How transforming diligence can drive the future of private equity

Speaker A: Foreign.

Speaker B: Welcome to Money Multiple. Today we examine how due diligence is evolving in a complex and volatile deal making environment. Validating a value creation thesis now requires deeper analysis, rigorous stress testing of assumptions and consideration of emerging risks such as cybersecurity, climate impact and regulatory changes. And yet, in a competitive market, bidders can't slow down. They need to move fast with agility and efficiency while still balancing risk and return. The game is changing and technology is leading the charge. AI and advanced analytics are uh, transforming due diligence, giving us sharper insights into risk and unlocking new ways to create value. While buyers aim to refine their strategies to win deals and create value, we know that preparations must begin much earlier. We will explore how sellers can ready themselves to achieve optimal outcomes in today's environment. Joining me to share their deep insights and experience, we have Geoffin, uh, George, uh, who is EY's ASEAN, uh, transactions and corporate finance leader, and Wykit Lai Ey, ASEAN private equity value creation leader Chaufin Waikit. Welcome to Money Multiple.

Speaker A: Thanks for having us.

Speaker C: Thanks for having us.

Speaker B: So Jophin, let me start with you. We talked about complexity in the environment, um, and um, the velocity of deals getting done in this very complex, uh, environment. What are some of the key strategies that your clients are using to navigate these complexities? And maybe you can help unpack some of these complexities for us.

Speaker C: Thanks, Luke. Um, so if you think about what is happening in the market, if you look at the state of the market, um, if you look at the last 12 months, we feel that there are a lot of people going after a few deals. If you look at the landscape overall, the macro factors, businesses are being disrupted, AI is playing a role in the disruption in a positive way and a negative way, depending on which side of the, uh, case you're in. Um, there are few deals in the market that are attractive, so there's a lot of prep work being done upfront, um, a lot of time being spent on deals. And from a buyer perspective, they're looking at folks that can add value. And from a seller, um, perspective, you are underwriting your value thesis well before the deal takes place. Now what does this all mean? It's a very complex environment. Deals are, uh, happening, but there are very few deals to go after. So you've got to think about how you do things differently. So getting insights early is very important. How do you do that? Through technology. Technology with AI, as we see, there is a lot of information and a lot of data that can be processed quite quickly. If you also think about, um, how traditionally people have done deals, it's across different verticals. Now up front people are doing a lot more diligence. They're thinking about not just the typical finance and tax, but they're looking at various others, including technology, um, cyber, operational, commercial. Now more and more what we're seeing is this is all being, collapsing into one because ultimately people are looking at what does it mean for me to underwrite the thesis. So technology plays a big part. Advisors working together, coming up and, and supporting their client on what are the key risks, but also the key opportunities up front in the deal. So the way diligence are being done right now are being obviously different and changing.

Speaker B: So, so actually that's uh, a lot of moving parts, right, because you have uh, uh, various teams coming, coming together. Uh, there's a lot of coordination, I guess between the teams and there's a little bit of sequencing. How do, how do people actually sequence all of this? And how much time do you get, uh, uh, before a bid to be

Speaker C: able to do all of this in a competitive process? You don't get two things you don't get. You don't have the luxury of time and you don't have a luxury of data. So the question, you're right, you have to sequence around what are the key questions? A lot of times people come to us and says, what are the key questions we need to answer? And how do we unlock that answer quite quickly? So this is where the ability to bring in different tools and we invest in quite a lot of tools as a firm. Uh, we do have quite a lot of analytical tools as well as AI tools that we're investing in to help with that process, either through benchmarking or being able to take lots of data and analyze it quite quickly. The other thing to think about is how do you bring experts into the network? Because experts, right, are leveraging the market intelligence and bringing that to a deal because you want to bring quick insights, um, particularly in that short environment and sometimes deals are faced. Now more and more we're seeing gate one, gate two, gate three, and how do you ensure that as you go through each of these gates, you are continuing to support, um, your deal thesis that this is the attractive deal to do, particularly given the multiples in these markets are currently quite high.

Speaker B: And maybe voikit, from your standpoint, right, you do a lot of work, uh, on the commercial side and on the op side. I mean, how has that evolved? Uh, uh, has that integrated a lot more?

Speaker A: Yeah, I mean um, what Jofin just talked about, when the value creation thesis now has to hit the ground running and time is a constraint just to realize those gains, that's something that we are seeing increasingly. But the value creation part is actually not new. A lot of times as part of the decision of go no go, the value creation is baked in there. What is different now is that the value realization plan to a much deeper level of planning, uh, it's expected, right? So that the value creation realization is really um, off the ground from, from day one. So let me give you an example. Um, there was a deal on the industrials company, right? M. And then as, as part of the revenue trajectory, um, that's, that's this aggressive growth that's expected, right? So P times Q, where is that coming from? And you know, after some analysis it comes down to the Q part, right? So is that coming from new clients or existing clients? After some analysis on share of wallet we said okay, it's going to be new clients. But it doesn't just get stops there. We should get down to analyzing the teams, the team structure to see hey, traditionally there have been farmers, but if you're going out to hunt new customers, the capabilities are different. So it's not just about people but the process. Whether or not there's a multi skilled team that goes out to realize this plan, uh, are incentives aligned for them to chase after this plan? Are the processes and systems in place to enable them them. So it's no longer just a value creation thesis but rather a value realization plan that is, uh, that's, that's expected and set in place um, as part of the commercial and operational diligence work. Uh, pre. Deal.

Speaker B: Okay, so, so the, the how you're going to make money from the asset needs to be very clearly visible upfront and baked into your, yeah, your deal evaluation. Uh, so, so Joffin, you talked about uh, uh, having uh, analytics and AI and those other types of assistants. It looks like these assistants will be part of the diligence team. You may have to add a new associate on the team, uh, which is the AI assistant. Maybe help us understand um, uh, how this is deployed. What are some of the tasks uh, that AI helps with and what's important when it comes to incorporating this into

Speaker C: the uh, diligence process? Yeah, good question Luke. Um, so more and more we are looking at lots of data and where AI and particularly some of the analytical tools. So we have two different types of tools. One are analytics and the other one's AI tools. They have been Able to crunch a lot of this data. So from an efficiency standpoint it's been great. Um, and I myself as played around with some of these tools just to see what the value is. As you're right. Does it replace your um, associate in your team? Now what is interesting is it does a great job. Um, if you find the right tools, it tells you the what, it struggles to tell you the why. Because if you think about the why, you need to bring not just data, experience, knowledge of the sector, uh, knowledge of similar businesses, country specific information now that comes from experience. Now can AI replace this potentially over time? I mean I can see, you know, the more you get AI to learn the way a diligence mindset thinks, I can see this being transformed. Right, right. Um, but for now I think they're great in terms of providing the analysis. I mean we do, we use a lot of you know, natural language learning, uh, models, um, and we continue to invest in this and we have a few platforms. One called um, Diligence Digital Diligence Assistant, we call it dda. And basically what that can do is I can take a lot of information that comes from a VDR I can put in the system and it'll generate a high level rapid insights. Now is that a diligence report? Probably not. But does it give me a very quick high level summary of some of the key issues? Yes, um, we also use tools which allows you to essentially like a Google search with documents, you ask them questions and it looks through the data that you've uploaded and it comes back to with you with specific answers. So using tools like this and we are experimenting ourselves to make sure that we are leveraging the best technology that's there. Um, ultimately it comes down to is can we get insights to our clients as fast as possible. And I think that's what we're trying to achieve here.

Speaker B: So all the way from structuring the data to analyzing the data to coming up with first level analysis, validating hypothesis and then what you're saying is all the way to potentially writing first findings which are then reviewed.

Speaker C: That's, that's, that's where we want to go. I think in different stages AI does a good job particularly with the analysis, but we are experimenting with tools that can do exactly some of that. Right. Can it even get you to some initial writing as you'd expect in report? So we are seeing this and this will evolve. Um, you know as we started doing diligence 20 odd years ago it was very different to what diligence has done today. And if I look through in the next 20, 25 years, it'll be very different how we do diligence. And I think the more we can leverage and utilize. And I think, look, one of the things is always going to be data privacy and data risk because we are handling a lot of confidential data. So ensuring that it's in a closed loop, I mean that's something that we pride ourselves. Right. And we try to make sure our teams have got, they can't use, um, outside sources. You know, we protect the data because that's the risks as well as the opportunities we have. Right. And how do you manage that is quite important.

Speaker B: So, so the data is in a walled garden. Uh, and uh, within that the AI basically can play with the data.

Speaker C: Yes. And look, and the, and the insights that AI is coming up with. Right. And the more you teach it, the better it gets, you know, and what we're now trying to. Because there is a big cultural change as well. Um, if you think about teams are so used to doing diligence a certain way now you have to retrain your own teams on how they would leverage some of this. Um, but what is still important is that experience of a diligence professional, the knowledge of how to do diligence, how to look at what the right findings are, um, that still is important. That doesn't go away. Um, you know, over time that might depending on how the AI develops.

Speaker B: But at the moment the teams have more time. So the teams, uh, can focus more on, as you said, the why.

Speaker C: Yes.

Speaker B: Uh, but as the, the crunching is done by the AI. Uh, but maybe also if you can spend a bit of time on what are some of the issues that you've seen, what are some of the guardrails, so to speak, that you've got to put in place as you do this.

Speaker C: Look, I think we talked about some of the guardrails, particularly around data privacy and risk. Um, the other guardrail is around, you know, it's only as good as the type of questions sometimes that you ask and making sure that you're asking the right questions to be able to get the right level of insights. And sometimes the questions are, you have to ask multi layered questions to allow it to give you an answer that fits for what your client is looking for. Uh, the AI is very good at pulling data from structured and unstructured sources and trying to analyze that, but it does need you to make sure you're asking the right questions to be able to give you the right feedback. Um, and again, as you said, we have professionals that are building systems in place and as we ask more and more questions, it learns and hopefully over time it brings some of that sector thinking, asset type thinking into some of the questions to make it even a lot more uh, richer, particularly in the responses that you get.

Speaker B: Well understood. And Vaikit, you had talked about uh, essentially having a very clear understanding of the value creation thesis very early on in the process. Right. Probably at a high level pre bid and certainly by the time you've submitted your bid and your signing docs that uh, has to be very well baked. How is uh, uh, I guess tech and AI helping you in this process?

Speaker A: I mean there are two exam questions that the clients always ask, which is, uh, how is AI going to help uplift EBITDA for this deal, for this asset? Um, and secondly, are there actually curveballs and disruption that we should anticipate? Um, so likewise we have a tool that answers half of the question. The tool is a value accelerator. Uh, and what that does is that it actually scrapes across the universe of all PoCs that have been done on AI and tech and the expected uh, uplift that we should get from it. But why, I say half the question is because, uh, it gives that range and on the higher bar of the range versus the lower bar of the range, why, why do some companies actually fare better and some companies are worse off and that's the part that AI struggles more. Because that's not a what question anymore, but rather that's a why question. Right. Um, and then we need to get into um, the POCs behind those numbers to figure out is it about the organizational maturity to uh, have embrace that AI transformation? Or is it about people capabilities? Or is it about AI governance, data cleanliness? And that's the part that get a bit more um, dirty. Um, but that's the exciting part because that means that teams actually still need to get on the ground to figure out uh, what's the uplift that's expected from AI. And there's no clear answer from the AI tool itself. Um, there's another part around when we think about um, deals and assets these days, the question that's being asked is also how resilient is it right in the volatile macro climate? And um, AI helps because there are many more scenarios you can actually start modeling and it's no longer just about the, the asset scenario, but rather the upstream and downstream implications as well. You get into the bond, the bill of material and you can actually analyze the different key suppliers and Figure out, you know, in this volatile world of terrorists, um, how they would be expected to uh, to shift their foot, um, and whether it would affect the asset. Likewise, uh, some of the key customers as well. That level of analysis on the upstream and downstream basis with AI can uh, be a lot more accelerated as part of the diligence process.

Speaker B: Does it need to be the case that the data environment needs to be rich enough? So for example there are two constraints, right? One is if uh, you have limited access to the target's data, uh, or the data is not very structured because they don't have the right systems or uh, uh, essentially the data could be a combination of manual data and electronic data sitting across different geos, locations, systems etc. How do you deal with that? Does that make all of it less effective or is there a way to structure the data, ah, to the point where you can do all of this?

Speaker C: So with that um, we use some third party tools which are very good at taking structured and unstructured data, particularly financial and non financial. So if you think about these data are coming from different systems and there's a level of cleaning need to be done. And when I talk about cleaning it's about ensuring that any anomalies are picked up and taken out or adjusted for, making sure that this data somehow tracks back to some sort of financial system so it can be relied upon to analyze. What we then are able to do is then take this data and then put into visualization. And the visualization and again this takes a little bit of knowledge about sector and the industry to sort of say what other type of analysis you do, what sort of metrics would you do and how would you present that. Now what we've then done is because then it allows you to then take all that rich data and again it's structured, unstructured and say the so what? Because essentially we're trying to answer the so what question. What does this mean for the deal? What does it mean about the business we're looking at and what does it mean when you're thinking about underwriting the value creation case? Now what we've then been able to take that data is then host it. So no longer is it just something that we would look at, we would share with our clients so they have the same level of visualization that our teams have and allows them to what I say, play around with the data. So you can change some of the metrics or you can change some of the um, levers and they'll give you different analysis. So now we're putting some of that, that we have into the power of our clients because ultimately allows us them to get a feel of what is happening. But again, trying to um, understand and um, unpick what is really happening in this business and where do we see the value drivers. And I think ultimately, you know, essentially in any deal, to Wicket's point, you're trying to underwrite an ebitda, uh, you're hoping for EBITDA growth, um, going forward. And this level of granularity allows you to see other areas, other missed opportunities, other areas that um, and part of it is also validation of what the sellers, um, and what the management team is telling you. Right. And this has been quite powerful.

Speaker B: So that's interesting. So basically then it's a very collaborative process. It's no more wait for two weeks and I'll deliver to you a report or wait for four weeks and I'll deliver to you a report. Uh, it's an ongoing interactive, uh, insights based conversation and uh, you can apply different brains to the same set of information and visualize it from different angles. That's very interesting. Maybe I'll shift gear a little bit. Uh, the other topic is complexity. Uh, if you wind back to the past, there should be three or four streams of diligence. We have our financial, we have the tax, we have the legal, a uh, little bit of commercial. Uh, has that changed? I mean what are some, uh, some of the new risks that have come into play? Uh, what are some of the new factors that people need to consider as they undertake diligence? How do they sequence at all? Uh, maybe either of you can comment on that.

Speaker C: Technology is coming up quite a lot now. Typically in the past it used to be it diligence, now it's gone past that. It's around. What does that technology look like? Is it fit for purpose? Can we grow using this technology? And more and more the questions around cyber, because pretty much most companies are connected one way or the other, um, online and is this subject to cyber risks, cyber threats, These sort of questions are coming in. So there's a lot of value or directly or indirectly that is placed on the technology. You know, the back end technology, the front end technology. So there's a lot of time and effort now that we're seeing clients spend on that, um, pretty much in all deals, particularly for uh, certain types of funds and large private equities. This is definitely done. Um, the other area that has been obviously mixed uh, is being esg. You know, I think a couple of years ago we saw a lot of people um, look at ESG and that was something, a hot topic those days. I still think it's very important now um, more not just the environmental but the social and governance part. Because particularly in the world we live in, this is becoming an area that a lot of um, funds especially have been focused on um, because they know that they have a fiduciary duty post transaction because now they're the owners and they're on the board and particularly on the social and governance. What can be done, how can be fixed, um, to protect the overall risk of the business. I don't know why.

Speaker A: Just to double click on the technology part and it's also linked to the AI. Um, exam question on where the uh, uplift is. Um, uh clients are getting more sophisticated now to recognize that when we talk about AI it's gone past use cases and just POCs to prove whether or not um, it can work, but rather how do they deploy at scale and uh, at an enterprise level so that you really unlock the full potential of that AI use case. And what we have come to realize is that there are a lot of foundational things that need to be in place such as uh, say a master data governance or whether you have that um, data lake in the first place, whether the systems actually enable you to fully tap on AI. So when you think about the diligence work, it's no longer just proving the AI use case but looking at the foundational pieces of the organization to realize that when they get in um, there is very little lead time to then unlock the full potential of AI to be deployed at scale.

Speaker B: Well that's interesting. And the other dimension to this is uh, the sellers because it uh, seems like the sellers can help this process a lot by being better prepared. They can help the process, they can help themselves as well because they get better value out of it. So maybe let's pivot a little bit to talk about the sell side and, and how should the seller be organizing themselves to be very well prepared to take an asset to market?

Speaker C: Yeah, no, I think, look, that's one conversation that we have with a lot of buyers, right? Because the moment you buy a business, I mean you should already start thinking about your, who your sellers are, who you're going to sell this to and what does that business look like? Um, we always say have a five year plan, make sure that's what you're underwriting. Now part of that plan would be co created during the deal and part of that would be finalized post deal with More information. But it's important to ensure that you have that plan. You're sticking to that plan.

Speaker B: Right?

Speaker C: And again things change. That's fine. And you can amend it as needed. Um, having the right management team, sometimes you might need to replace the management team, um, parts of it, all of it. We've seen combinations, um, and also incentivizing the management team. This is super important. And there are lots of different structures in place, right? I mean some use esop, some use sweat equity and there are different ways of setting it up, structuring it because ultimately the management team is going to be very important, um, probably the most important thing to deliver those numbers. Now one of the things that we've also seen is when you, particularly when you're going to sell from a buyer perspective, a seller always need to think about a buyer perspective. A buyer want to make sure the next 12 to 18 months forecast plan is locked in with minimal execution risks. Now that means as a seller you need to way up front make sure that you are uh, putting the building blocks in place so that the buyer can see that. So this is not from a, putting it in a PowerPoint presentation or deck prior to sale. Right. This is making sure that you have sowed the seeds 12, 18 months before. Because some of these take time, um, from implementation and execution to actually getting the benefits. And that's super important because the more you can demonstrate that and the buyer can get comfortable to underwrite that, the, the more they're going to get convinced. Right. Uh, particularly to support the valuation case. I don't know if there's any, that's

Speaker A: a big part of, of exit readiness. Right. When we prime the uh, the asset back to the marketplace and, and there are actually two parts there. One of it is having the visibility of um, the matrix that matter, um, before you take it back to the marketplace. And a lot of times these matrix could be financial matrix. And what we realized a lot of times is that these are lag. By the time you make decisions off these financial matrix, it could be somewhat late. So as part of exit readiness a lot of times we also try to figure out what are the operational matrix that are linked to these financial matrix and how can we track at the level that uh, is at the operational matrix. And decisions can be made more speedily for more clear interventions. Right. Um, so that you give the assurance to your buyers as well that you know, when you make certain interventions of these operational matrix, there's a trickle down effect to the financial matrix. And history actually then shows the Interventions that have happened, the uh, impact to the financial matrix and you can actually then take it out to the marketplace with the assurance to what Jofin is describing, the assurances that's given to the buyers that actually sets up um, your sales process to be much more of a confident conversation with your prospective buyers.

Speaker B: So, so, and as management then is presenting the business to the next buyer, uh, they're presenting it with a lot of conviction because they're probably two years into a five year strategy at that point of time.

Speaker C: And, and you know we've been on many management presentations on a, on a buyer side, right. And it's very easy to tell which management team is convinced and has got conviction behind the plan and which management team is sort of maybe less convinced around the plan.

Speaker B: Right.

Speaker C: And that gives different perceptions, uh, from a bias standpoint. And you're right, if you're working on the plan, you've laid out the foundations, right. You're the one that has got the conviction as a management team that this will result in essentially hopefully beating some of the plans that you've set in your forecast.

Speaker B: I like the term that you use wit exit readiness. I mean today as you look at um, you know, private capital in the market, as they take assets to the market and we know there's a lot of exits that are waiting at the door. Do you see funds generally being very well prepared with all the things that you talked about?

Speaker A: Well, they're well prepared um, from a financial matrix point of view. And there's a plan, definitely a plan in place, how convicted they are, uh, on the planet, um, there's half of it which is posturing, but the other half of it, um, that conviction can't be denied if actually that plan is already in place. It's already um, not just in place but it's already underway. Imagine planning for that plan two years back and kicking it through, uh, two years of performance that have been seen. That conviction isn't just about positioning you actually then sharing a plan that's underway. I think that type of conviction, uh, it's what we were discussing earlier. When you have operational matrix in place, you actually have historical evidence of interventions that have been applied because of certain operational matrix and the cascading effect on financial matrix and saying that hey, you know, three, four years down m after the buyer, the new buyer takes over, this is going to continue on track and that conviction can be replicated.

Speaker C: And just to add to that, right, we are seeing things change. Um, we are seeing particularly investors um, looking to not just Organic but inorganic. And we're seeing um, quite a bit of that take place now. And inorganic strategy could either be to take market share, it could be adjacencies, new geographies, et cetera. And these when it's done correctly and integrated well, we've seen these businesses transform, um, particularly where we've come back as a sell side later on what, what we saw on the, from a buy perspective now from us to a sell, the business has completely changed and that also helps investors, um, get the conviction particularly around the ability for this business to continue to grow, continue to innovate. Um, and that's been a strong proposition. So I think, you know, it's still evolving look in terms of how prepared companies are before exit. Um, but I do think to weaken spoiling, right. 12, 18 months exit readiness. Really thoughtful. Hopefully before that, um, that you're really thinking through the operational aspects of this business and laying those foundations are critical, particularly in an environment where a lot of assets are coming to market. And if, how do you differentiate yourself from the next asset that's in market?

Speaker B: So clearly a lot of changes happened in the way I uh, guess both buyers and sellers approach uh, the business and approach diligence. Jofin Vaykit, this has been a fascinating conversation. Thanks so much for joining us.

Speaker C: Thank you for having us.

Speaker B: Look, you have been listening to Money Multiple. If you liked what you heard, subscribe to our show on Apple podcasts, Spotify or wherever else you listen.