Welcome, everyone to the AI and financial services Podcast. I'm Matthew Demello, senior editor here at emerge Technology Research. Today's guest is Bill Wade, chief product and technology officer at Fico. Bill has been with FICO for over 20 years developing decision management systems. Prior to his time at FICO Bill worked for different software platforms in sales and systems engineering. He joins us on today's program to talk about what challenges for financial service leaders look like from a data perspective. Throughout the episode, he offers advice and strategies on how to level up data governance operations at their organizations. Without further ado, here's our conversation.
Bill, thanks so much for being with us on the program this week.
Thank you, Matthew.
I'm looking forward to it. So it's been a real pleasure having folks from FICO on the show just the insights that you guys have from looking across financial services. And I'm really interested just in terms of, you know, just that vantage point right now, what are you seeing is the biggest challenges currently in this late 2023, at least for the fiscal year. And just in terms of the biggest challenges currently facing financial services professionals right now from a data perspective? Yeah, I
think there's a number of challenges, quite frankly, all driven from the fact that there is an underlying premise and intends that if there is more data brought into the operations, there's going to be better results, and has pretty much proven itself true. But there's a number of stumbling blocks that we see commonly run into from our client base anyways, and the themes across the market suggests that this is a common set of issues that each and every one of those organizations looking to capitalize on data, and more specifically, analytics or AI on top of that, they're all sort of struggling with them.
Yeah, it seems a lot like it's hitting financial services, especially the democratization of data. That said, I mean, you know, just looking across sectors, you know, financial services definitely has a leg up compared to, you know, other industries and other sectors, what would you describe, just as you know, the challenge in terms of transitioning that data into something that they can use?
Yeah, I think there's actually six areas that I've seen stumbling blocks or hurdles that pulled back from the full potential of realizing the value of the data. The first one is, how do I get access to it very frequently, there's an intense to get access to it, it can be purchased, it can be mined from their own data sources, but it's usually in disparate systems, and it's difficult to get access to. A second problem that I see come up is how do I manage that in a cost effective manner? It's a large volume of data, and it grows very quickly. The third problem that I've seen is how do you get business insight out of that useful business insight out of that data? Then that carries into fourth? How do you action that at fifth? How do you measure the outcomes and the returns from that data? And then finally, how do you govern that data? Compliance lineage? How is it used to traceability in concert, all six of them provide a pretty big hurdle for organizations looking to bring in alternative data sources and improve their outcomes.
Absolutely. Now, I know a lot of folks tuning into the show have a decently strong background in data governance. So they have a good idea of a lot of it, at least the the six points that you're putting out there in the problems involved, access is the front door, I'm wondering how much of the battle takes place in management and what that means versus governance, maybe to the folks listening who you know, hear these terms and just want to get a differentiator of the difference in just listing it last for governance. It sounds like, what is that data doing? Once it's out the door? Do I have that right?
It is correct. In the growing world of CISO, and data security and data privacy and the growing global regulations that are mounting, it's not enough to just have access to the data, you got to be able to explain how you got that data where you used it, how the decision was actually derived? Then that follows on from regulation perspective, how do I get rid of the data, if I'm being asked to do so? Those are the more common practices. Some organizations struggle with that tremendously. Other organizations avoid it. But whatever the strategy is, for using alternative data, it has to be foundational and core to whatever approach you take.
Indeed, you mentioned avoidance right there and I know I absolutely this is happening in fin surf, just in terms of a changing culture, you know, call centers, just as an example, like, you know, financial services organizations, they used to try to put as much distance, you know, between themselves in the customer as possible. That was sort of the philosophy going into call centers for a very long time. You know, any customer complaint is a bad thing, you know, kind of a bit of a Don't Ask, Don't Tell policy, but only if we have to now we absolutely see that attitude is changing, we're seeing huge opportunities in the call center space, especially where we can have technology come in and make it more efficient. Do you think that avoidance of data is is of a similar mentality, and in terms of, you know, down the road, maybe 1015 years, financial services, organizations won't be able to afford to avoid data in the same way that maybe this is kind of a last stage right
now. They definitely will not be able to afford avoiding the data. If there's not a strategy and an approach to include alternative data and grow your data to understand your consumer, you are going to actually end up on the short end of that all leading organizations that are moving towards a digital engagement, they're craving and seeking more data. In fact, you mentioned the call center, what a what a great source of data actually find out what your customer said rights, if you if you want to know where your customers at what their behavior patterns are, and what's coming next, as just a perfect example of that. Another example is it borders on the on the creepy side of the data side is, what do they do when they interact with you? Do you actually know where they spend their time in your mobile app, or on the digital channel, whatever that might be? These are things that we're seeing is that uptake? Because the more you can, you can actually understand that data, the better suited you're going to be to addressing your client's needs when the next Touch, touch point with you. Right, right. And
in not quite an implicit theme of your last answer, I think it was pretty explicit, is leave no stone unturned for where data is being collected, also kind of implicit, and then that lat last answer is the challenges surrounding privacy and how that can be overcome in the spaces where you already have the 360. You look at the customer from every angle, which is on your mobile app. Well, in that way. I'd like to even open up this this kind of second half question. We you know, we're usually asking about data tools. But I'm wondering if it's, you know, maybe it's not tools in terms of, you know, specific solutions or specific technologies, but maybe it's rather what strategies, our financial services leaders leveraging in order to solve these challenges, or is it technology based?
Yeah. A common theme across the board for any successful digital transformation is, is the equal balance between people process and technology, it cannot be any one of them, it has to be all three brought together and successfully, you know, traditional strategies of building data lakes, and then mining that data from a data scientists or BI perspective, haven't really produced a lot of fruit to date, huge investments being made. So one of the things that we see is an up and coming element here that actually addresses the governance and privacy aspect of it as well as you should be able to actually access data where it is in the form it's in without having to make major investments in re platforming or coalescing that data. And in the process of collecting it, that way, you begin to formulate your lineage, you begin to formulate an explanation of how you're using that data. But ultimately, that's the problem, right? It's, it's less about, where is the data? How do we get access to the data than it is? What did you do with it? And can you explain that, right? And those tools are the up and coming tools relative to getting leverage out of data? Right, and
I know kind of an important theme in the democratization of data conversation of head across industries is, you know, there is such a thing in this lust for for data just to have, you know, all the possible inputs that you can, it is actually possible to have too much data and having the right data is a lot more important, just as kind of an underscore of your answer. But I'm even wondering, you know, even for financial services, leaders that, you know, don't know a lot about data governance right now, you know, more legacy institutions that really haven't, you know, dip their foot in this poll. I know the bigger ones absolutely have. But where's a good place to get started in terms of leveling up an entire organization when it comes to data
governance? That's a great question. I actually want to go back to something you just said Matthew, because it gets Pivotal, which is, sometimes it's actually not about the volume of data. It's the quality of the data. And the answer actually lies in addressing that question, how do I know what data is actually useful? What data provides insight that can be actioned And in the course of getting to that you actually have to institute a mechanism for how you're using the data. Right, right. And so what I see is as a very useful tool for organizations that have been successful, this is not a big bang approach. It's not about making a huge investment in clicking petabytes of information. But rather start with the information that you have today, and actually refine it, bring it down to the set of data that's actually useful. In order to answer that question, there needs to be some mechanism to measure how did I use that data? And did it actually provide business return? Not that it actually created an excellent machine learning or AI model. But did that data actually find its way into a decision either through that refinement process or directly? And what outcome came as a result of that decision? If you can do that, you can begin to measure the effectiveness of the data and you can start shedding data. You don't need all of it, you just need the right data.
Absolutely. And I think that goes to the first two points in your three point in terms of you know, where the solutions are people process and technology that really more has to do with the first to not technology. And I know all the conversation right now, especially in the explosion of generative AI tools that are grabbing everybody's attention, the people in the process tends to get a little bit last. So it's all the more important to talk about them. Really, really interesting stuff. I know you've specialized in decision management systems, we're actually going to come back to that in another episode. But Bill, this has been very illuminating for our audience. I really, really appreciate you having on the show.
My pleasure, Matthew, look forward to the next opportunity.
I know I made a big point of this on today's episode on the line with Bill, you probably heard me make this point just a few minutes ago. But I think the point that he had made in terms of the three elements of data governance, people process and technology at this current point of generative AI adoption in the hype cycle. I think there's a lot of emphasis on technology right now. And people jumping into that pool without dipping their toe in the water of what it means to have the people and processes in place first. And I think Bill gave a lot of really incredible advice about how to start to think about that process. If you want a real tutorial on how to go about data governance, we might need far more than a 20 minute podcast episode could ever allow. And if anybody's teaching that class online, I really hope it's bill. But stay tuned for our next episode. We're actually going to post it immediately into next week on the AI in financial services podcast, where Bill's going to talk about the challenges for fencer professionals in winning executive buy in for data governance projects, how this can help FinServ teams level up the infrastructure necessary for transformative AI use cases in the role of Decision Management, which I know we touched on here and there in today's episode. We'll play throughout the process on behalf of Daniel and the entire team here to merge. Thanks so much for joining us today and we'll catch you next time on the AI in financial services podcast.
