Why Post-Merger Integrations Fail Without Data Governance - Sandro Venturini of UBS

Welcome everyone to the AI in business podcast. I'm Matthew D'Amelo, editorial director here at Emerge AI Research. Today's guest is Sandro Venturini, executive for UBS Asset Management Switzerland. Sandro joins us on today's show to explore how fragmented fund data creates bottlenecks in cross border launches and mergers and how establishing a single source of truth enables AI to streamline structuring compliance and reporting. Our conversation also covers practical workflow changes such as using AI to anticipate stakeholder concerns and generate draft prospectuses from turn sheets, cutting legal fees, reducing formation costs, and minimizing manual stakeholder coordination for faster fund launches.

Just a quick note for our audience that the views expressed on today's show by Sandro do not reflect those of UBS or its leadership. But first, are you driving AI transformation at your organization, or maybe you're guiding critical decisions on AI investments, strategy, or deployment? If so, the AI in business podcast wants to hear from you. Each year, Emerge AI Research features hundreds of executive thought leaders, everyone from the CIO of Goldman Sachs to the head of AI at Raytheon and AI pioneers like Joshua Bengio. With nearly a million annual listeners, AI in business is the go to destination for enterprise leaders navigating real world AI adoption.

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It runs continuously giving guardrails tracking activity and providing a safety net so teams can scale AI without risking critical operations. This segment is sponsored by Rubrik Agent Cloud. If your business relies on AI agents, you can get exclusive early access to monitor, govern, and rewind their actions at rubrik.com. That's rubrik.com rubrik.com. Without further ado, here's our conversation with Sandra.

Sandro, welcome to the program once again. It's great having you.

Thank you,

Anthony. Absolutely. As we discussed a little bit in our last conversation when two fun platforms come together, after a merger or a variety of situations, you know, the challenges that you outlined in that episode go far beyond simply combining those systems. You spoke about how, you know, duplicate security matters inconsistent, share class IDs and mismatched time zones can silently accumulate into major operational risks that only surface when a firm, you know, tries to automate reporting or introduce analytics. These are hidden data quality landmines.

It don't just, you know, slow down integration. They can create uncertainty for distribution teams, expose gaps in compliance, and undermine confidence among stakeholders who expect a seamless transition. You know, for many leaders, it becomes clear way too late that that governance and alignment must come first. Just diving into the the data quality pitfalls that that you were talking about in our last conversation, Tell us a little bit of if we can dive in a little bit further, what those pinfalls are what those pitfalls are and how they tend to surface after a platform integration.

Sure. I think if if you thought about platform integration and let's take it let's take the example of, the fund merger maybe. So you got, two funds merged together. You have the merging funds, going into the receiving funds. And there, obviously, the first thing to do is you need to check where are the data, basically, where the data are held, what kind of data they have, and how different the data is from between the those two funds.

And it's again, here, I mean, you need to go through a lot of manual work in order to ensure that, basically, you you you get the complete picture. And it's a bit there. You basically you differentiate because that's that's that's that's a project. Right? So you're going through a transaction, and there you have different aspects on how with the same perspectives on how to look at the transactions.

So you have the regulatory one. You have the commercial one. You have the operations one. And it's on these those, you have many, many, many different things to tackle. Now looking at the, the data, the database match, you it's such as that you basically it's not just about availability of the data because normally, it depends a bit if you if you're talking traditional funds.

I mean, there you have much more transparency as compared to alternative funds, which are, let's say, sold on the private placement. We're we're reaching with their information. It's not that publicly available. But let's now, for the sake of this example, say that we're talking about, let's say, retail funds with all the information is publicly available. You need to get the full picture, so you need to know who which fund has its information where.

And then, obviously, you look then with a deep dive on the on the data type, and then also check where the gaps are between the two funds. So they can be actually if you look at the each fund, there's a share cost offering. The share cost offering, each share cost has different eligibility criteria, so it's therefore different investor segment with different requirements, and you need to somehow manage those share clauses. Right? And that that that's a long process.

And in that process, multiple stakeholders are involved. You have to work through the data with with them, and it's only once, basically, you have that all sorted out. And then you know, basically, what's the new target operating model is for the receiving concept, basically, for the for the end stage. Is it of interest just to show how, let's say, what the the the key phases are for such a transaction? Because once this has been laid out, we can also then tap into the different base to see where AI maybe can add some some value.

And we're we're we're talking about data quality. This goes hand in hand, especially when we're, you know, combining different sources for a platform, you know, in a merger, in a cross border fund situation, etcetera. Those go hand in hand with not just, you know, how you're getting that data to work together, but also the the real information, that you're that you're putting into these systems for for very practical purposes. You know, where are we seeing compliance in in investor reporting processes, in particular typically get bogged down in these processes?

Yeah. That's a good point. So you have if you take the investor reporting and, yeah, maybe if I can just make one distinction. If you look at if you just do the comparison between, traditional funds, right, and alternative funds. I mean, with traditional funds, you have you have to see much more standardization.

So, they use you can actually use the same process for a number of different investment strategies. With that, you have more efficient processes in place that are scalable. Now for alternative investment funds, that's a bit different because they might they might have, let's say, some some special needs or just some special process they need because of their specific vaginities of their asset class. And there, let's say, in most cases, standardized processes, they don't really work, and you you cannot really scale them. And and that's and here, I see a a big advantage with AI where AI can help a lot in, you know, customizing or automating current manual process in a customized way.

If we take the example of investor reporting for for example, we imagine you have the funds that uses series accounting for performance fee calculation. So you you basically show in that investor report a number of different share process in series. And in order to to check the reporting before it goes out to investors, you have to do a lot of manual work to make sure that all the data is correct because also you most likely pull them from different sources. You get some inputs from the administrator. You have some information in your own system.

In there, the the investor reporting can basically be quite loaded with a lot of information, making it, let's say, less comprehensive for the investors to read the information properly. And there, basically, I see there, let's say, some areas where AI can actually help just to make the necessary reporting production faster with less, let's say, lower operational risk because there's it should be manual work involved. And also, let's say, to have it also presented in a way that makes it more readable or digestible for the investors.

And we've we've been talking about, you know, data quality pitfalls after the platform integration. We're talking about how to deploy, you know, AI to make make that process faster. What data governance points or challenges or or practices are essential before introducing AI to a post merger environment?

I think what I would say is currently data is I mean, if you look at the service provider of the funds, so the main data basically is sits with the administrator, where you have the fund accounting and and everything. So that's actually the main source of data, but there's also a lot of information on the investment banking side. And if you then produce the investment you basically you need to get your inputs or your your your source your data from different data sources. And and I think that's obviously if you the more resources you use, the the higher likelihood that you get something wrong. Right?

And I think that's quite a challenge currently because, I mean, that can be very different from from structure to platform structure in the. But I think there, if you look at the future, if you can somehow use a system filtering the information that you actually have, all the the the fund grade information, people from one very capable system that actually is basically is used as the golden source for any recording production, whether it's regulatory driven, investment driven, also for internal business purpose.

Absolutely. And it this is still very early days, especially for deploying AI in these areas. They can, you know, occur between entities that are aren't currently, you know, using data tools or one side's only using data tools. As we start to see these capabilities become a little bit more widespread across financial services, how are you seeing or how do you envision rather AI ready data streams transforming regulatory and investor reporting based on what you're seeing in these these platform and merger situations.

I'm I'm not sure if if they're I mean, maybe they're a little bit too far away from. I mean, I don't know if it goes in one extreme situation. I mean, the scenario could be that there's a consolidation in the system used, I mean, for the different topics, but they let's say, in in the front of us from accounting to whatever transfer agency that they somehow they they consolidate the systems or on the other side that you basically use multiple AI tools, actually, being complementary to existing tools and build on existing tools. I mean, they're they're honestly, I'm a bit too far away from from that topic, so it's just yeah.

Right. Right. So that may be a little bit kind of farther ahead in what we can see right now. It's a very interesting moment right now. I often compare it to kind of where we saw Internet adoption.

You know, it was it was hard to tell what the Internet would look like once, you know, the entire neighborhood had WiFi or like basically what what the Internet looks like today versus especially at a time like, you know, the early two thousands, twenty years ago, twenty, twenty five years ago, you know, when everybody just had kind of the Ethernet cable, you know, right on the street. So I definitely understand your reticence there, but even for what you can see right now, I think this is extremely revelatory. Even for, you know, folks outside of financial services who are going to be conducting mergers. Obviously, that'll that'll have some, you know, financial services interest as all mergers do. But in just combining two entities from a financial standpoint, from a data standpoint, I think a lot of the same principles are are gonna fall into play no matter what industry you're in.

Sandro, really appreciate you being with us these past two episodes clarifying these issues and laying out for leaders across industries what's necessary to really make these operations function and scale up as you've been saying from the manual processes that we've had so far. Thanks so much for being with us this week.

Thank you for having me. It was a great discussion we had. We appreciate it.

Wrapping up today's episode, I think there were at least three critical takeaways for enterprise financial services leaders to take from our conversation today with Sandro Venturini, executive director for UBS Asset Management Switzerland. First, establish a single source of truth from term sheets to align stakeholders and accelerate cross border fund launches by reducing manual coordination. Second, deploy AI to anticipate regulatory and operational issues early, generating draft prospectuses that cut legal fees, information costs. Finally, prioritize human oversight on AI output for compliance, treating generative tools as efficiency boosters rather than autonomous decision makers. Interested in putting your AI product in front of household names in the Fortune 500?

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