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When I think about the hottest areas in fintech these days identity verification is right at the top of the list. It impacts anyone doing business online so whether you are a bank or a fintech you need to stay on top of this fast-moving space.
My next guest on the Fintech One on One podcast is Joel Sequeira, the Director of Product Management at IDology. Joel is an identity expert with a long history in the space and he talks about the new attack vectors that are in play today, particularly with Generative AI, and more importantly, what fintechs and banks can do to curtail fraud today.
In this podcast you will learn:
- What Joel does exactly at IDology.
- The challenges for fintechs and banks in automating customer onboarding.
- How to incorporate automation and AI into your strategy.
- The role of human-supervised AI and how to maintain compliance.
- What he means by onboarding with inclusive customer journeys.
- How they define advanced identity verification.
- The different data sources that IDology works with.
- Why it is important to have deep data in different sectors.
- Some of the differences between KYC and KYB (Know Your Business).
- Examples of how Generative AI is being used to commit fraud.
- The trends that Joel is paying closest attention to.
- How fintechs and banks can future-proof their identity verification.
Read a transcription of our conversation below.
Peter Renton 00:01
Welcome to the Fintech One-on-One podcast. This is Peter Renton, Chairman and co-founder of Fintech Nexus. I’ve been doing this show since 2013, which makes this the longest running one-on-one interview show in all of fintech. Thank you so much for joining me on this journey.
Peter Renton 00:27
Today on the show, I’m delighted to welcome Joel Sequeira. He is the Director of Product Management at IDology. So we are talking all about identity verification and fraud today, and how to stop fraud, more specifically. Really fascinating discussion, we cover a lot of territory here. We talk about onboarding and onboarding workflows and customer journeys. We talk about advanced identity verification, we talk about the differences between KYB and KYC. We talk about generative AI, of course, we spent quite a bit of time on that topic. And Joel also gives his perspective on what are the future trends that you should be paying attention to. And much more. It was a fascinating discussion, hope you enjoy the show.
Peter Renton 01:16
Welcome to the podcast, Joel.
Joel Sequeira 01:18
Thank you, Peter. Thanks for having me.
Peter Renton 01:20
My pleasure. So let’s kick it off by giving listeners a little bit of background about yourself. Just take us through some of the high points of your career to date.
Joel Sequeira 01:30
Yes, absolutely. So I work with GBG IDology, we are based in Atlanta, Georgia, and our parent company is headquartered in the UK. And we belong to a larger group of companies in the US we have located who does address verification and Acuant, which is like a doc verification provider, and the other two companies that got recently acquired. So I joined IDology back in 2017, if you can believe it. And prior to that I’ve had fraud roles in mobile security, I’ve also done consulting with the big 4s, and also been a programmer my past life. So definitely a lot of skill sets I think I bring to the table. So what I do today is I work very closely with our head of product. And we lead the product design and strategy here in the Americas. A lot of my work kind of falls in, it’s kind of at the intersection of different teams, right. We talked to the customer success teams, the fraud analysts, and the marketing teams. And definitely the fun part of my job is hearing all these use cases and translating them directly into you know, specifications which we build into our solutions, and we kind of take those to market. So yeah, I mean, we we do offer a lot of solutions. Our goal as a solution provider is to provide one platform where we build trust across different user journeys. And our main objective is to drive revenue and combat fraud for our customers. So usually, when we talk to our customers, we have three questions, which kind of makes it a lot easier for us to figure out what service best fits their needs. So if they’re looking to perform basic checks to see if a person is deceased, or if they exist, the KYB and KYC solutions are pretty much what drives most of the compliance initiatives right, even for fintechs. We also check to see if you want to do business with the individual, you want to make sure you can do business. And that is where our sanctions screening, our PA checks come into place. Or if you want to check if a person has logged on from a sanction location, for example, right. And the third piece is pretty much where you want to bring all the other metrics and like you know your device, IP and phone and kind of get a holistic view of your identity. So at a minimum, we take minimum attributes like name and addresses, but we also do the SSN and DOBs, and kind of layer all the other attributes in, and we deliver that to our clients. And we work with, in the fintech space, we kind of work with a combination of traditional and non traditional. So on the non traditional front, it’s the payday lending services. And definitely we also work with some of the big banks and some of the neo banks as well on the traditional side.
Peter Renton 04:21
Okay, well, let’s get right into it, then. Let’s talk about some of the details about what you guys are doing. And I want to ask about automation, particularly when it comes to onboarding. How can fintechs, or traditional banks for that matter, how can they automate their onboarding workflows?
Joel Sequeira 04:40
Yeah, absolutely. So I think we got to talk about the challenges here for a bit, and the good thing about fintechs I think is, they’re probably the most open to technology than any other sector, right? But the one thing that’s restrictive with fintechs is the compliance right? So even in fintechs, it depends on who you are speaking to. It could be segmented. So you have banks, the traditional banks that have been regulated for many years, and they have been consuming KYC solutions for a very long time. And they’re pretty rigid, but the compliance is right. So you need an SSN or DOB to open an account at a minimum. But at the same time, you have the other side, where you have small dollar lending services, where the consumer demographic is pretty different. You see thin files coming in and they want to do business with them, so the regulations are probably not as stringent. So you have this combination of younger, thin file demographics where they lack digital footprint, it definitely makes it harder to provide a loan. And that’s lost revenue to your customer. I think when you look at the other onboarding flows, there’s budget constraints, right. So they might not be open to the doc verification services that we have today. So they still resort to manual doc verification, and that could be lengthy and frustrating at times. So I think when we are talking about, you know, automated solutions, you’re definitely looking to add that excessive friction for, you know, the bad actors, and you know, less friction to kind of bring in the good customers in, and kind of give them give them that preferential treatment. So this is kind of where we can think about incorporating automation and AI, they are two different things, into your strategy. And this can kind of make the process more efficient. So I think the hot topic right now is, is definitely GenAI. The table stakes are doc verification, you know, your algorithms, your transaction monitoring, but I think the hot topic right now is is GenAI. So I think we are starting to think about personalization, right? In terms of like, when you come in, and you look for a particular product. Yeah, GenAI I think can articulate, you know, based on the unstructured data that it sees, because it’s really good at breaking down unstructured data, understanding the customer requirements, and producing relevant products and services, which is kind of tailored to each of those individual transactions. So you’ll definitely see a lot of the onboarding workflow start to use GenAI. But at the same time, I think when it comes to fintechs, you got to think about compliance, first. You got to think about, you know, KYC, and you got to have the SSN and DOB checks. And you definitely have to have good data sets that can do that today for you. So yeah, there are a couple of things that, you know, can be used to kind of like, improve the onboarding journey, and kind of automate that with the basic automation tools, like, which are table stakes today. But you know, with GenAI I, we got to be really careful as to how we use that in different workflows.
Peter Renton 08:03
Right, right. What about human supervised AI? I’ve heard that talked about quite a bit in in recent months. How is IDology using that, and what’s its role here?
Joel Sequeira 08:17
Yeah, I think that’s a great question. And especially like we mentioned earlier, when you introduce AI in a automation workflow, especially for fintechs, you got to look at regulations first. So I feel right now, there’s a lack of understanding from a regulator standpoint, in terms of what AI is capable of, and what its applications are. And this is quite evident from the limited policies that dictate how and where AI can be integrated into the consumer workflows. So I think right now it’s kind of the Wild West at this point, right? So everybody is kind of trying to incorporate AI into their workflows without thinking about compliance. So one significant challenge, it continues to be explainability, right, where you have to articulate if an AI has made a decision to a regulator, who comes knocking at your door, and you’ve come back with a probabilistic score and say, Well, yes, I have this amazing AI model, which threw me the score. But that’s not going to fly with a regulator. So that’s not going to fly with, you know, someone who you’re going to make a decision on getting a loan rejected. So regulators will continue to look for more of a deterministic metric, where it’s easy for a customer service agent to go back and say, well, this transaction failed because it had an incorrect SSN. And really, it’s kind of crucial for companies to be able to explain the decision when you have AI at play. And I think I think this is where the human supervision comes in play. And where we, as a company have seen the most efficient use of AI is again in automation, and an example is we have a fraud analyst team. And they sift through vast amounts of data on a daily basis. So what they have done is they have partnered with the tech team, and they have built these ML models that run all night. And this helps them identify trends the next day, especially the ones that have been overlooked, right. And what they’re able to do is they’re able to go back to the customer and say, I think we kind of really looked at this, this particular trend, and they’re able to bring that up instead. So also the feedback that we get from, you know, the manual reviews that we have, because I think the manual reviews is always important. We use that to update our models, which I think you know, is table stakes as well at this point. But I think one thing that solution providers kind of leave at the table is those manual insights can be so informative, to take it back to the product, and kind of design new actions and new flags. So it’s kind of a combination of a lot of teams working together when you’re talking about, you know, human supervised learning, and AI will continue to be this tool that will complement the different teams to kind of save time on one side, and also provide insights and you know, the best action items for your customers.
Peter Renton 11:18
Right. So I just wanna make sure I’m clear here. When you say human supervised AI, are you talking about the data scientists that have really created the model and overseeing that, and the customer service person who’s on the front lines? Is that what you’re saying?
Joel Sequeira 11:32
Yeah, I think especially for fintechs, right, I think there’s really a partition. Again, when it comes to going back to a regulator with a model that states the score, it’s not going to be enough. So with human supervised AI, you are building models to kind of make your day to day more efficient, while still meeting regulations. So KYC does the compliance space. Post KYC is more of the risk analysis piece, right? So this is where you can incorporate AI, along with human supervision, which is feedback from the fraud team, feedback from the customers back into the AI model, and kind of working together to kind of combat the trends that you see on a daily basis. And that’s kind of what we mean by human supervised AI, and not just everything is automated, you come in the next day, and you got rejected for X amount of loans, and you’re probably not able to figure out why because it’s a blackbox score, if that makes sense.
Peter Renton 12:36
Okay, so let’s talk about the customer journey. And I’ve seen you guys talk about inclusive customer journeys. What do you mean by that?
Joel Sequeira 12:48
Yeah, I think that’s a really great question, especially for me personally. And I think at the crux of it, we’re looking to see how financial services are made accessible to everybody, right. And this is where we started focus about the Gen Zers, the thin files, the banked and the underbanked. So when I first came to the United States as a student about 13 years ago, it probably took me about a month to open a bank account, right. But considering that I had already gone through a lot of checks to be accepted into a master’s program, I should probably have been seen as a lower risk. So may have been helpful if the bank had faster ways to check my identity and make sure I’m not a risk while they are still being compliant with their regulations and their rules. So that’s one instance where, you know, a quicker onboarding flow for a lower risk consumer like me would have helped, and I’m pretty sure that has changed now, since it’s been that long. The other thing, I, the other thing that we have noticed is, a lot of our technologies are location centric. So this is where we are looking at name checks from different regions, which are not in the United States. So providers usually focus on US centric names. So definitely expanding that scope to check for names that are of different dialects and languages is crucial, especially where names have different encoding practices or escape characters. And this is where, you know, we as IDology, when we speak about our global presence, with the UK in different regions, a lot of companies come to us and all they need is a sanctions check, right. So if we can do that only for the US based customers, that’s probably a no deal for them. So definitely having that flexibility and thinking about subtle things like a name that can come in differently, with a different encoding, makes a big difference. And that allows providers to have customers become more sticky, and you know, they can offer other solutions in their stack. And I think the last thing is definitely thin files. There was there was a CFPB report, back in 2015 was said about 20 million Americans were thin file. And this is where, you know, individuals probably have very less credit history. So really, they do not have a credit score, where a loaner can trust the individual and provide a line of credit. So, looking at things like, you know, past credit history and loans, especially for thin file clients, there is a lot of order and data sources out there, like utility bills, or DMV records. And I think this is where, you know, solution providers need to look at different data sources to offer that to different groups of individuals.
Peter Renton 15:54
Okay, so then, let’s talk about identity verification, specifically. Maybe you could talk about what I saw you guys refer to as advanced identity verification. So what is that? And why are consumers, why do consumers prefer this today?
Joel Sequeira 16:13
Yeah, I think consumers worry that, you know, companies are not safeguarding their personal information. I mean, my data is out there. And it doesn’t concern me anymore, right. But I think fintechs should start to focus more on the onboarding flow to be less complicated, quick and trustworthy. So when you open an account, depending on what service you consume, you anticipate a higher level of verification in some cases, so you know, your account is protected. And this, in turn, improves the brand loyalty to your service. So for example, if you’re wiring $100,000 to an escrow account to buy a property, it’s completely fine if you’re asked for additional verification, such as providing your document. And also if you’re new to country, it’s completely fine when you’re asked for a passport, right? So I think that’s one place where we need to think about, you know, the consumer side where it’s okay to provide that additional verification, and that additional friction is completely fine. On the business side of things, what they’re looking for, is to increase their user base, they want to focus on pass rates, and definitely increase their bottom line. They want to have that good consumer experience, right. And finding that balance between fraud and friction, I think it’s an ongoing battle. But I think in these cases, particularly, it’s more important to think about, what is the cost of trans? What is the cost of fraud per transaction, right? What is the dollar value of a transaction before you can make a decision to escalate into other services like doc verification? So, in gaming for example, there is a lot of promotional abuse. So the cost of every transaction is probably like $200, because that’s the promotion you would get, or that’s the dollar amount you would lose to a fraudster if you let them in, right. So you’ve got to think about the cost of the transaction before you decide to say, All right, I’d like to escalate it to doc verification and get, you know, more evidence that it is really the person who says they are. So I think in our offering, what we do is, we are able to expand our data stack in terms of the data we provide. So we look at public records. And we also look at credit data sources. As you know, we also have these high risk signals like email, IP, and phone. And we provide those levers to allow friction in some cases where you want doc verification if you provide an incorrect SSN. The other thing I think we need to focus on as the consortium, right? I think solution providers that have deeper insights into different industry sectors will be able to provide more insights on the fraud that travels. So if you look at an identity that comes in, if it is a fraudster who has obtained that from the dark web, they’re able to use that in different sectors. So just having that insight and providing that the client to say, all right, I think you may be a victim of, you know, for dark web search. Maybe just adding that additional layer of verification is okay for the end user to go and say, Alright, I think I’m a victim. I’m completely fine providing you documents to make sure that it is me and that improves that brand loyalty back to the service.
Peter Renton 20:00
So can you just talk a little bit more about the consortium piece there? Like is this something that IDology has set up? I mean, who’s involved in that consortium, and how is the data being shared?
Joel Sequeira 20:13
Yeah, absolutely. I think that’s, it’s very specific to each solution provider in terms of how they partner or store data within their entity. So with IDology, we have, you know, velocity rules, and what we can do with velocity and something, we also call us network alert losses. You know, it’s kind of a feedback loop. So we’ve taken data, and we store it for a set amount of days based on a privacy policies. And even if it’s a small time period, we are able to kind of like share that information in terms of insights, we don’t really say that it’s the customer A, or the customer B, that we have seen our data flow through, but we are able to share that insights. So a lot of solution providers are investing in consortiums. But at the same time, what’s really important is how deep that sector is, I mean, if you’re only focused on banking and lending, it’s probably not enough to, you know, price it as a good consortium, compared to if you’re in gaming, banking, lending, tax. And also, crypto, for example. So we, we call it tax season, we actually call it fraud season, because we see a lot of fraud during tax. And what we see fraudsters do is they would get those tax refunds, and they would open prepaid cards, or go to another sector and probably open multiple accounts in gaming. And the only way for you to get that visual is to have that deep data in different sectors, so you can report back to the client and say, Well, I think your data has been breached. Or you need more verification in these particular workflows, because we kind of sense that it could be a synthetic that’s been built from your identity, or it has been shared by multiple fraudsters or like a fraud ring.
Peter Renton 22:14
Right, yeah, that’s interesting, because I mean, I noticed for me, like, particularly when we were recording this just after a couple of days after tax season is over, I noticed a huge amount, I was getting probably three to five calls a day. I have a Coinbase account, and they mention Coinbase, and they’re saying like someone’s trying to steal money from your Coinbase account, and please press one, or whatever it is, it’s kind of a blur now, but yeah. It was three to five a day, and that was fairly recent, like it’s only really been the last couple of months. Is that sort of some of the things you’re talking about there?
Joel Sequeira 22:54
Yeah, to some extent, yes, absolutely. I think what fraudsters are doing is they’re buying lists, right from the internet. They’re buying email lists, they’re buying valid phone lists. And this is available on the dark web. And they are also able to understand if a phone number is attached to a particular service. And that’s kind of how they’re probably targeting you so well, to see, alright, Peter has a Coinbase account with his phone number. Let’s find him and see if he’s going to fall for this. And it’s probably been shared, right? And that’s why you’re getting multiple messages at the same time. So it’s definitely something that’s on the dark web where multiple fraudsters are buying the same phone number and spamming you at the same time. So that’s interesting.
Peter Renton 23:41
Let’s switch a little bit and talk about small business and KYB. Know Your Business. What are some of the differences between KYC and KYB?
Joel Sequeira 23:54
Yeah, absolutely. I think let’s just take a step back, and, you know, go back three years. And I think during the pandemic, right, there was definitely a huge surge of new LLCs opening up. The marketplace has started to evolve, the gig economy workers started to pull in. So prior to this, fintechs that used to provide that line of credit to businesses, what they would do is they manually verify the addresses on Google. They download the Secretary of State filings. And post pandemic, those processes got significantly challenged, it was not a sustainable model at all. So one of the challenges was verifying the UBO are the ultimate beneficial owner of a business. So this could be very difficult because sometimes they could have complex ownership structures with the LLCs that they own. So you could have an owner with multiple LLCs and they might also need to provide details of different LLCs before they could issue credit, right. So at the same time you could have multiple owners for an LLC, which is in good standing, but one of the UBOs was suspicious, right. And all this information is not easily accessible, you know, to a customer. So, I think the other challenge also is the availability of business data. So consumer data, you can find that in many places, right. You can find that from public data sources or credit data sources. But, you know, business data is not that easily accessible. It’s available on the internet, but you have to manually download it. So having automated know your business verification solutions suddenly started to become very popular. And it will continue to be like a great opportunity of solution providers wanting to invest in it. And our KYB solution, it’s kind of built on the same DNA as our KYC solution. So you verify businesses and their owners with minimal input information, such as the FEIN numbers, or the business name and the address. So our solution, what it does is, we take more of an integrated approach. So we have multiple layers of risk evaluation to understand that the business or the owner is engaged in any suspicious activities. We provide verification for about 50 to 60% of the business base, and we pull it from multiple sources, we pull it from public records, from government licenses, the Secretary of State filings, business credit header data, and we can use that and verify it against the business itself. We also check the sanctions list. So we don’t just do checks on the UBO, we can also, you know, look up the business entity, as well. So yeah, there are a lot of checks that go in, and one of the other checks we do is we can check to see if it’s actually a commercial building, or if it’s a residential address, we also check to see if it’s a corporate or private email that the UBO claims to have. And there’s a bunch of other fraud checks that we have. So it’s definitely a comprehensive solution that we have built as part of our onboarding. And yeah, that not just allows us to build trust with, you know, our customers who look up the businesses, but they can also continuously assess the risk over time in terms of the sanctions checks, or anything around continuous monitoring.
Peter Renton 27:39
So then are you saying that like someone who has a business address that is actually a personal home, is that a, like obviously there’s a lot of people that have that, right? Particularly the gig economy workers and small businesses, but is that sort of a flag that you kind of take into account? I mean, when you’re checking that, what are you looking for?
Joel Sequeira 27:59
Yeah, absolutely. I think it really depends on the customers. Sometimes they do want to have a, they do want to have like an on site visit for a business, right? That’s one of their checks. So do you have a printer at your place of location, is this a 20 person office, right? So that could not be a residential address. And that’s where they have a subtle check to see if it’s a commercial property that they are doing business with before they can process them for credit. We do a bunch of checks with some of our clients and that’s why we kind of know what it entails. It could be as simple as can your door lock at your location? Do you have like an actual physical padlock at your location? So I’m not sure if a residential address could have that.
Peter Renton 28:50
Right. I want to go back and talk about AI. Wanting to sort of particularly talk about generative AI. Are there some examples that you can share of how Generative AI is being used to commit fraud, as well as to fight it?
Joel Sequeira 29:06
Yeah, never a dull moment, since it all began.
Peter Renton 29:09
Right, I’m sure.
Joel Sequeira 29:11
So yeah, I think it’s important to understand what GenAI is good at. It generates text, voice and video. And I think we also understand that it can take multimodal inputs. And everybody has tested or heard about ChatGPT. So ChatGPT covers the text portion. They came up with their Sora solution, which does video generation. And they also launched a new tool, right? It’s called a voice engine. All it requires is 15 seconds of Peter and it can generate a natural sounding speech right? Which is kind of, which is definitely, I know, concerning. I think the other thing that’s really concerning is the best language model tools are, that have been built in the world, they are offered to anyone who has $20 to pay for per month, right. So that’s really a low barrier to kind of get access to these these models. And I think the other aspect is, there’s so little input needed right now from the victim to create a profile and to communicate like the victim. So that’s definitely concerning. And you’re already seeing, you know, the text being cleaner. There’s no spelling mistakes, when the day becomes a sticklers for spelling errors, right? There’s definitely going to be these phishing or smishing incidents that you’re already seeing. So we get the text part, but now it’s slowly translating into images. There was a website about a month or two ago where GenAI was used to create very convincing, synthetic identity documents. And we’re not talking about the ID documents from the previous year where, you know, they replace the photograph or the signature, we’re talking about really good synthetic documents that have the shadows in place and the forensics in place. And it was really hard to distinguish. So it definitely started to challenge a lot of these doc verification workflows. So I think we’ll continue to see a lot of these incidents, we can always go look up and see what’s the most recent incident, it is always something new on a daily basis. So these attacks are definitely going to continue. And I think what we need to do here as solution providers is reevaluate some of our workflows, and work with our client and decide to use the other tools in our stack, where we can add more friction, in cases where we believe GenAI is going to influence some of these workflows.
Peter Renton 31:55
Yeah, I imagine it’s challenging, to say the least. Because particularly when you’re trying to do like, oftentimes you do liveness detection, and if they have video capabilities that like, it just feels to me there’s no real way, right, to determine whether something was created by Generative AI, text or video or audio, there’s no real way to tell that it’s human created, or the machine created, is there?
Joel Sequeira 32:23
Well, I think that’s where we’ll see a lot of investment for 2024, right? You’re gonna see a lot of these detection softwares on the rise. And I’m not an expert in that field, but I think if you look at a GenAI video closely, you’ll start to analyze the gestures, the body language, you know, the inconsistencies, or some pixelations in regions of the image, right. And those will be some giveaways that it was generated by AI. And I think if you look at the Sora videos, there’s a video of, I think, someone blowing the candles off the birthday cake, one of the candles has two wicks, instead of one. So it’s those subtle things that we’ll start to see. And I think there’ll be a lot of these detection softwares. And also in speech, right? It’s going to be the hesitations or the pauses that kind of look or sound AI generated. For example, when you ask me a question, there is a pretty subtle pause that, you know, the AI is thinking before it responds, and you can definitely tell that it’s definitely synthetic, and it’s not real.
Peter Renton 33:40
Right, right. Okay, that segues nicely into our last question, which is talking about some of the future trends when it comes to identity verification. What are you paying close attention to, and what are your recommendations to sort of help fintechs future proof their identity verification?
Joel Sequeira 34:00
Yeah, absolutely. I think we did cover about, you know, the investment and AI detection software. So that’s definitely going to be on the rise. And I think solution providers are definitely going to think about ways to incorporate those in KYC workflows, you’re going to see a lot of biometric checks and KYC checks that will be supported by, or complemented by some of these AI software. So definitely a lot of integration there in your KYC journey, especially for fintechs. I think that’s kind of the tech stack side of things. And what we need to focus more on is the data side of things because most of these checks are data driven. They are API based. So we’ll see a lot of solution providers invest in their consortiums, like I said, you’re going to partner with a lot of providers who have this data, they’re going to look at, you know, different data sources like public and credit, and alternative data sources, so we’re gonna see a lot of investment into that. And I think it’s also, there’s also going to be a lot of investment in AI that can explain how a decision has been made, right. So explainability is going to be crucial. There are already companies out there that are built to describe how a model performs, or how a model reached a particular decision down to the granular detail. So again, coming back to some of these compliance obligations, right, and ease of onboarding for fintechs. They’re going to, we’re gonna see solution providers focus more on making their tech stack easy, and integrate more into different data sources to kind of, you know, provide that customer journey and make it more efficient.
Peter Renton 35:55
Okay, Joel, we’ll have to leave it there. Really appreciate you coming on the show today. It’s a fascinating area that you’re working in. Certainly a fast moving one, and one that probably has great job security for people like you, given how much it’s changing. So thanks again for coming on the show.
Joel Sequeira 36:12
Absolutely. Thank you so much for having me, Peter.
Peter Renton 36:16
Well I hope you enjoyed the show. Thank you so much for listening. Please go ahead and give the show a review on the podcast platform of your choice and go tell your friends and colleagues about it. Anyway, on that note, I will sign off. I very much appreciate you listening, and I’ll catch you next time. Bye.
