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TRANSCRIPT:

0:00:00:115 

Krupa: Hello everybody, and welcome to our Webinar: Beyond Compliance - Transforming Client Experience Through Intelligent Data. My name is Krupa Malawade, and I am the Content Manager here at FraudNet. 

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00:00:11.235 

Krupa: We're very excited to have you here for this discussion on how to leverage your data intelligently to improve the client experience for business customers across their lifecycle. We'll delve into some of the issues with gathering and storing data, how this can cause friction, and also how to address it. But first, let me introduce you to our speakers for today. 

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00:00:28.685

Krupa: The first is Kevin Shine, Head of Sales at FraudNet, where he leads go-to-market efforts and client engagement for the company's AI-native fraud and risk intelligence platform. Kevin brings a wealth of experience at the intersection of finance, banking, and technology, with a track record in helping companies modernize their compliance and risk infrastructure through scalable ROI and data-driven solutions. 

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00:00:51.685 

Krupa: Also with us today is Yetunde Ekunwe, who currently serves as a Product Initiatives Executive within Treasury Sales and Services at Bank of America, where she leads strategic business initiatives and drives financial and operating plans across the bank's Global Payment Solutions division. Her work helps power platforms like CashPro, enabling individuals, corporates, and public sector clients to move money efficiently and securely around the world. Both of our speakers have a great deal of expertise in this topic, so I believe our audience is going to take away a lot from this discussion. 

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00:01:22.875 

Krupa: Yetunde, Kevin, anything before we get started and get to housekeeping? 

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00:01:28.095 

Kevin: No, just thank you for the invitation, today, Krupa, and looking forward to speaking with you and Yetunde. 

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00:01:35.715 

Yetunde: Likewise, echoing the same thing from Kevin, and looking forward to hearing our dialogue. I can't wait to impart some knowledge in folks. 

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00:01:43.325 

Krupa: Thank you both so much. All right. With that, I will turn into some housekeeping. 

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00:01:49.545 

Krupa: First of all, all attendees are in listen-only mode, but we encourage you to submit questions as they come up throughout the webinar. There's a Q&A button right on that bottom bar, on the webinar screen. We'll address them at the end if we have time. You can use the Q&A button to submit your question at any time during the webinar. And we'll also have a reminder towards the end of our main discussion today. 

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00:02:11.345

Krupa: All attendees will receive a link to the recording via email after the event wraps up. So if you need to drop off at any time, don't worry, you will get the recording. But I would like to also say that if you have to leave the webinar at any time, I do urge you to book a meeting with one of our solutions consultants, just so you can get a deeper look into our platform. A lot of the things that we cover today, you might wanna get a little bit more insight into, maybe some more recommendations. Please book a meeting with one of our solutions consultants to get more, and especially what we can specifically do for your business as every business is unique. With that, let's get into it. 

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00:02:48.085

Krupa: To start, let's dive into customer experience. For any business, getting customers and retaining them is obviously their ultimate goal - customers equals revenue, but a huge part of getting those customers and retaining them is customer experience and satisfaction. I'll ask you, Kevin, first, in your experience, what is your assessment of the current state of things as it pertains to client experience at legacy banks and financial services for those business customers? 

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00:03:14.635 

Kevin: Yeah, I mean, slow and manual, I think, is the best way to summarize it, right? I think that's where we see, and at least in the engagement that we have with our prospects and clients, where we see some of the bigger bottlenecks. It's not that our clients don't care about their clients or their customers, it's just that they're typically working with outdated systems, legacy integrations, fragmented workflows that just make it harder to move quickly. 

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00:03:39.985 

Kevin: It's a struggle that we see across our customer base - across our customer base globally. So, this is not necessarily a banking issue, a European issue, or a LatAm issue. It's really trying to figure out the processes around data collection, data storage, reusability of that data more effectively. Institutions are capturing the right information. They're just lacking that overall coherent strategy. Thus, that leads to data siloing, process redundancies. It just leads to that clunky, disjointed experience by the customer. And I think on the inverse of that is in the consumer-facing side of the world. I think this reflection and today's conversation is namely on more of a B2B type activity, Bank of America trying to find business clients. 

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00:04:26.515 

Kevin: FraudNet, we work with businesses generally trying to acquire other businesses, as customers. That consumer expectation is more of that real-time experience, full stop. We're used to interacting with apps with highly responsive websites, opening up accounts, sending money, all of this stuff is happening in real-time. So that consumer who's representing those businesses on those day-to-day engagements is expecting more of that consumer, that digitally native feel, maybe more of that real-time experience, which again, I think is sort of underpinning a lot of what we're talking about today. How do we maybe learn from some of those consumer experiences and those best practices and infuse those into more of a traditional B2B model? 

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00:05:17.315 

Krupa: Yeah, you're absolutely right. You mentioned data collection a little bit earlier. I think a very core section of the client experience is onboarding. That's where all that data collection really happens for the first time. So in that onboarding sphere, I know Yetunde, you have quite a bit of experience in that sphere, in that portion of the client experience. A lot of your current work centers around onboarding - what issues do you currently see with the onboarding process for those business customers? 

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00:05:46.055 

Yetunde: I would say a core consideration when it comes to onboarding and intake of a new client is time to transact. That is a critical KPI that we measure. The faster that our clients can transact, the sooner that we can generate and hit bottom-line impact. So when I think about that, the relationship that's established at the very onset, and then basically when we start - when you open a relationship, the onset, it's basically called a mandate that the client said, yes, we wanna go. And from the time that they start till the time that they actually can transact, can take a while. 

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00:06:23.155 

Yetunde: Part of the challenge is that we have documents that we utilize or a manual entry entered into systems in order to be able to service the client for what products and services they're requiring. And so that sort of lack there of capability to be digital prevents them from getting a much more enhanced client experience overall. Then unfortunately, with all that back and forth of manual processes and manually keying, the QA process sort of evidences that and there's a lot of back and forth and interactions in manual touches. I think on average, the industry average we've seen is about 6 to 10 times. So getting rid of all the manual interaction is really critical to improving that transaction time for our client in order to obviously better service them. 

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00:07:17.835 

Krupa: And just to clarify, time to transact in this scenario means the time between, let's say, a closed deal with a client and their actual go-live, correct? 

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00:07:29.045 

Yetunde: Effectively, it's the time when this client says, “We would like to move forward with bank,” with whichever bank that is, and to the time when they actually are able to utilize the product or service that's provided. 

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00:07:44.355 

Kevin: Hopefully, when value is being unlocked in that relationship, right? I mean, I think that's another way to look at it. I think just building upon that a little bit, summarizing what you're, you're saying there at the end there, Yetunde, is just manual process fatigue. 

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00:07:57.845 

Kevin: When we go under the hood and start to investigate, how our clients or prospective clients have their systems operated - and again, for the benefit of the audience here, as a service provider to organizations like Bank of America, we're trying to help them utilize technology to solve some of these: reduce that manual process fatigue, reduce fragmentation, try to de-silo data sets, just automate is really part of what we're after as an organization, and how do we adapt technology to do that? Really trying to help them eliminate some of those inefficiencies, which can lead to people being demoralized, quite honestly. Leads to burnout, leads to error rates, sort of, there's a whole set of downstream issues when you're heavily reliant upon manual processes from that data collection to that validation and that input. 

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00:08:52.805 

Kevin: I think on the client side, it's just as serious. What we're trying to help organizations do is reduce having to ask their clients or customers for the same information multiple times. So being very thoughtful when we are, Krupa, to your point, during that onboarding phase, when there is a lot of data acquisition happening, or Yetunde, as you put it, also a lot of manual key entry and validation, and just making sure that everything looks okay. We'd like to ideally measure twice, ask once, or measure twice, cut once, as we say internally here at FraudNet. Just being very thoughtful around how we're approaching these processes to make sure that the ask of the customer, the client is as impactful as possible to reduce that time to transact or that time to value. 

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00:09:46.045 

Kevin: Fundamentally, this isn't a data problem. Our clients are collecting the data. Again, it's - foundationally, where we see the biggest issues is data organization, data labeling, understanding the workflows - once you've collected that data, how do you streamline the usability of that information. It’s just a core tenet of some of those problems that continue to underpin a less-than-optimal new client onboarding experience or workflow. 

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00:10:19.635 

Krupa: And from a - just to kind of like wrap up what you guys have been saying about the onboarding process, these disparate processes, data mismanagement. As a result, the customer, the client, just goes through a less efficient onboarding process. That's - if you are an end consumer to any business and you shop at let's say, any commerce website, and that time to transact takes too long, you are more likely to abandon cart and move on to the faster, more orchestrated solution. That is, of course, true on the business client side of things as well, right? 

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00:10:56.605 

Yetunde: Oh, absolutely. And unfortunately, with the consumer side, you talk about abandoning that is a very quick decision with the client side. Oh, it's felt - the impact is even harder. And so, there's a lot of trying to apologize and sort of rewrite wrongs, but by the time that they do wanna abandon, it's too late. Yeah. 

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00:11:17.845 

Krupa: And it's so much lost potential revenue, lost reputation in the sphere of things, because these clients talk to each other, right? 

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00:11:26.535

Yetunde: Exactly

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00:11:29.955 

Krupa: It's just something that has to be addressed. So with that, that tees me up to our next portion of discussion, which is, how do we actually solve for these issues? How do you address these inefficiencies, these silos? I'll point that to, I think Yetunde first, because you've been working on that quite a bit in your current role. 

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00:11:49.475 

Yetunde: Absolutely. I mean, I think just as a first step, you need to understand: what is the problem you're trying to solve. And based on understanding the problem you're trying to solve, which we obviously just articulated, then you have to define goals to solve them. As a business, and then making sure that there's consistency across the enterprise. And defining very clear objectives that will drive the outcomes you set forth. So, a lot of those outcomes have to be measured from a KPI perspective or whatever particular metrics make the most sense to ensure you are indeed setting, executing per what you've actually put forth. 

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00:12:27.705 

Yetunde: Obviously, time to transact we've just talked about is a key core KPI, but there's so many nuances and innuendos that it takes to get there. So, process is a really critical journey in understanding that. And then once you understand currency process, you can sort of figure out what are those nuances and the gaps that we need to plug in order to define what future state will look like. And then understanding the right tools or potential digital tools, really - resources that you require in order to get there. And then, potentially, how does that data feed into it. When it comes to you take this sort of clunky data that sits on a document to data that you wanna now sort of presale on an ongoing basis through a systematic tool, creating that sort of future state, and then defining and putting the right resources to execute on it, and then see it play out. 

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00:13:20.245 

Yetunde: I always believe in the concept of a POC, a proof of concept, to test it out, test the water so that you can potentially scale, but obviously you have to get all the right parties involved, risk, compliance, all the people to ensure that we're making the right decisions along the journey to get to that outcome that we intend to get to. And of course, ensuring and pressure test it with some of your clients that are the friendliest to enable that, to determine that they agree with the approach. 

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00:13:50.725 

Yetunde: And I think understanding what that client wants is really critical. We always think we do, but it's not always the case. So having those dialogues through surveys along that journey, along trying to accomplish those goals, are such an important piece in building out sort of that new framework or process, and collecting that information once and reusing it everywhere you potentially can. 

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00:14:16.485 

Yetunde: And fundamentally, once you're able to get there, I think that, while it'll be an ongoing process to continue to refine the data you're collecting, manage the data you're collecting, but if you can get to the point of minimal request of data from your client, that just automatically enhances that experience, enables an improved process, and time to transact. 

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00:14:39.355 

Kevin: Yeah. And we think of that as layering in transformation or digital transformation. And, and very much again, from the FraudNet side of the world, I think we're looking at this through the lens of fraud risk and compliance. I'm sure there's other tools to help with digital transformation more broadly. But in the context of client onboarding and where we sit in the world, I think the framework by which we look at a lot of this, this process: you don't necessarily need to ingest the whole apple, you could take bites of the apple over time. 

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00:15:11.085 

Kevin: A framework is, and we've talked about this already, but collection, validation, and verification of what you've collected. So I think on the collection side, how do you move from maybe more manual document collection, or manual inputs, into more of a digital data collection. Then that data can be organized and stored and reused, as you just said, Yetunde, more easily. Once that information's been collected, how do you move it more towards a digital validation and verification set of workflows as opposed to having a bunch of people involved in key-entering information and QAing information. 

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00:15:48.625 

Kevin: And at FraudNet, we're really good at orchestration, so when we think of validation and verification, it's how do we take that application information that was ingested on that client and then run things like a sanctions look up or prove out an address or understand business ownership, or are they a registered business, what's their tax status? There's a whole host of hundreds of different data inputs that we can bring in, in real-time, to validate the information that may have been collected at that point of application. 

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00:16:18.915 

Kevin: Then, from there, I think it's putting a decision framework around all of this. Again, further automation. How do you, you're Bank of America, and you have, I don't know what your, your, your client count is, but tens of thousands, hundreds of thousands, or millions of businesses that you service. How do you, yes, evaluate each one of them on their own merits, but put in place a decisioning methodology that can be replicated, can be repeatable, can be auditable. So now you've collected the information, you've validated and verified it. Now you quickly wanna put aside all of the applications of the clients that look good and meet your criteria, and then put into queues or various QA workflows for those that the data looks a little off and maybe does need a little TLC from a person, or you need to request some additional information as part of that vetting process. 

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00:17:10.185 

Kevin: And I think finally putting a nice reporting package around this, right, whether that be, as you mentioned earlier, Yetunde, measuring success against KPIs, like that's critical, reporting up to other stakeholders throughout the organization. And where we spend a lot of time with our clients is proving out these processes to their regulators. So when they do come in for an audit or they're in communication with that regulator, being able to show them the processes, the outputs, again, I think just having a broad framework around all of this, which has been critical. And that's ultimately shrinking that time to transact, as we've said; it reduces that manual effort. 

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00:17:51.265 

Kevin: It reduces that fatigue and really gives you that strong data foundation, that data structure to, I don't know if we'll capture it or cover it in great detail today, but you kind of knock out some of these pieces. Your ability to be AI-ready for future acceleration of your processes - you're in a very good position. A lot of times, people come to us saying, “Listen, we want to use AI. We want to use AI.” And you're like, ”Well, you're still collecting things manually.” You still have people that are fat-fingering in the wrong information. We've gotta work on that foundation before we can really turn on a whole host of productivity tools for you. Yeah. 

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00:18:33.415 

Krupa: And I wanna reiterate what I think you mentioned earlier about, understanding, first of all, what data you have, what goals you have, how you wanna solve for those goals, how you're gonna measure those goals. Because that, I think, is a key part before we even get into the digitization conversation, which then can allow you to incorporate an AI or automation much easier. So, I do want to kind of hone in on that automation portion or the orchestration side of things. Kevin, as solution provider, you've dealt with a lot of businesses who have dealt with this exact pain point - what outcomes have you seen as a result of doing this process? Of doing this goal-setting process, KPI setting process, and then, of course, orchestrating that data? 

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00:19:20.095 

Kevin: Sure, yeah. There’s two that come to mind straight away. Probably more recent ones that we've worked on over the past year. 

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00:19:28.045 

Kevin: One was a payments company that provides a certain technology capability to merchants, and they've got to onboard those merchants, acquire customers just like anyone else does. So with that process was very manual, very paper-based, very non-digital. That would take anywhere from four to six weeks to realize that time to transact. And that was a critical north star for this payment processor, because they want that merchant to be using their services as quickly as possible, because then they start generating those fees, delivering that value. 

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00:20:08.395 

Kevin: We've gotten that down to less than a day. So in our view, kind of still too long, or a couple days. We want to get that into hours or into as much real-time as possible. But when you're talking about something that historically took anywhere from 30 to 45 days, having that happen in a day or two is a huge improvement. So just compressing and think about that too from the merchant experience - that business can start utilizing a solution provider that they've identified as something that was gonna be critical to their success, in this case, accepting payments from their customers inside of a store or online, or whatever that may be. 

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00:20:45.805 

Kevin: Another one was they were a bit more digitized. They, I think, had solved some of the application onboarding process. That being said, some of their operational response to all of that just led to a lot of reviews, a lot of alerts or reviews. Looking at this application information in certain instances, looking at transaction information, we were able to reduce their alert queues by 93%. So you're talking about just dramatic operational efficiencies. So less in that use case about trying to shrink that time to transact. It was more, how can we help them more quickly respond to their customers. So, sort of two examples there where you're probably talking 90 plus percent in both cases, improvements in core KPIs. 

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00:21:41.045 

Kevin: I think another comment beyond that as well is when we think about some of the benefits beyond these onboarding use cases, or this alert fatigue use case. This is actually tied to one of these two provider, two clients of ours, excuse me. Out of the data that we looked at on their behalf, a little bit more downstream of the onboarding, once we were also monitoring all of the money moving in and out of their accounts, looking at the payment data, we were able to identify upsell opportunities to their customers. So they onboarded the customer with one sort of perspective on how this customer was going to behave or how this client of theirs was going to behave. Their behavior started exhibiting other characteristics. And that led to, maybe, a repackaging of the products and services that they provided them, which I think was really interesting, very helpful to the business development side of the house. 

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00:22:40.465 

Kevin: Another one, we built out for the same client. We built out a propensity-to-churn model. So as we were looking, you onboard thousands of merchants a month utilizing your payment processing rail to accept payments, and you start to see behavior going the other way, sort of slowing down, which is indicative that maybe they're shopping for a competitor, right? So now we have a feed that goes into their customer success team to reach out or their account management team to reach out, to potentially block that churn. 

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00:23:11.705 

Kevin: And then, I would be remiss without touching upon the fraud prevention side of things here at FraudNet, again, foundationally, if the program that you have in place to onboard new clients is in good shape and it's as real-time as possible, the likelihood of having fraud downstream goes down dramatically. So you're bringing in good clients, and then again, as well as you're looking at the behaviors exhibited by those clients once they're onboarded, our ability to help our clients identify potentially fraudulent behavior just improves full stop. Less garbage in sort of less garbage, downstream. 

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00:23:54.355 

Yetunde: That's great. I think that's awesome. Thanks for those examples. I think it's the proof is in the pudding, and I think when it comes down to it, downstream is where you really have to make the biggest impact. So it has to be clean, it has to be comprehensive, and it has to be well organized. I love how you articulated the very specific metrics that were captured, and unfortunately, as a result of some of these banks being so large and over time have come with all this complexity, it takes that one step at a time to kind of get there. 

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00:24:27.675 

Yetunde: The quicker we can get to clean data, the easier it is to leverage the specific tools around AI or really whatever is revolutionary to be much more thoughtful about how we engage our clients and how we service them. There's so much that's out there that's innovative. It's hard to use them when there's so much to unravel, uncover, and clean. So, I think it's great that there's a lot of companies that are out there. 

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00:24:59.575 

Yetunde: But I will admit that what I find sometimes, that companies that are able to get over that hump tend to be smaller and don't have as much complexity. So I always ask the question, well, top-tier bank, or are we talking regional or whatever, or just whatever industry. And I think that translates into some of that, that too-big-to-fail model is truly a fundamental thing where complexity is unfortunately the name of the game. So unraveling - I call it the spaghetti. I was talking about spaghetti yesterday - we're having a conversation around just data that we discovered, which is a very simple thing, a client name captured 20 million ways and 20 million times, obviously, there's a very clear number there, and it was just unnecessary because you've got disparate systems over time that don't always talk to each other. So clean is important. So the cleaner we can get, the sooner we can utilize those innovative tools, 

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00:26:00.025 

Kevin: Yeah. Whether it's, whether it's AI now or something we don't even know about. Even at the speed at which AI is moving at Yetunde, the adaptation that we've applied that internally or in certain client use cases, what I would've told you three months ago is probably different than what I'm telling people today or, or how we're using tools internally. I think we're really, how do we make sure that FraudNet as a company, our clients by extension, are in a position to try out or incorporate the next latest set of tools, which really I think, we believe, will be force multipliers. 

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00:26:35.945 

Kevin: Helping our clients grow more quickly, maybe with less human-based resources. It's not necessary, I think humans sort of in the loop, being critical to this is still gonna be here, for some time, but it's, how do we position everyone to be able to take advantage of the next greatest thing coming down the road, Krupa. 

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00:26:55.925 

Yetunde: Yeah, for sure. One thing I would just add to that, I think part of the challenge of the fact that it's moving so fast, I mean, look at Bitcoin, a great example, because of the highly regulated industry that financial services fits in, it becomes challenging to say, yeah, let's jump to that next new thing until it's truly vetted. And obviously, we've seen in the past with a lot of these AI solutions that do have some challenges that still need to be worked through. So jumping to the first thing is really never going to be the play for a lot of financial services firms. 

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00:27:28.405 

Yetunde: It's more about understanding how it works, understanding the implications behind it, and ensuring that law, rule, reg, is followed and executed appropriately to ensure that we comply with so when auditors come knocking, that all things are above board. So it is challenging to sort of move with that speed of light, that I would say that, as you highlighted, AI tends to move, or whatever tools. So we have to be thoughtful, we have to be nimble, and it comes with the territory of the industry. 

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00:28:01.585 

Krupa: Yeah, you're absolutely correct. And, just to reiterate something that Kevin, you mentioned earlier, garbage in, garbage out, right? Especially when it comes to AI implementation. I know at the beginning of this conversation, we centered it around onboarding, the issues with onboarding, data collection. 

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00:28:19.575 

Krupa: But I think something I do wanna highlight of what you guys said is that downstream impact. If you address the onboarding, if you address what data you're collecting at the outset, how you're labeling it, what goals you are using that data to kind of measure against, everything else downstream just becomes so much easier. It becomes, it's really kind of plain and simple. The better you set up kind of the architecture, the framework, as to how you are collecting, validating, using this data, the better outcome you're just gonna have. 

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00:28:58.505 

Krupa: And I just do really wanna highlight that portion, because it's not just solving your onboarding woes, it's not just streamlining that process. It is also effectively giving you more insight into your business operations. It is also giving you the framework, the baseline, to then use these tools. And, anybody who's used any LLM knows, it's all about what information you put into it. It's all about the prompt. It's all about what context you give it. Otherwise, you're just gonna get a junk answer. 

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00:29:31.465 

Krupa: We all know that ChatGPT has a propensity to hallucinate. It does not validate because a lot of information is going in there, and it's not getting tagged, it's not getting validated, it's not getting double-checked. So that's why that human in the loop is there, of course, in that scenario, but it is also true in the business operations side of things to do that, just to be frank with it. 

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00:29:56.225 

Krupa: I do wanna thank you guys for all of the great insights. We have reached the end of our main discussion here. More and more industries are turning to AI automation to improve their internal processes. So, I think this is a very good conversation to have. AI is in the zeitgeist. LLMs, machine learning are all in the zeitgeist right now. So, especially as more businesses kind of implement this, this is very much a core piece of the discussion. We do have some questions coming in from the Q&A. We'll turn to that in just a second. But before we do, any final takeaways either of you have that you want to make sure our attendees listen to and kind of like really keep in mind today? 

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00:30:45.885 

Yetunde: I honestly, I was gonna say, keep it really simple and say slow, but steady.

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00:30:51.145 

Kevin: Yeah. I think that's accurate, especially when you're in these highly regulated entities, such as, you find yourself at Bank of America. I would also add… don't hesitate to talk to your peers, talk to service providers. We're all dealing with the same set of issues. I know that at times it can feel a bit insular, you're working inside of your own organization. 

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00:31:19.495 

Kevin: But there are - like we're trying to impart some wisdom here today - these are things that many of us, as software companies, as service providers, are dealing with day in and day out. I have probably flavors of the same conversation with different companies all over the world every single day. So, you're not alone in this. Some of this we have figured out, and companies like ourselves have built technology to help solve some of these problems. 

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00:31:47.695 

Kevin: Increasingly organizationally, we also interact with regulators as well to make sure that the guidance that we're providing, the tools and technologies that we're building, to an earlier point that you made Yetunde, as it relates to regulated entities having to consider maybe slowing down a bit because of the regulator, we are being mindful of what their opinions are and what those approvals may look like. So that when we are making these recommendations that there has been some pre-screening or pre-assessment by regulators. But I would say you're not alone in this. Reach out to your networks, reach out to organizations like ourselves. 

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00:32:31.525 

Krupa: You're absolutely correct, Kevin. With that, we will turn to our Q&A. This is one of your opportunities to ask questions of your peers. If you have any questions, as a reminder, please use the Q&A button right at the bottom of your screen to submit your questions. If we don't get to your question today, we will make sure to follow up via email. So please make sure to use that Q&A function. Even if we don't answer your question today, we will absolutely follow up. 

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00:32:56.465 

Krupa: So the first question comes in through email, and it actually, I think, comes - it logically follows your statement, Yetunde, on slow and steady. What's the actual implementation timeline for this kind of framework overhaul and optimization process? What does that implementation timeline look like? 

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00:33:19.385 

Yetunde: That's a great question. A long time.

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00:33:25.965 

Krupa: Slow and steady! 

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00:33:29.995 

Yetunde: Realistically, obviously, it's not an overnight thing. It's going to take some buy-in. Clear agreement on how you wanna approach everything. We talked about the very specific steps you have to take. There's a lot of people that are involved in making the decisions to move it forward, and also getting it actually executed. And then when you think about large companies, large financial institutions that have different complexities, you have to really factor that in. 

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00:33:57.385 

Yetunde: So on average, I would say it's minimum five years to really, truly have transformed to the point Kevin made earlier, when you're talking about the large institutions. I think if you can start to slowly see impact over, let's call it minimum 15 to 24 month period, it gives the momentum to move it forward and continue the path if it's laid right. If your objectives are defined, if you are, you have the right buy-in, and the most important - money. So without money, you go nowhere. You can have the best and greatest ideas and the brightest, and it will go nowhere with no buy-in, and especially no funding. So fundamentally, I think that with the right investment, one can get there, but it's not overnight. 

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00:34:47.195 

Kevin: Yeah, I was at a bank's office yesterday, and we're probably year two of our dating relationship, Yetunde. It's not Bank of America; it's a different bank. But we're at the point now where we've built out the business case with them, and it's budget allocation time, right? They're putting the plan out there, seeking that money. We will earmark most of ‘26 for implementation, onboarding, user training. Not that it takes that long, it's just there's competing projects, competing priorities, where are you at in that queue. Production will be somewhere in the latter half of next year, if not possibly into early ‘27. So, if you're talking, we're 18 months in now, three and a half to four years start-to-finish. 

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00:35:37.315 

Kevin: Like, my boss doesn't wanna hear that, but that's the reality for some of these things, that it just takes that time. Others we've been able, maybe it was a more acute pain that they needed solved quickly, and there was a lot of organizational momentum to solve it. You can get something done in 12 months and then start to layer on, sort of fully expand and fully engage with that organization year after year after year. There's some clients that we have that we're probably in year six of our relationship, and we're constantly sort of adding to the value that we're providing them. And to your point, Yetunde, it's just because that trust has been built. The organization has seen value from, not only what the executive who sponsored the project identified as a problem, but the solution that he or she has brought to the table has proved to be fruitful. And then you get more trust, more money to continue to solve more problems. 

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00:36:33.445 

Yetunde: Trust equals money. I like that. 

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00:36:37.495 

Krupa: And going back to the process, just as a follow-up here, from your perspective, either of you can answer this, do you believe this process, the framework, this timeline, this implementation, do you believe it's optional in any way? Or is this ultimately necessary? The way I think about it is it's not if you do it, it's when, how, and who with.

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00:37:02.995 

Yetunde: Absolutely. When you do it. It's an absolute must because if you don't, you fall behind. And when you fall behind, you become obsolete. And when you become obsolete, it's a problem. So, if you want to compete in the market, it must be done. If you don't, then you will find out. 

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00:37:25.335 

Krupa: All right. Thank you so much for that. Moving on to our next question. How do we know if our orchestration efforts are actually working? 

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00:37:34.235 

Kevin: I'll tackle that one, great question. I think it foundationally goes back to the very beginning of today's conversation, where we talked about outlining goals and objectives. What problem are you trying to solve? Why are you trying to solve it? Then we'll arrive at a how, but first you need to be able to - I think we threw out very early in this conversation a time to transact. Like that's a key north star, and there's a before and after that you can measure in terms of whether or not this whole process, this digitization, digital transformation or orchestration, or various other buzzwords you want to use there is working. 

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00:38:09.965 

Kevin: I think operationally, when we talk to a lot of our clients and they're responding to the fraud, risk, and compliance systems that we help them implement, it has to do with operational performance. Are their team, are we helping them do more with less? Are we reducing their false positive rates, and their review rates, or their QA rates? I think that's, that's always important. I think that, on top of that, it's - can they effectively grow their business without having to scale up and hire a bunch of people to fill in those gaps, right? 

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00:38:43.665 

Kevin: So again, if you're retiling technology, you're going through some sort of transformation. Sure, part of that goal is delivering value more quickly to the client for that delightful experience to capture more value in the form of revenue. But can you more aggressively go out and potentially open up your marketing spigot to acquire even more customers? 

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00:39:07.665 

Kevin: I can think in some instances with our clients, we've given the Chief Risk Officer control over his or her risk portfolio, which then allowed them to go a little bit further out on the risk curve in terms of what type of new clients they're trying to acquire, that maybe historically they didn't. They were more of a lower risk business, and now, with the right boarding, and monitoring, and the right reporting, they can proactively go out and expand their marketing funnel, if you will, to bring in customer segments that maybe historically they didn’t. And maybe some of those more risky customers, while presenting more risk, can be more profitable to them if you've got the right controls in place. 

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00:39:53.975 

Yetunde: Yeah. And, definitely, I think stakeholders have to be satisfied as well to ensure that it's actually impacting their bottom line, and that translates into actual success. So, I think all the different work, all the different solutions as Kevin just mentioned, orchestrated appropriately and simultaneously, gives satisfaction to those that are involved and those that are actually invested in it. And so I fully agree, I think that understanding of dollar for value is really critical when you try to understand whether or not it's actually working. So, it's really important to consider. 

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00:40:41.795 

Krupa: Thank you guys so much. One more question. We have a couple more coming in, but I think we do have to wrap up in just a bit, so this will be our last question. We have regulatory and risk management requirements that require different teams to verify information independently. How do you balance orchestration with team-specific requirements around verification? 

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00:41:05.275 

Kevin: We've tackled that through technology. I think, again, just reflecting upon this conversation earlier, was very much grounded in trying to design and create operational efficiencies with the onboarding. I think that's a critical component where there are multiple stakeholders looking at information historically in silos, and can you break that down. 

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00:41:28.665 

Kevin: We now -  I'm thinking of two use cases that come to mind where we not only broke those silos down, but also on the ongoing monitoring, where we have at least three independent teams looking at the same data, to your question, Krupa, so it's the same data coming in. They all have their own different points of view. There's a financial crime team, there's a compliance team, there's an underwriting team, all looking at this data to the best benefit of the company that they collectively work for. All protecting the company from different risks that the data presents. But we've been able to bring that into one singular platform, allowing them to have their independent workspaces, allowing them to collaborate when necessary. 

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00:42:17.585 

Kevin: This sounds like, duh, why wouldn't it be this way? You'd be surprised with how siloed these activities have been historically, or the collaboration would happen offline as opposed to being done online or in-line, where they can share notations, share documentation, send information back and forth to each other inside of a platform that is fully auditable, which is very helpful for a lot of our clients. 

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00:42:45.225 

Kevin: So again, it's not only trying to de-silo and help with operations in one specific activity like onboarding, but then there is, okay, now the customer or the client is a client and they're doing things inside of our systems, and they're moving money in and out of their account and they're doing things. As many of you know, there's monitoring and rescreening, and all sorts of things that happen downstream, so it's trying to bring all of those capabilities into a single source of truth for a client. Give them truly enterprise-wide controls, give them enterprise-wide reporting. I think, give the regulator sightlines into… here's how we're engaging with this client from the very beginning, and here's how we're monitoring their compliance on an ongoing basis. 

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00:43:37.705 

Yetunde: Excellent. I think an example is obviously the client provides a tax ID, and that information remains in the system for every team that requires that ID to be leveraged. So rather than each individual reaching out to the client and saying, “Hey, send me that same information,” it's in this orchestrated system that allows for Compliance to verify that tax ID against an IRS database. Credit might verify it against a credit bureau, but then Fraud might just check it against their own fraud databases. 

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00:44:09.465 

Kevin: But all that information doesn't go back and forth to the client. It's on this orchestrated database and everyone can utilize it to accomplish the same goals simultaneously, different goals simultaneously, rather. And so, some of that data verification layer, and the customer only had to provide it once? Amazing! That would be nirvana, that would be awesome. So, it just goes back to the point that was made earlier. Too many teams, despite being organized separately in terms of business structure, might be using that same data point. It really would be truly a pinnacle of success as just a measure, in and of itself. 

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00:44:48.865 

Krupa: Yeah, and it goes back to something we said in the main discussion of “Collect data once, reuse that for all the use cases you need.” Collecting data and verifying data are distinct from each other. Collecting data - that can be repurposed to so many different lanes, but you don't have to continuously recollect that data. It's the same data! It's the same data point, right? 

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00:45:09.855 

Yetunde: It's such a simple thing, but yes. 

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00:45:11.975 

Krupa: Yeah, you’d think more companies would adopt this, but some legacy institutions are still catching up to what seems to be an obvious point. 

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00:45:21.885 

Yetunde: That's right. Hard is hard. Hard is hard, but we don't run away from the hard things. You gotta do the hard things and face it head-on. 

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00:45:30.025 

Krupa: Absolutely. With that, we do have to cut our discussion short. That's all the time we have for today. If we didn't get to your question, someone will reach out to answer you directly. We will respond to your Q&A. If you asked the question with your name, with the email associated with your Zoom account, we will respond to that Zoom account email. If you ask that question anonymously, much harder to find you. So, I would say, follow up. 

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00:45:57.245 

Krupa: With that, thank you for a great conversation. I think there are a lot of really great points here made about how can we reuse this data, how can we build that framework to make these onboarding processes so much faster, so much more efficient. I had a great time listening to both of your perspectives because we have compliance on one side, and also fraud on the other side. And both of you have dealt with these business clients, who have churned because of these outdated processes, very manual ones. So I learned a lot. I hope our audience learned a lot. 

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00:46:35.285 

Krupa: With that, I will wrap this up and kind of take us into our outro. I do want to thank everybody who joined us today. I'm going to share a slide in just a second with a link on the screen if you have any follow-up questions. If you would like to learn more about our solution, or if you would like to contact either of our speakers, there is a link in which you can do that. You can also respond to any of our marketing emails that we've sent you. I will pull that up on the screen in just a second. 

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00:47:06.245 

Krupa: But, before I do, I do want to thank our speakers for joining us today. Thank you so much, Yetunde, and thank you, Kevin, for joining us, for answering my questions, and for sharing your insights with this audience. Any last thoughts? 

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00:47:25.175 

Yetunde: Thank you for having me. Yeah, same. 

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00:47:28.125 

Kevin: Thank you, Krupa, for organizing today's event. Yetunde, thank you for joining us.

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00:47:34.665 

Yetunde: Thank you for your key insights, Kevin.

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00:47:37.285 

Kevin: If you listened in, we appreciate you taking some time out of your day.

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00:47:40.525 

Krupa: I just want to say a final thank you, have a good rest of your day, and we'll see you on the next one!