Scouting for Growth
00:16:39 · Sabine VanderLinden
[00:16:39] S1: So let's visualize that. So I'm Bast, and I have my chatbot on my browser, but that is a space I'm working from, connected to Slack, Gmails, and everything is happening in front of me. So if you actually look at this claim, chief claim officer or this underwriting manager, right, and all are AI enabled today, describe for us properly, you know, because most people will hear things in a different ways, right? Picturing it is going to help us understand really what we see on the screen. What does that underwriter or that claims officer, what do they see when they arrive at their desk at 09:00 in the morning and open that beautiful browser?
00:17:22 · Bastiaan de Goei
[00:17:22] S2: So the persistent agents or DOTS in in this case, they operate in the work surfaces that you already engage with. So yes, you can engage with this via chat GPT if you so wish. If you prefer to work together in Microsoft Teams or you prefer to work together in Slack, you can do that too. Okay. And I myself, I actually work a lot on Slack. And so like when I start my day, my dot has prepared my agenda and has shown all of the things that need attention for me and has already prepared responses for all of the urgent things that require my attention. So if you actually you know, like make the parallel to an underwriter, then what an underwriter would see is that you've effectively abstracted away the entirety of like the underwriting workbench and and the email inbox. So instead you'd open your preferred way of communication, could be Microsoft Teams for a lot carriers, where the agent has already prepared. So, okay, overnight I received the last 12 submissions. I've already analyzed them. Four of them are well outside of our risk appetite. There are another four that are from your preferred broker relations and the remaining... Of the remaining four there is two that I think could be very interesting and two others that are maybe. And I've already prepared a quotation for them. Do you want to review them? And so that could be like the way that a underwriter starts their day.
00:18:56 · Sabine VanderLinden
[00:18:56] S1: So as Asian carry more of the workflow or the task we do every day end to end, I think the question stop being, what does the machine can do and become who is accountable for the work, right? And we know we need that human in the loop. We are in a regulated industry. So we need to know what may go wrong as well. Pricing coverage was a big topic yesterday. Declare true because we were talking about agentic distribution settlement. So this changed someone's life. This changed someone's rules, accountability, responsibility in a regulating framework. So where especially does a human have to stay, right, in that loop? And how do you tell a decision that can be delegated from one which should not be delegated at all?
00:19:46 · Bastiaan de Goei, Sabine VanderLinden
[00:19:46] S2: So I think that, first of all, what is important is that the... When staying with the underwriting example for a moment, the ultimate quotation is the underwriter's responsibility and that person is accountable for that decision. And so the tooling though needs to help the underwriter understand what they are saying yes to and what decisions were being made, what data was being used as input for those decisions. And I see it as something that we are investing in, in order to make that really easy. As an example, and this is a very small example, we... Earlier this year, we released ChatGPT for financial services. This is a tool that is focused on banks... Banking right now. But as a very tactical example, we noticed that analysts, banking analysts, when they were preparing investment presentations and using AI tools, were double checking a lot of the data points and that went into those presentations. [00:20:44] S1: 100%. [00:20:45] S2: And so... Because it needs to be precise. And just like in insurance, banking is a business based [00:20:51] S1: And on last, you know, I do the same. I still check everything because it's about reputation now.
00:20:51 · Sabine VanderLinden, Bastiaan de Goei
[00:20:51] S1: And on last, you know, I do the same. I still check everything because it's about reputation now. [00:20:56] S2: Exactly. It's about reputation and banking, a lot of financial relations are based on trust. And so... But if you double check every single data point that an AI tool provides you, then you've just lost a ton of efficiency. And so as a very practical example, we released as part of ChatGPT for financial services sources by paragraph and that are provided in the interface of the results where you can then just hover over that particular claim or data point and see exactly not just which document that particular claim came from, but also which paragraph with the exact, line where that data point came from. And so this actually practically starts saving an analyst like a ton of time as they are going through these types of workflows. Now expanding that, that specific example across like an entire workflow, an AI tool should be able to give you the steps that they took, the assumptions that it made and the data points that informed those assumptions. So that the underwriter or claims adjuster is empowered with that understanding and can make an informed and competent decision.
00:22:03 · Sabine VanderLinden
[00:22:03] S1: Another conversation I had yesterday, which I thought was interesting is, you know, how do we codify human knowledge? When you look at, for example, the underwriting examples, a lot of experts have knowledge and expertise, right? And therefore, what happens is we need to make sure the system can manage exceptions. Yeah. So when the knowledge is not codified, what would be your recommendation for an organization which starts using, let's say, Astra or Dart to make sure that things, processes we might not know because it is the way we used to do it, it started to be codified so that the system is much better, I'm gonna say, acquainted with the processes we are working around or the underwriting exceptions which we didn't know about before.