Fintech Insider Podcast by 11:FS
00:04:17 · Laura Watkins
[00:04:17] S0: Amazing. Well, congrats. Sounds like you joined at just the right time. And thank you so much for joining us on your on your first week. So now we have our panel. Let's get started with the news. Our first story this week has already been kind of announced. Confide launches agentic governance Platform, a story on FF News and other places. Confide has launched a new Agenetic Governance Platform designed to bring governance risk and compliance, GRC, into a single system. The platform connects case management, policies, risk, and audits, replacing fragmented tools with a unified record of corporate decisions and incidents. It is designed to oversee both human employees and AI agents, allowing businesses to define where AI can operate autonomously and where human approval is required. Every action taken by a human or AI agent is recorded in an audit trail, helping organizations demonstrate how decisions were made and concerns were handled. Confide says the platform is model agnostic and already serves users across more than 150 countries. Pav, obviously coming to you first on this. Huge congratulations on the launch. Hopefully, I represented that accurately for you there. But maybe you can tell us a little bit more about it and and sort of how this came to be and and the problems you're trying to solve.
00:05:33 · Pav Gill, Laura Watkins
[00:05:33] S1: Yeah. Sure. So agentic governance as a concept I think is still fairly new. What is not gonna be new for long is the fact that billions of agents are gonna be running companies alongside humans. Now how do you govern both of them? Right? So traditionally in the pre AI state, it was all about, okay, pre AI legacy software, mostly point solutions. There's a new regulation that came out, let's just get that point solution that fixes it, or we just built one ourselves. Whereas with the way that the world's moving towards that, I don't think any of those point solutions that existed before today's world would be ready enough to fix a lot of these upcoming problems and state of events. So that's what Confide is trying to trying to fix, not just, you know, how we govern agents and people, but also using agents to do a lot of the operational compliance work with the human in the loop. [00:06:30] S0: And sort of where do you think sort of the traditional tools fall short at the moment and the sort of, I guess, we're also future proofing, as you say, as so many organizations are likely to be run-in association with, if not run by AI going forward? [00:06:46] S1: I see like a lot of, I mean, given my background, you know, having lived through a massive failure at Wirecard, I think one of the biggest enablers of governance failures is data fragmentation. [00:06:57] S0: Mhmm.
00:06:57 · Laura Watkins, Pav Gill
[00:06:57] S0: Mhmm. [00:06:58] S1: And that's compounded in a few ways, right? You have departments that are kinda like built around the software that they use, and then you have people in different departments that are cross functionally working together, but not necessarily communicating, and then you have all these different solutions that they're using that also not speaking to one another. So I think that problem is just gonna get worse, and that's why we're trying to bring everything in house. The other bigger problem is the connecting the dots isn't there, it's very manual. Right? You still have very, very advanced or big companies relying on Jira and Slack, on Excel spreadsheets just to do investigations, for example. Right? This shouldn't be the way we are working in 2026 going on 2027. I think if you can have a place which can connect the dots, you can see what's going on between events, cases, people, subsidiaries, brands, everything. I think that's just a more intelligent way of doing it. You could literally speak for example, today to confide, and ask it like, how many bribery cases you had in the last ten months, how you dealt with them, or you know, what your audit universe looks like, and it just churns it out in seconds, right? But imagine if a regulator asked you that question, you would be scrambling through people, meeting notes, I don't know, like it's just a big mess. And so I think that's a very clear and obvious problem, and we are really trying to solve it.
00:08:25 · Laura Watkins
[00:08:25] S0: Wonderful. Yeah. I'm just sort of putting everything in one house and say that all the, you know, metaphorical scraps of paper aren't just lost in the And and just I know that's horribly simplifying what you've built, but essentially that is solving an enormous problem just in and of itself, the the the organisational piece. Maya, you were you were kind of nodding along as as Pablo was kind of explaining the sort of problems facing organisations. Obviously, in product yourself. Sort of what's your take on this and how sort of needed something like this can be? Yes.
00:08:56 · Maia Bittner, Laura Watkins
[00:08:56] S3: I think the opportunity is huge for compliance systems. I think the risk is also obviously really obvious. Right? We know that AI systems can hallucinate. That could be very dangerous in the governance context. We know those risks. But for a really purpose built tool, I think that it can mitigate those risks and bring so much value. So just... I know. I was nodding along because the investigations, every... There's so much manual work that AI is really well suited for. Right? It is not to make the big strategic risk trade off decisions, but automating all of that manual work, it's really, really good at things like that. And I think sometimes when people have designed old systems, they'll say things like, oh, well, we should have an audit trail of everything that everyone does. That sounds great. It'll be re... Right. Audit trail. Wonderful. But then when it's time to actually conduct an investigation and you were trying to dig through records of everything that everyone has ever done, it's really hard to make heads or tails of it. But AI is so good at turning it, like, through all of that data, finding what's important, finding those patterns, pulling out the dot piece. I think it could really speed up some investigations in particular. [00:10:11] S0: And, Vijay, you know, as a a sort of head of business innovation yourself with sort of, you know, how is the use of AI in businesses changing the way that you're sort of thinking about governance as you set up your business and your business expands and grows?
00:10:27 · Vijay Konduru
[00:10:27] S2: One of the most important things is, like, I believe that agentic AI sits at a very interesting knife's edge or at a really interesting paradoxical situation. Like Maya mentioned, the promise of what AI can deliver is enormous, and that's why we as a bank are investing so much in AI and agentic driven innovation. But by very nature, the models that these agents are built on are nondeterministic and probabilistic systems. Right? And that intrinsically introduces some kind of variability. The way we're thinking about this whole growing ecosystem is really threefold. The first one is to engineer whatever products we build for trust with the right robustness, with the right orchestration, guardrails, and evaluation frameworks and appropriate risk management built in. The the second is to make sure that not every agentic workflow or process, whether it's for a consumer or for another business or even for an enterprise, has to lead towards full autonomous execution. Right? The use case determines where and when autonomy plays a big role. And then the third big principle is we believe that given the state of AI and agentic AI, the human in the loop is absolutely critical. Right? And the human's gotta be more in the loop from validating the outcomes and outputs of these models and approving actions and so on as the consequences get more critical and as the workflows themselves get more complex. And that's what I believe we really have to do to manage agentic AI, at least for now.