Credit in Focus

Defining Alternative Data: Building a Common Language for Credit Risk Management

• LexisNexis Risk Solutions • Season 3 • Episode 1

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In this episode, we explore what “alternative data” means and why defining it matters for credit risk management around the world. Moderated by Stewart Watterson of Datos Insights, LexisNexis Risk Solutions experts Kevin King and Neil Allen discuss the growing complexity of credit data, the challenges caused by inconsistent terminology and the impact on innovation, regulation and consumer trust. 

The conversation introduces a standardized framework for understanding traditional credit data, alternative data, open banking data and emerging data types, with regional perspectives from the US, UKI and global markets. Gain practical insights into how clearer definitions can accelerate adoption, improve risk decisioning and support more inclusive lending outcomes.

For more information and to download the Defining Alternative Data Report and supporting collateral, click here.

DISCLAIMER: The information provided in this podcast is for informational purposes only and is not intended to and shall not be used as legal advice. The views and opinions expressed in this podcast are solely those of the speakers and do not necessarily reflect the views or positions of LexisNexis Risk Solutions. LexisNexis Risk Solutions does not warrant that the information provided in this podcast is accurate or error-free.

LexisNexis and the Knowledge Burst logo are registered trademarks of RELX Inc. Other products and services may be trademarks or registered trademarks of their respective companies. Copyright© 2026 LexisNexis Risk Solutions.

SPEAKER_03

Welcome to Credit in Focus, a podcast series by Lexus Nexus Risk Solutions that unpacks the global complexities of credit risk across the customer lifecycle, from marketing and origination to account management, collections, and recovery. Let's get into it. Welcome back to Credit in Focus. I'm Kevin King, VP of Credit Markets at Lexus Nexus Risk Solutions. And joining me for a discussion today are my colleague Neil Allen, head of strategy for credit risk in our United Kingdom and Ireland markets, and Stuart Watterson from Datos Insights, who's going to be moderating our discussion today. Why don't you both take a minute or two and tell the listeners a little bit more about your role and kind of the perspectives you're bringing to today's conversation? Maybe Neil, we'll start with you.

SPEAKER_01

Yeah, thanks, Kevin. Yeah, it's great to be with here with you all today. As Kevin mentioned, I lead the market planning for credit risk decision in the UK and I. And I've spent my entire career in financial services and credit reporting agencies. So alternative data is an exciting innovation for the UK credit industry. And I'm pleased to discuss how we can help the market better understand it on this podcast. Stuart?

SPEAKER_02

Yeah, uh great. Uh thank you, Neil. So, as uh Kevin said, my name is Stuart Waterson, and I'm a strategic advisor in the retail banking and payments practice at Datos Insights. And that's where I cover several aspects of retail banking and including consumer lending. Now, I am not a consultant by trade. Not that there's anything wrong with that. But uh prior to this gig, I I did spend my first 30 years of my career in consumer lending and and then in mobile technology with both P and C and Chase. Uh I as well am excited uh to talk and dive a little deeper into the topic as a whole. Uh I've been uh publishing a lot on this topic, and uh everything starts and ends with um the new data is a game changer in the lending market, and it's going to have all kinds of really great primary and secondary impacts for lenders in every market. So uh with that back over to you, Kevin, to set up uh our topic for today's discussion.

Why A Shared Data Lexicon Matters

SPEAKER_03

Well, I think uh, Stuart, you know, a really good job there and helping a segue because what we're gonna dive into uh on today's conversation is really the common language that we think needs to be established around alternative data to help credit risk managers push forward and find out ways to uh to kind of innovate and uh pull new insights into the various risk strategies. Credit risk, I think at this point in time, is in a very, very interesting space from a data perspective. We have a large influx of new data types entering the market around the globe. I think you could argue it's it's more diverse than at any time since the invention of what we would now kind of consider modern traditional credit scoring like FICO and subsequently Vantage. Um, but despite all of that change, the industry faces a real terminology challenge where we're using inconsistent naming conventions. So we may alternate ways we talk about the same data source, or we may look at similar data sources and not properly distinguish critical qualities that very much impact their ability to be adopted in different regions. When it comes to barriers to adoption, innovation, global collaboration, this issue of not having a standard taxonomy can be very, very problematic. We've recently collaborated with Stuart and the Datos team on developing a taxonomy that we'll talk about on this podcast. And I think subsequently we'll be putting out some content on that I'd encourage everybody to go read. But I think maybe as we go through this conversation, we'll start to dive deeper and unpack what we mean when we say similar terms for identical data sources or different terms that can often lead to fear, uncertainty, and doubt in the credit risk landscape. But if we're gonna have that conversation effectively, it probably needs to be moderated with more skilled hands than mine. So, Stuart, I'm gonna throw it to you to really guide us through this conversation.

Real-World Impacts Of Terminology Confusion

SPEAKER_02

Thank you, Kevin. Yes, and my hands are skilled at talking. But you know, I couldn't agree with you more. I was really very glad when we started this conversation earlier this year and found out that I wasn't the only one seeing this. I thought it was me. But you go into a conversation assuming that others understand specifically what you're talking about, but that's that's just not always the case. So this is definitely an issue uh that needs to be resolved. We have also been seeing and experiencing terminology confusion throughout our organization and when it deals with credit and the subsequent uh miscues that are often as a result of the lack of a standardized naming convention. Talk to us about how this could have some real real world impacts on credit risk management.

Benefits And Caveats Of New Data

SPEAKER_03

Absolutely. Well, I would say at the highest level, I think most risk managers that are listening to today's conversation would agree conceptually that gaining a more complete picture of a consumer in their financial health is incredibly beneficial to designing not just sound, but let's say highly competitive lending strategies. Getting that more complete picture does everything from enabling financial inclusion and opening up more pathways to affordable credit to consumers who may not have great access today when strictly traditional credit data, we'll define that a little bit later, is used to evaluate them. And, you know, beyond those kind of societal benefits, we know as uh profit-focused enterprises, right, that are out there to lend money to help achieve organizational goals, having that more complete picture allows you to influence critical metrics, you know, boost portfolio growth, improve profitability, improve response rates, reduce loss rates, all of these things. So I think we all agree with a couple of critical caveats I'm gonna lay out here that understanding a consumer better is really, really valuable. But those caveats matter a lot. I think one of the really obvious ones once we're operating in that credit risk base is what is the regulatory fit between a category of data or a specific source of data and the specific region you're looking to leverage it in. Also, any new data source truly needs to be additive, which is to say, all right, maybe you're not capturing that uh particular consumer behavior or insight in your underwriting strategy. But if that insight isn't helping you improve your ability to project how a consumer is going to perform with a financial product, it's really not adding any value and isn't worthy of consideration. I'd also say that in a lot of markets, getting clear on the adoption of a data source can be really important. Historically, this is not always true, but as a broad rule of thumb, the larger the organization, particularly very large financial institutions in the US, this holds true, the least, uh the less likely they're going to be to want to be one of the early movers on a particular data source. They may want to see a certain data source get used in the underwriting market, perhaps in the fintech community or elsewhere, before they feel comfortable plugging into their organization. And then relationship to credit risk, right? So is a particular alternative data insight or maybe some other form of credit data insight, is it correlated to credit risk or is it causation, right? And one of the examples I often give here is when we think about the insurance space and this adage that we have, at least in the United States, that red automobiles tend to be more expensive to insure because of historical data that says that some of these cars have um have a higher propensity for speeding. It really tells you a little bit more about the driver than the car itself, of course. But understanding over time, is this something that can be gamed? What we know is since that became common knowledge, fewer people buy red cars, right? So are we talking about data insights that once fully embraced and understood by consumers, and we should want all forms of credit data to be embraced and understood by consumers if it's going to be used to dictate their access to credit? Will it remain predictive or is this a less stable insight that uh can easily be manipulated? So these are some of the qualities, and and you know, for sure, Stuart, not an exhaustive list of things that we really want to understand about any insight. But when we don't know which insight we're talking about, all of a sudden it becomes very difficult to keep track of those issues. And that's where I would kind of, you know, I'll probably return to this term a number of times under discussion. The fear, uncertainty, and doubt that often stands between risk managers and their access to new data really starts to creep in because when you have lack of clarity, the most common behavior in the risk management space is to pump the brakes on adoption and innovation. So, Stuart, that that's kind of how I think about at least some of the most notable challenges that can arise when we don't have that clear taxonomy in the credit data landscape.

SPEAKER_02

So, Neil, I couldn't agree with this more, but uh, how are you seeing this from a UK perspective?

SPEAKER_01

Yeah, so although the the UK is several years behind the US in terms of the market understanding and adoption of alternative data, we do face similar terminology challenges and for the same reasons. But also ones about general credit education that have a more regional focus due to our government's intervention on some things. So I think from our perspective internally, yeah, we must continue to educate the market about the distinctions and benefits of alternative data. We can see from our consumer lending research that there is a strong interest from the market and they want to capitalise on using it to make better decisions. So helping them understand specifically what it is is is is obviously very important for that objective. Externally, there's a general lack of understanding among consumers regarding the information contained in their credit profiles, which might be surprising to some given the you know the relatively mature nature of the UK market, but nevertheless, um it absolutely exists. Um I think you know, in terms of how we can improve things, obviously podcasts like this one you know help the market gain a clearer understanding of what alternative credit data is and the terminology that underpins it all within the context of the other information types that are out there. I think from a credit education perspective, um for consumers specifically, that is still you know a big challenge. The industry often employs technical terms that are not intuitive, it leads to confusion, you know, generally just due to a lack of a straightforward explanation as to you know what something represents in terms of data. So we need to improve this area um and particularly when presenting a consumer's own data back to them. Obviously, enabling a consumer to interpret literally their own data should be you know the absolute minimum, you know, that we aim for as part of this work. But also, you know, to help them understand you know the complex financial repayment information that they often have to interpret, you know, from the lending and creditors that they're sort of working with and seeking credit from. I think what is encouraging, it's it's encouraging to see that firstly the government recognises this issue and you know the national financial inclusion strategy, part of that also aims to improve financial capability. So the reason why you know they alongside ourselves think that is so important. In their report, they cite that over one in ten UK adults report having low financial capability, and that significantly affects how they can manage and perceive their finances. Half of those feel overwhelmed or stressed when dealing with financial services, and similarly, one in five of those with low financial capability don't feel confident shopping around for financial products. Now, if we could make that easier for them, the likelihood of these individuals being able to secure better credit, better repayment rates, and all of that good stuff is is obviously heightened, and obviously that's being recognised by our government and um the national financial inclusion strategy. So although the statistics are concerning, I think you know the fact that we're trying to address it, you know, industry-wide is obviously beneficial. Helping you know the consumer understand the financial products and manage their manage their money more effectively is the ultimate goal in all of this. One of the things that I do want to call out though from that strategy that I really do support is financial education will become a compulsory subject in English primary schools. So this is something that you know people like myself and many others within our business have been observing as a requirement for many, many years working in the credit industry. And it's great to see that that is um that is one of the definite outcomes of this work. So, yeah, that's a positive step forward.

UK Perspective And Consumer Education

SPEAKER_02

Yeah, absolutely. Uh it really sounds like your market's kind of hitting it from all fronts. You know, uh Data is a is a global firm, and we we have a strong UK practice with credit offices in Richmond. In several ways, I I see the UK's emerging credit data, you know, utilization as quite a few steps ahead of the US market. But with that, it sounds like it's it's the same issue that's that's plaguing the management discussion of these uh you know these various types of data. Now, when viewing globally, this is an issue that probably is is uh you know really multiplied in its complexity, having different data sources that first of all have their own specific terms, and and we see that in UK versus US market as well, but then another whole set of uh terms. And we tend to look at it at least by by region, and uh but in APAC it's almost down to the country. So cross-border operations can be tricky, but you gotta start somewhere with with, like I said, saying as Kevin said, setting up the uh the uh lexicon. You know, and going back to actually some things that that Kevin said in a research study that we performed earlier this year that that was sponsored by uh Lexus Nexus, we saw that, and and we asked a lot of questions, but I I think this was the one that kind of set it off. We asked, what percentage of your total applicant population cannot be scored using traditional credit data due to no file, thin file, etc. And and globally, so this is globally, uh 20% of global lenders are unable to score 30% of their applications. So, you know, let that sink in. The number is actually a little bit higher when you look at the US and and uh actually I think it's a little better in the UK, but again, you know, you guys are farther along the the uh uh the spectrum on this one. So that's that's the issue. How do we Kevin's point bring those people in into the the credit market within within the region? So let's let's let's move on to the solution. I think we've identified the the problem pretty well and and the impacts it has. There's been you mentioned obviously a great influx of new consumer credit data over the past few years, and and that's created this challenge. Um and is a it's a good challenge to have. The solution, of course, is a standardized and commonly accepted framework. So, you know, Kevin, um, would you please kind of you know take us through that?

The Case For A Standardized Taxonomy

Four Categories: Defining The Framework

SPEAKER_03

Certainly. And I think this has, Stuart, if I've been answering this kind of question for 15 years, I I would say that certainly in the last two or three, it's devolved a little bit. And I think there's whereas maybe in the past we had only a need for three of the terms that we're gonna cover. I think we've recently expanded to a fourth in recognition of how this market is growing. So we'll start with uh traditional credit, right? And whether we're gonna think about this through a score lens, in which case we're probably talking about credit scores like in the United States, a FICO score, or a vantage score, or whether we're talking about the data itself and we're thinking about that kind of trade line data that most commonly gets reported to national credit reporting agencies here in the US that show how a consumer is managing and repaying certain debts like uh mortgages, other loans, credit cards, installment loans, that would be traditional credit data. And I think again, I'm I'm gonna try and be very region-specific and remind everybody what where am I referring to or where am I not referring to here. So for the US, generally speaking, I think my view on traditional credit data is that it provides a critical foundation for underwriting or any other credit man, uh credit risk management exercise, but it's table stakes, right? It is the starting place that every person who is managing some kind of credit risk responsibility should be looking. But not only does it not offer any competitive advantages, I think in many ways over the last uh 10 years, it's been actively degrading, meaning increasingly it is not self-sufficient to fuel complex credit risk management tasks. Now, the more simplified your needs, right? If you are an organization that only has two products and a decline to choose from. So somebody applies, and the question is, are they gonna get approved? And then you're just gonna split that approved population in two product categories. Maybe traditional credit data is enough. But once we start getting into the world where I think most of our listeners are operating, where there are so many levers around pricing, credit lines, various terms, all of this is gonna depend on the kind of financial product we're talking about. You need that more granular risk separation. And that's where traditional data, again, is that helpful foundation, but oftentimes is not solely sufficient the way it might have been, say, 20, 25 years ago. The second category is alternative data. And in the US, at least, I would define alternative data as data, and I'll give some examples in a moment, but I would say which meet a couple of key criteria. This is data that meet all of the regulatory requirements for a given region, is not included in traditional credit data, nor, and I'm spoiling a little bit here, in our next category of open banking data, and which is highly predictive and additive to whatever's being used in your uh underwriting strategies today, right? And that last category maybe isn't critical to define the category, but to make it a functional definition. Anything that can't meet that last criteria there probably isn't worth thinking about very much at all. So examples of that, again, it's gonna vary by region, but at least in the US, we would think about very often similar signals to what get captured uh from a consumer behavior aspect in traditional credit data, but which aren't being actively reported to the credit bureaus, uh, the national credit reporting agencies, I should say. So think credit inquiry data, where those inquiries are not appearing on your core credit reports. Think public record data, which again is not found on traditional credit reports. Those would be all areas that to me would clearly qualify as alternative data. So that could be court records, that could be professional licenses that might indicate that a consumer has an ability to obtain steady employment in a certain field. Those are just a couple of examples that I would point to. Different types of alternative data have different benefits. When I think about inquiry data, which is increasingly a signal that I would argue is kind of moving from traditional to alternative, as a lot of mainstream lenders in auto and credit card are declining to post hard inquiries with credit reporting uh organizations. Those inquiries, in and of themselves, are not massive needle movers, right? You don't see a consumer applying for certain financial product unless maybe it's a very deep subprime loan and radically change your view of their credit worthiness. But it does give you a very real-time, somewhat high frequency window into how they're managing their financial lives. On the other hand, some of those public record event data types are not going to happen very, very often, right? They are not something that happens once or twice a year. It may be once every five years, once every 10 years. But when someone does get a new professional license, we'll keep using that example. That often signifies something pretty notable when it comes to shifts, and in that case, hopefully an improvement in a consumer's credit quality. So just a couple of aspects we think about with alternative data. The third category, and I think the one that's become worthy of its own distinct segment in this taxonomy here over the last, you know, let's call it two to three years. The term we're going to use is open banking data, right? And uh, you know, we we mentioned at the top of the call that one of the challenges here is the fact that some of these signals go by many different names, right? So cash flow data, uh consumer permission data, checking account data, all terms that very often get used here. But I think particularly globally, since open banking, I think has become really Really well establishes a terminology in UK and some of the uh AMIA markets that that's the one to go with here. And we're talking about consumer permission financial data access directly from financial institutions where consumers are managing various kinds of credit or checking account relationships. That data is often delivered via API in real time. It doesn't have to be that way, but most commonly it is. And again, we think about that data. It is distinct enough, both in its benefits and in its challenges, that we think that probably represents a separate category from alternative data. And then the last one, which I think is particularly as as uh you know to show my age, someone who's been in this market and working to grow adoption on alternative data and open banking data for some time. This final category is very important to me, which is emerging and specialized data. Sometimes I'll use the term experimental data. And that's mostly behavior-based information, which often cannot be used in uh the specific geography where we would categorize it as emerging and specialized. So, why might something fall into an emerging and specialized data bucket instead of alternative data, for example? Well, maybe it doesn't meet current regulatory framework, right? That tends to be the most common one. Here in the US, a classic example I will give here is social media data. There is very likely a fair amount of data to be mined through social media, which is correlated, if not having some causational relationship with consumer credit risk that could enrich underwriting flows. However, it does not currently meet most standards when it comes to uh at least US regulatory guidelines, for example, you can't dispute that data very often. If a consumer calls up and says, hey, I was declined for a loan, uh, and you cited data that you got off Facebook, there's not a method to call up Facebook and dispute and say, hey, I didn't really post that data, take it down. It just maybe one day. You could imagine a world where that process does exist. We're a ways off it today. So that last category, emerging and specialized data, does not mean that a data signal is valueless, but it means that for whatever reason, and again, I think regulatory is the most common but not exclusive reason, that data is essentially off limits to most lenders today for the purpose of informing underwriting or any other kind of regulated credit risk uh decisioning event. So those are the four categories. Traditional data, essentially what I think most of our users use and maybe have been using for at least the long and recent history of their uh their organization, alternative data, data that meets all the appropriate regulatory standards for your given region, is predictive and is not traditional or open banking data. Open banking data, that's that data that you're getting consumer permission to access from their financial accounts. And then that last category, emerging and specialized, potentially predictive, but currently off limits. I know I I know I uh went on a bit of a tear there, but I I think again, if the whole theme of this podcast, Steward, is you know, definitional clarity here. I wanted to make sure I rung those bells a couple times.

SPEAKER_02

Yeah, absolutely. It's it's a lot to uh take in at first. And that's one of the reasons why there will be a publication thought leadership paper that will put all this forth and uh and do it not only in words, but also some graphical representations, I think will make it easy to consume. So there are the four categories. Neil, how do you see these categories aligning with UK practices and how data is viewed there?

Alternative Data Examples And Signals

SPEAKER_01

Yeah, so that there are lots of similarities um with the US, there's no doubt about that. But I guess one of the biggest differences is in the UK, public information data is a mainstay of a UK credit report. So obviously that's something that from a US perspective sits more in the alternative space. In the UK, you know, that an expectation is that all traditional credit providers would have access to public information data and be able to display that. Another source that's um probably unique to the UK is uh a source called current account turnover data or Cato data. So this is um essentially information from the um large banks in the UK about the information contained in individuals' current accounts, funnily enough. So this um data is typically used for the affordability indebtedness use cases, more so than you know, credit risk decisioning. Um, but nevertheless, you know, it's uh it's a useful supplement prior to the data that's already available from a general credit perspective to provide a more nuanced outcome for those use cases that I mentioned earlier. Also, Cato data has recently had its rules relaxed. So in theory, that should make it more useful to lenders who are not just the banks that supply the data. The more granular view that the more relaxed rules afford will help these uh lenders um be able to make better decisions with that data source. Other than that, the emerging and specialist data, um, there is um a concept of um what's I guess commonly known in the industry as work report data that's um growing in the UK and particularly from the sort of traditional bureau perspective. Um this is essentially data from the payroll services providers in the UK. So all of that good stuff about your your pay and your tax and your salary sacrifice and all that kind of insight can be made available and accessible through a consumer consent system for that type of data. Uh obviously, you know, it's very interesting to us to explore. And last but not least, is um what what we would call essentially tax data from the HMRC. So over the years, this is you know a data source that you know people that have been in the industry a long time, like me, have campaigned for a lot to access. And access to that source is becoming more and more um prevalent, and it is becoming more and more accessible to the right kind of organizations that will utilize it appropriately. So uh they're just uh two more examples of where you know we've got emerging and specialized data that I believe will, you know, add considerable value to the portfolio that's already available to us to use.

Open Banking: Distinct Value And Challenges

SPEAKER_02

Agree, you know, that there is obviously so much similarity in definitely definitely in the in the categories, definitions between the US and UK, and that makes perfect sense. So as we kind of turn around and and looked east though, it's kind of the same thing incrementally off of let us say the UK format. The EU also sees um at least the first three categories pretty clearly and and definitions hold up. Now, the specific types of data that that falls into alternative data may be a little bit broader and and and wider. And open banking is relatively consistent. Now, the the one thing that we did notice in the EU is that they they really don't have the concept of the emerging and specialized data. They don't see that as something separate from what we would call alternative data. And this would be like super app data, any kind of behavioral data, Internet of Things, IoT, wearable data, and and things like that. So they really think in terms of uh three categories, the traditional alternative and then open banking data. For uh AMIA um that's that's where obviously things is maybe as you go further east, I guess, or further west, this is seen as as being uh a little more simplified. And they they really just deal in terms of the traditional data, which which is common across the world, common definition, and then alternative data. A an established open banking framework like the UK has had for years, and and the US is really kind of bringing up on all cylinders right now. That that does not yet exist, so in the future there may be this category, but we really just see it. The data that they have used and the data that they look to use in the future really all just falls under that alternative data bucket. And lastly, APAC, which is you know, in many ways, just as making the same kind of inroads into you know quality of credit management, but doing it in some different ways. But much like uh Amina, it uh it does not really break out into the open banking and emerging specialized data, and uh they're they see everything falling into one of the two buckets of traditional and and alternative. So the I would say the framework holds up quite well across you know across the globe. But it's it's really a question of some of the categorizations don't apply, but but the definitions of those that do apply, they work and they work quite well. So that's why I really think of this as a you know as a framework that uh will work uh globally. So, you know, let's take a closer look at those categories, Kevin. Um would you would you first walk us through traditional credit credit data versus alternative credit data?

Emerging And Specialized Data Boundaries

SPEAKER_03

Yeah, you know, good question, Stuart. Traditional data, and again, I'm I'm gonna try and be mindful of the different regions we're covering here, and I'll just focus on the US. We already gave maybe data source specific uh description as we talk about things like you know, trade line data, some inquiry data, some public record data fitting this category. But sometimes from an innovation and adoption standpoint, it might just be easy enough to say if you have used a certain data signal for longer than 10, no more than 15 years, it is almost certainly traditional data, right? And again, I will tell you in my time here, I have worked with four or five organizations that had an organizational goal to move away from traditional data for any variety of reasons. They were going to make a big marketing push that they don't use these kinds of insights. They thought they had the ability to build better products, things of that nature. But 99% of uh lenders that I've worked with in my career um do not view ironically, based on the uh kind of industry terminology, alternative data as an alternative to traditional data, right? They think of it as a supplement, and that traditional data is something that will always have a critical role in underwriting processes. It's just not something that provides those competitive advantages. It's not something that unlocks either business opportunity or kind of mission-driven goals around financial inclusion. It has limitations, and that's why it is often in need of supplement. Alternative data, then, and the same can be said for open banking data, does present an opportunity to gain those competitive advantages and those additional insights, which can first and foremost evaluate people who don't have a presence in traditional data, uh, might evaluate and identify consumers who are underestimated by traditional data. And we know particularly with younger consumers and consumers who are recovering from situations which have damaged their traditional credit profile are very common. But then the last thing from a benefit standpoint, before I get into some examples, the power users of alternative data in the United States are ones that have realized that these benefits are not unique and confined, essentially, to consumer populations, that they are either actively declining today or whom are only eligible for their most limited and restricted offerings. Um, you know, in the auto lending space in the United States, there are a number of the largest players in the alternative data landscape that are actively having success, outbidding their peers and increasing their book-to-look rates without growing their loss rates through the use of alternative data. Because the game here is essentially that you are looking at a puzzle that has had notable pieces popped out of it over the last 15 to 20 years. How do you start popping those pieces back into place to get that complete picture of a consumer so you can offer them the best possible, most competitive terms, all while kind of adhering to your desire to limit risk exposure? That's all going to be unlocked through alternative data. So we could boil it down as simply to traditional data is what you've been using for decades. Alternative data is what you have either recently started adopting or considering adopting to expand that insight. But there is, as I just covered, a little more nuance than that, Stuart.

SPEAKER_02

Yeah, it yeah, it is. When you dive down deep into the details, it kind of self-evident the need for some organization of the information. So Neil, we're uh seeing open banking, you know, say gaining. I mean, it's gained traction in the UK and the EU for that standpoint. So, but how do you see the adoption rate outlook uh going forward of this data?

UK Nuances: Public Info, CATO, Payroll

SPEAKER_01

Yeah, yeah. So before I get on to that, I think this is perhaps the most important clarification we'd like to make in the UK. Um, the distinction between alternative credit data and open banking. Um, yeah, as Kevin's already explained, there are significant differences between the two. And I guess our role and partly, you know, this podcast is to continue to educate the market about these differences and to some extent help lenders understand the respective benefits of each of them. But in terms of adoption, um, I think it's fair to say that open banking has undertook a slow start in the UK, but it is gaining traction very quickly now. Um, the reason for that is the growth of what is primarily a payment transaction use case rather than its application in credit services, like perhaps the technology was originally envisioned to support. Nevertheless, in July this year, it was reported that there are over 50 million users of open banking, and that is, as I say, primarily a result of the growth in the payments use case, and particularly the growth of what's known as variable recurring payments. This is uh a significant innovation for open banking, um and it enables um flexible subscriptions, easier rentals, and uh um more efficient invoice payments. You can uh you can understand why you know the the the technology is being utilized in that way and it is growing because of that. The credit use case is is is more challenging, it's not been as adopted as quickly as many had anticipated it would be. And I think the average consumer that's seeking a loan is still a bit sceptical about using an open banking approach to secure their finance. Nevertheless, I believe there is a role for open banking in the credit ecosystem, despite all of this. Um and I think it's largely reliant now on providers and policy makers helping the market and more importantly the consumer demystify this journey that they would undertake to benefit from this type of service. We do have some regulation and policy makers that um you know are driving this forward in the UK. And we have something called the Data Act, which was passed in uh June this year, and that in itself should help the industry realise the potential of um what is now termed smart data and I guess a broader open finance set of use cases, and you know, if we can do that, I think the technology uh applied to more use cases across more industries than perhaps it was envisioned to support originally with the appropriate safeguards, I may add, is uh only going to sort of strengthen um its position in the credit space going forward. So although perhaps um you know not not where we thought we would be when open banking first launched in the UK, there is still opportunity there for it to be leveraged.

SPEAKER_02

Yeah, yeah, it's um it's funny how the use of the data is there, but it's kind of growing in different ways than the other markets, you know. As you said, UK payments and and and uh US it's being used in payments, but it's definitely a credit focused product, and that's that's where we're gonna see the biggest lift. Um so you know, you know, most everything that we we've said kind of you know uh uh applies in terms of um you know where it's where the the classifications work and um and where they and where they don't really apply. But um we see that those markets as quickly as it seems that is that the data is evolving here, uh particularly in APAC, the evolution uh is running at light speed. So, you know, I I think um uh over time and relatively short period of time, we'll need revisit uh because it is um the market there and and and me as well, uh, is uh changing rapidly. I still feel that the uh kind of the the four categories framework will hold up, but it's a question of uh kind of how the how the local markets um view the use and and what data belongs where. So, okay, that's it's it's time to wrap it up here. We're running out of time. So really interesting conversation today. And uh and like I said, it you know, day one, I I was glad that I was not the only one seeing this. But as we wrap up and kind of kind of ruminate on what we discussed, I'd like to hear uh a key takeaway uh from both of you that that resonates in your part of the world. Kevin, I'll start with you.

Regional Views: EU, EMEA, APAC

SPEAKER_03

That's that's a really great way to put a cap on the steward. Um I'll go with this. Clear terminology is a critical tool needed to move aside so much of the fear, uncertainty, and doubt that often stands between risk managers and the data they need to meet their credit risk objectives. We've already kind of hit on this theme throughout the call, but depending on your region, innovation isn't necessarily something that comes comfortably to those of us in the credit risk space because of the highly regulated aspect of it. And frankly, how much financial risk there is in getting things wrong. Making sure that once we do get all the right boxes checked on a certain data source to know that it is additive, we can use it from a regulatory perspective, we think it's going to stand the test of time and not change and become less stable once it's in our underwriting policies. We need to be able to speak about that clearly and not get it kind of conflated and confused with other data sources that maybe haven't met those tests yet. Um, so when we get this lexicon right, and I think Stuart, you yourself and the data stream have been instrumental in us doing that here, we really pave a much smoother path to to innovation and adoption. Um I'm I'm excited that we've got that.

SPEAKER_02

Okay, great. Yeah, me as well. And it was uh it was definitely the the right thing at the right time. Yeah, so so Neil, we'd like to hear your takeaway here as as it applies to your market.

SPEAKER_01

Yeah, well, it's it's very similar to Kevin's, to be honest, Stuart. You know, I I agree with with everything Kevin has said about alternative data and you know the the requirement, you know, for us in the UK to help our market understand it better. It's perhaps even more important for the UK because we're so new to alternative credit data, generally speaking. But I think we can, you know, we can utilize the learnings from the US and how they've been operating successfully in that market for a number of years. And I think you know, the clarity of the terminology is becoming clearer. Um, you know, again, I reference the um the consumer lending research that we do every year, and you can see by the results of those, you know, those surveys from you know 150 UK banks and lenders, you know, that the the adoption and the understanding of the concept of alternative data is absolutely becoming more clearer to them as we move forwards. Um we just need to continue on this journey that we're on um and continue to you know educate and inform and provide the market with the right kind of insights and help them understand um how they can utilize this sort of data and ultimately make better decisions in and amongst all of the other options that are in the market available to them as well.

SPEAKER_02

Yeah, and and everything you said really applies to all markets. So I I think that's a wrap. Uh Kevin, I'll toss it back over to you.

SPEAKER_03

Well, uh Stuart, I'll just say thanks for moderating such a fantastic conversation today. Uh and a big thank you, Neil, uh, for giving your time to join us as well and sharing such valuable insights. I think uh, you know, we we can all sometimes get tunnel fun uh kind of tunnel vision on our regions where we tend to spend the most time expanding outward and and discussing this issue more broadly uh geographically is is so, so helpful. If you'd like to learn more about alternative data from LexisNexis Risk Solutions or access our defining alternative data resources, which Stuart and I have referenced throughout the call, visit risk.lexisnexis.com forward slash defining dash alt a L T dash data. We'll return with season three of Credit and Focus, bringing you fresh perspectives, new. Topics across the credit lifecycle and expert voices you won't want to miss. In the meantime, catch up on any episodes from season two. Take care.