand which workflows are ready for this kind of AI transformation within private markets?

Saurabh Mukherjee (3:41)

Sure. So in a PE life cycle, the most common use cases are AI-powered deal sourcing and target screening, right? Then automated due diligence and document extraction. When a private market manager does due diligence on investing companies, they have to go through scores of documents. They do the ODD, operational due diligence, then investment due diligence. I used to take due diligence analyst 15 days to one month time, and now it's seamlessly done in a matter of not even hours, it's minutes.

Now, ongoing monitoring of portfolio companies, like I said, on the fund operation side, I see following workflows are being increasingly automated, like NAV review, Net Asset Value reviews and checks, in a cash and position reconciliation, that is asset book of record, investment book of record, partner's capital validation, variance analysis, how the NAV has moved from one period to another, supporting document extraction and summarization, investor reporting preparation, quarter on quarter. This is like a fund might have hundreds of investment puts pressure around month end to send out investor reports. And this, because it goes to the end investor, have to do a lot of reviews. And this is taking time off the analysts and the reviewers. And AI is like, okay, these are the two comparable tables. This is the working file. This is your output. And of course, financial statement review. Cintiq fund accounting controls.

These are controls and review part. This is an important part of AI transforming the workflows. Data migration, data validation, onboarding, KYC of investors. Wherever the bots has to read through documents and compare to values, then it does a pretty good job and increases efficiency.