Why KYC Isn’t Enough

Thank you very much for sticking around. I know it's a late session. I'm very excited to be speaking to industry peers tonight around Y K Y X KYC. Isn't enough anymore. We are going to dive into the answer of that, but we are going to start with a quote of one of the genius minds where I think that really relates quite well to the issues that we see in the KYC market that we see as well in the global development of AML and fraud in general. So it's not just a big picture play anymore. You really need to be on top of the details.

You need to understand at the right time with the right data in the customer life cycle, what's actually going on and you need to start building a profile of your customer that allows to really have intelligent decisions along the way. I am not going to hit you over the head with too many numbers, but just a couple, which I think are really telling. So you see that 70% of the organizations in that survey in the post pandemic, which is a big statement, a reality actually are expecting the level of fraud to increase quite drastically over the next 12 month.

At the same time, you see a market where there's lots of opportunity. You see companies investing into anti fraud, technology upgrading, and as well, most of them already implementing anti-fraud services to make their life a little bit easier. So the big change for us as to what we've seen and for me as GM platforms and like previously in the previous life, being the CEO of foster, which is a data and identity orchestration hub, like we see the shift is really to a customer lifecycle management review of onboarding experiences.

So while you are actually onboarding the client, you're making sure that you already checking some of the key facts that you understand some of the data that the customer can deliver you throughout the onboarding, but that's not enough anymore. You need to go to the point of account management and as well, if the customer is starting to interact within your ecosystem, you will need to stay on top in regards to monitoring the behavior. But as well, everything that is data related to that customer. I think the question, however, still remains the same. We call it on our side is Bob really? Bob.

So can you trust that the person who's onboarding with you is actually who they say they are, is a suitable person to do business with now and ongoing. And I think the ongoing is really something that pushes you towards actually using the right data at the right time and making sure that you can ongoingly take intelligent decisions on this very consumer. So this one is a slide that's probably going to give you a little bit of headaches. It is quite messy, but there's a reason behind it. This is what we see in the market.

On the left hand side, you see a couple of use cases in this PA in this case, we took a payment service provider, but it could be any kind of digital player. It could be a marketplace, it could be a crypto exchange, or it could be even a, a neobank or other traditional finance institutions. So where you have a customer onboarding, you need the right data in order to be able to verify them. And currently this is involving a lot of different data integrations.

I think you see here from business to identity, to payment data that actually translates in the real world into having different integrations as well. Making sure that these integrations work as well together, as you can possibly have them work together. And in the middle, you see some of the key pain points. So we see that in the end, there's usually lots of multiple contract relationships that need to be upheld. You need to stay on top of your development team. There's always a backlog in development queue. So that is one that's actually bigger pain point than one might think.

And on top of that, this is something that's ever evolving. So if you look at, for example, a FinTech that is growing into new regions that is trying to gain new subsets of customers, you do not really have the time to catch up on launching new identity services on trying to make them part of the view of risk that you have. And on the right hand side, really the one big statement that I want to put in front of you is the fragmented view of risk.

So as funny as that sounds like with all of these different integrations, it's not uncommon that we still see Excel files from time to time that bring all that together. So teams are looking at their individual back office are looking at a, at a login that then brings them to fragmented report, but it's not something that's centralized. It's not something that is streamlined. And where we see the solution is really the next slide. So you see a single solution as needed.

You are not able as a business to be the KYC, identity, theft, fraud, compliance, and AML expert, and then really focus on your key key business. What you need is actually to have just one single integration and hopefully just one single contract. And this then actually allows you within that platform that you choose. And within that single solution provider to activate the data ongoingly that you need at the right time of the interaction with the client. So I think having a look at that, you already see that involves a certain type of workflow that it needs to involve.

Actually, you being able to activate data points and make them work together in an intelligent and in, in real time way. And at the same time, it needs to enable you to integrate a new data source within days, not weeks or months, and then make that data source really, or solution part of the overall roster that you have in your data hub. With that you create a centralized view of risk. You have everybody looking at a single source of truth.

You have everybody understanding what the potential risks are, either from an organization or sorry, organizational point of view, or actually within their role, potentially as a compliance manager, as an onboarding manager, or even taking customer calls. And for us, we see the future of the market being orchestration. I think it comes as no surprise that we do.

So the, the last 12 months really have seen the market adapt orchestration quite a bit. There has been lots of market movements. We at foster are very excited to have become part of Juu. And for us, we see that businesses are really, really on the one side struggling, but on the other side, moving towards a simple onboarding experience, making sure that there's the least amount of fraction. And on the other side that you open up the door for the good guys and actually close them for the ones that are trying to defraud you.

And as I already said, like you are doing that with a centralized view and with the flexibility, flexibility, and agility that you need in an ever-changing regulatory environment, but as well as you expand into new markets and verticals. So another big statement here is one from Gartner. I think for us as orchestrator and identity orchestration platforms, it's very powerful one. It goes along as well with you, either going to be orchestrated or you're going to become an orchestrator.

And I think the need to move to a single integration to a single provider is really evident because we see lots of movement in the market on that. And if you go back to what it means to be a true orchestrator and a global player that we see, it really starts with data data is that the absolute focus you need to on the one side, obviously have a workflow. You need to be able to mimic the internal processes of onboarding within a platform. And you need to do that quite automated to cut down the manual, work in the end, but at the same time, you need premium data on a global scale.

And that means that there needs to be a healthy cooperation, but it's why co-opetition in a way, because a lot of the big players, a lot of the premium tier one data sources are starting to see that value of actually working together where it makes sense. And at the same time to build out their own data, coverage and solutions. And here you actually see a couple of examples, not all of them, but a couple, because I think some of them are quite powerful. So the device verification is something that we will see in the use case a little bit later on with fintechs.

This is a very light touch, and it allows you to take a couple of good decisions, right from the get, go and weed out some of the obvious players as well in regards to association tracking. It's something that's very powerful. I think we've all seen that AML in adverse media is at the absolute focus. And then it's going to be paired with a lot of the other data sources you see here. The key point here is you need to have data sets per region or even Pervertical. And then on the other side, you need to be able to make it work together seamlessly.

I do wanna kind of illustrate this a little bit further with use case. We took FinTech here. I think you can exchange FinTech for quite a lot of verticals, because it really stands for most of the digital players that have an onboarding experience, but then as well, have their clients interact within the ecosystem that they operate. And for FinTech, if you take a look at this, you see that there's a first touch point. That is the customer life cycle start. There's a device involved and you are already able to collect certain data parameters.

Now, if you wanna take a decision on that is fully up to kind of the setup and the risk scoring that you want to set up here, but as you continue to interact, or the customer continues to interact with you, you gather more and more data points that actually create quite a powerful data profile. So here we have an account set up with email, with phone and date of birth. All of these can already trigger data calls to different providers that then are harmonized in one single place, and that are then actually allowing you to strengthen a customer profile and take decisions based on that.

And this is not just for killing the fraudster out of the system. It is as well to create individualized customer experience. So everybody that's actually providing you with good data could have a heavily increased customer experience downstream. You see here that there's different touch points. So now we're actually moving to one of the first deposits. This could as well be an account login, or it could be an account management update. These are all points.

If you're integrated at those touchpoints, you're able to collect data in a way actually as well, not always the same data per customer, but based on the scores and based on the quality of the data that has been provided in the previous touchpoints. So I think all of that kind of shows that an orchestrated approach of different data points and different solutions combined in a workflow and then powered like with very, very powerful kind of mechanisms in regards to how you bring the data together and score it is really where the industry is going in our view.

And to move on just quick word on how we do it at Juu. I think as we have become part of Juu, the word orchestration is an absolute key word, and you will see and find a lot of the things that I just talked about here as well. So you do set up KYC and different providers and solutions per use case. You're able to do that not only per use case, but breaking it down to a supp account by region or even a timeframe.

And then the workflow is really how do you internalize the already existing checks that might be manual of your customer into an automated way in the end, you combine setting up risk thresholds with that. And then as the customer interacts with you from account creation to login to actual edits, or as well within a payment ecosystem, you start to see the results of the workflow. You start to actually engage with data sources and data points, and what comes out of it is an intelligent score on the particular customer.

And I think combining that with a lot of reporting makes sense, especially if it's individualized. So everybody has the right intelligence for their respective job roles. Moving on to one of the last slides. I think you see as well here that we are integrated at the different touchpoints and that we are using data sources like email, phone address, verification, and AML screening all the way to then maybe some more heavy lifting. Once you go into actual the payment space and the payment ecosystem.

So there is OCR verification, there's biometric verification and there's extended E K YC, which actually on our side means all of the platform functionality that zoom for stop have brought together and have in the market today. So I think I have been going a little bit quicker, but I think I do wanna end with the statement that there's definitely hope so as complicated it, as that sounded.

I think what we have found as well is that FinTech firms that have integrated a solution provider actually two times more likely to say that managing and staying on top of fraud has become somewhat or very easy. So I think this is obviously a big statement. I think it's going to be something that you have to ongoingly stay on top of, but in order to do so we strongly believe that an orchestration play is the one that's going to win it. So with that, I hope you all have a good night and thank you very much for being here.