Connect & Collect Podcast - Episode 6
In this episode with Alexandre Semenov, Customer Solutions Lead at SAS Institute, he dives into the ins and outs of accelerating a tech-driven transformation within the financial services industry. We discuss how the increasing number of technology options and solutions available in the market can cause a sense of paralysis in decision-making. Institutions must find the right balance and approach to adopt new technologies effectively.
Alexandre shares strategies to get leadership teams on board with modernizing systems. We explore the changing landscape of recruitment with younger generations, the importance of data security in banking, and the need for flexible solutions that can adapt to changing preferences in the payment and recovery experience.
What we're talking about in this episode:
- The challenges around data security within financial institutions
- How to deal with decision paralysis when it comes to evaluating new technologies
- Learning to fail fast and adapt quickly to achieve success with large-scale projects
- Catering to customers’ preferred communication channel and payment options for higher recovery rates and customer satisfaction
"I think the requirement from the consumer as a general statement is for the payments and the processing of transactions is to be easier" -Alexandre Semenov
*Please note that the views and opinions expressed by our guests are solely their own and do not represent the views or opinions of their employer.
Alexandre Semenov is the Customer Solutions Lead - Financial Services at SAS Institute.His focus revolves around championing the adoption of the latest SAS technology and effectively aligning the analytics, data, and modeling needs with the solutions and infrastructure components available within the SAS portfolio.
Prior to joining SAS, Alexandre was at BMO in the Risk, Strategy, and Portfolio Management roles, followed by a journey as a Consultant and Senior Advisor at TransUnion where he developed Credit, Risk, and Marketing products & solutions for the Canadian Banking Industry.
He is a speaker at conferences and gives lectures at universities and business schools.
Connect with Alexandre Semenov on LinkedIn.
Learn more about SAS Institute
Read the Transcript:
[00:00:00] Michael Pupil: - Welcome to Connect and Collect, the podcast for leaders in credit and collections brought to you by Lexop. Get firsthand insights from our expert guests around the latest innovations, challenges, and opportunities that lenders are facing today. I'm your host, Michael Pupil, Vice President of Sales at Lexop, and today I have the pleasure of introducing today's guest, Alexandre Semenov, Customer Solutions Lead, financial services at SAS Institute.
Alexandre's focus revolves primarily around championing the adoption of the latest SaaS technology and effectively aligning them to analytics, data modeling needs with the solutions and infrastructure components within the SAS portfolio. Prior to joining SAS, Alexandre was at BMO in the risk strategy and portfolio management roles, followed by a journey as a consultant and senior advisor at TransUnion, where he developed credit risk and marketing products and solutions for the Canadian banking industry.[00:01:00]
He is a speaker, often at a number of conferences and gives lectures at universities and both business schools. Alexandre, welcome to the podcast. It is a pleasure to have you here today, sir.
[00:01:10] Alexandre Semenov: Thank you for having me, Michael. It's a pleasure to be here.
[00:01:13] Michael Pupil: Wonderful. Well, before we jump right into, uh, some of this stuff, I'd love for our listeners to get a little bit more background on yourself and and SAS as well. You've obviously, you know, in the introduction work at BMO, TransUnion, and now SAS. Can you walk us through a little bit about your journey in your career and what has led you to your current role that you sit in today?
[00:01:32] Alexandre Semenov: Michael, it was definitely a buildup. So I entered the professional industry at the onset of the financial crisis, around 2008. So it was, it was a tough road for a master's student to find this place in the industry. So I started my quantitative background in, BMO modeling division, on the retail and commercial side. And from there I did different roles all on the data analytics and risk management side, which [00:02:00] had opened my eyes at the time to, to the broader world of banking. From there, I joined TransUnion, which is one of the credit bureaus, as a senior consultant serving the mid-market, the fintechs, then the banking industry, so the, the big banks, and I did that for seven years.
So having that background of analytics, data, understanding of the banking and the knowledge of the financial industry, I was able to join SAS in this, capacity where I provide advisory services to our clients, all in the financial industry. So I guess to do a full circle, it was a buildup of different building blocks, which had allowed me to become the persona, who I am from professional standpoint, because I bring the combination of practical. So I used to do things with my hands on a keyboard. In my [00:03:00] previous job, I used to deal a lot with the executives, and this is currently what I do now, again, sometimes I like to describe my job as bringing a simplified view of complex solutions to a ground level.
[00:03:15] Michael Pupil: I'm really glad you ended it that way cause I think it's a perfect segway to a question that I wanna follow up with, but before I do that, talk about an exciting time to join financial services was in that time period, which I was roughly around the same time as me, so I know some of those pains where that was the first foray into that area.
So, I know what, uh, what exciting time that was and, and a lot of it is reminiscent of today. When you mentioned, the comment of taking complex ideas and boiling it down to simplified solutions and especially dealing with the executive level, I think there are many executive teams that have some very well versed individuals on a technical background, but not all.
Um, and that's probably where the simplified version, you know, kind of comes into, these [00:04:00] complex ideas. Talk to me a little bit about how you bring those complex ideas and solutions to the executive team specifically.
[00:04:07] Alexandre Semenov: So I'll start by saying that the consulting background definitely helps cause in the consulting shoes you're required to understand complex or non-trivial concepts rather quickly in order to apply them down the road. So, first of all, boiling it down in my own head and understanding the building blocks of a solution helps.
So kind of like, you know, apples and oranges. Then what I do is I go to the resident experts because sometimes in the world where we live today, which is ever so complex, you are required to have a deep knowledge of a subject in order to, bring the right ideas to the table and I'm blessed that at SAS we have an array of different experts, so let's say I'm not an expert in IT, but we have architects that I work with on a daily basis. So if I have a question about [00:05:00] Kubernetes or the compute service, I can go to an architect and have him explain to me in lamen terms what that, particular detail is without taking a master's degree at a university for example. So I do that. Also, by spending enough time in a certain area, you start acquiring knowledge. So the combination of the knowledge that you've acquired, over time through working in different roles, different industries, definitely helps as well.
[00:05:29] Michael Pupil: Yeah, absolutely. SAS is obviously connected very heavily to the infrastructure side of things. Would you say that there are more challenges today, in 2023 than let's say a number of years ago? Or what challenges, or changes and challenges have you seen specifically with SAS? Because they're are a little bit, than the, let's say, the Lexop's of the world.
[00:05:51] Alexandre Semenov: What I'm seeing in the last two years is an acceleration with regard to the modernization of the systems upon [00:06:00] which an institution, whether it's a bank or another, large institution sits. So today everything is data, right? Like what you buy, what time you buy, where you go, what you browse, etc. So all of this is, is data.
So the quantity of data available to end users, to institutions has become extremely large. Hence we have the rise of cloud, the rise of, data processing, tools, modeling, and so forth.
[00:06:33] Michael Pupil: Yeah.
[00:06:34] Alexandre Semenov: What I'm seeing in particular is a heavy aspect on data security. Because in the recent years, we've had in North American, Europe, a number of large institutions that have been subject to attacks by hacker groups and what not, which had as an ultimate effect, data leakages.
So PII and other sensitive data for customers was, was leaked and [00:07:00] traces of it could be found even today on the dark net, which is something that makes industries, and we don't need to stay in the banking industry, we can go literally in any industry that uses PII, whether it's paying your bill or whether it's subscribing to a grocery delivery, all these industries have all of a sudden realized that, oh, well my customer base may be at risk because I've built this home ingrown system, which I have two guys monitoring and everything was kumbaya until recently, and then we got hacked. So, in order not to be in those shoes, institutions have started investing heavily in data security, whether it's through internal processes or whether it's by purchasing solutions from, vendors, big vendors, most of the time that allow them to have their data to be secured and at the same time, often what happens is, the data could be now modeled with the additional tools that [00:08:00] those vendors offered and that led to an expansion of the services or to the account management capabilities that those institutions have championed.
[00:08:09] Michael Pupil: Yeah, especially the, the move from financial institutions to being on-prem into the cloud has been a big shift. And then of course now with the advent of moving into the cloud came the opening of the door to many applications, many pointed solution applications. I mean, it wasn't that long ago, that I remember where electronic signature was still - is it legal? You know, is it safe? Can we adopt this? And now that, that ship has sailed and I think that everybody has, you know, kind of moved past that there are still a number of vendors out there with pointed solutions and applications. What we have found, and I'd love to know if you see the same thing, is a little bit of paralysis in the industry because of the need for the subject matter expert that you talked about before when you mentioned, let's say Kubernetes or somebody in a department somewhere that is an expert and you cannot be [00:09:00] an expert in everything, but we have certainly seen a little bit of a paralysis of activity because there is just simply so much choice that is out there.
And so how do you tackle that you know, coming from SAS?
[00:09:12] Alexandre Semenov: Paralysis is the right word in my book, because this is something that I have seen certain institutions deal with, and this is a matter of choice. There's so much choice available as you've mentioned, for literally all the systems that exist out there. There's always a competitor, a substitute, there's another option which may be cheaper, faster and what not. Now, understanding and balancing those options is the silver bullet in my opinion, because from wearing an executive hat for one minute, you wanna grow your business, but you also wanna maintain your business and looking forward, you may realize that you need to make heavy yet necessary investments in order to continue holding or even growing the market share.
Now [00:10:00] many vendors out there will have very strong messages and basically may promise you the world with their solution. Now it is on the experts, on the combined teams of experts because of the complexity of those solutions to provide the right level of advice to their executives, so that money is well spent, money is not wasted.
The approach that I've seen work well is quote unquote fail fast. So you try something, fail fast, get learnings from it, and off you go on the loop. And by using this approach, you'll quickly realize what worked and what haven't, and you will put your money on things that have worked. And this approach allows a group, an institution, company to make a few bumps down the road, but then ultimately become successful with large scale projects, which are, [00:11:00] let's be frank, they're complex.
Very, very seldom everything goes as planned. So therefore, you need to have the right buffer and the right mentality to adjust to the challenges that may arise as you are knee deep in one project.
[00:11:14] Michael Pupil: Yeah, well said. Look, I just have maybe give an example of this, this there, the term AI is thrown around so much and there are advancements in technology so while there is paralysis, there obviously is advancements in technology and adoption of those things. Uh, and it's basically choosing and feeling comfortable with what you are choosing and failing fast and being able to pivot.
I'm gonna ask you a question here and forgive me for putting you on the spot, but it is something that drives me absolutely crazy. I go to a lot of conferences. I hear those two letters being thrown out constantly of AI and I personally think that there is a difference between AI and machine learning.
And I would love to get your definition and maybe I am absolutely incorrect, and maybe they mean the same things, but I'd love to just put it out there. Is there a [00:12:00] difference and what is the difference between AI and machine learning?
[00:12:03] Alexandre Semenov: Michael in my simple mind machine learning is a subset of AI. So when I think about AI, I think about, uh, developing intelligent machines. Machines that may help one with tasks that have required human interventions. For example, let's use Siri. So I upgraded my voice recently and all of a sudden Siri has started reading the text messages I was getting.
I was like, well, this is cool. So AI is Siri. So Siri has become my personal assistant. And what does Siri do? Well? Siri analyzes the patterns that I follow, and those patterns are being modeled through machine learning. So machine learning is the teaching of Siri, and Siri is the intelligent machine.
And Siri, we have different [00:13:00] teachings applicable. So from one end, she may tell me that based on an email that I received to schedule a meeting from another end she may tell me, Alex, you've been working six hours. You, you want to take a break? Those are two different call it predictive outcomes.
Hence two different, two or more different algorithms may have been used to achieve those predictions. So hopefully that vision answers your question, Michael.
[00:13:26] Michael Pupil: It does. For me, I think I will continue to see a blurring and a blending of the two. And, they are so tightly connected because like you said, one is a subset of the other and one delivers, into the next. And I think it goes down to that subject matter expertise.
And because technology moves so fast and because there is such a plethora of options that are out there, I think vendors, and I can speak coming from Lexop vendors, do a tremendously good job of confusing everybody with everything, using some buzzwords that are out there that are very, very popular. And I like to keep things [00:14:00] simple, which I think is why we get along so well.
Um, if I were to shift gears a little bit and make it more towards your personal experience and your professional experience specifically in the financial services and relating it down to collections, debt and what the trends are there. How can, banks, credit union, financial institutions, neobanks, how do they balance their focus on the origination side and the origination need, as well as investments into the collections process on infrastructure?
what would you say there?
[00:14:33] Alexandre Semenov: Oh, that's a tough one. But ultimately what it comes down to is the platforms you're using, right? Because rarely nowadays you'd see an institution where you have a physical person doing the origination, and I'm talking about the retail side and the collection. It's just too expensive, right?
So you'd rely on a degree of automation and a platform that allows you to build, decision rules, to [00:15:00] input models, to hang those models, execute them and store the data in. So what I'm seeing now, the platforms which are used for FI's across the consumer life cycle are agnostic to the part where the customer is.
So whether be that on the origination side or as a customer on the account management side or on the collection side, the platforms are, call it machines that allow an end user to input a certain logic and execute on it. AKA, I'm adjudicating Alex, and my minimum cutoff is 720 score for a credit card.
Well, that's a business rule. Same for collections. If Alex is 30 days past due and his balance is above X and score is above y, I want to give him a phone call and not send them a letter type of thing, right? So the platforms in which customers, invest they need to be flexible and agnostic [00:16:00] to what you wanna do with them.
So whether it's executing a rule set or sending an email, all of those are rules and decisions, and those platforms should be able to handle those decisions, no matter where those decisions are placed in the consumer life cycle.
[00:16:19] Michael Pupil: Couldn't agree more with you. We definitely see it, and I've personally experienced this in even my prior lives on the origination side. I remember in the days where the websites for FI's was the shiny new toy when it came to technology. Being able to access your bank account, your balance, you'd be able to do a couple of transactions, and then with greater cybersecurity and protection of information and protection of the transactions, www my bank.com became a pretty useful and still is a pretty useful tool and a cost effective tool and one that is genuinely well received. I remember when banking apps first started and it the response was, well, we already have a website, [00:17:00] we don't need an app. And the market demand, uh, grew so quickly with the advent of, these smartphones that, the nice to have very, very quickly became a must have for those applications.
And that allowed more origination of applying for, and qualifying for different product from financial institutions. At least on the collections and the debt side what is interesting to me is I am seeing, and I wonder if you see the same thing on your side. What we're seeing is that now the same level of, integration of systems that you're talking about, that efficiency of workflow is, well, we've already been collecting the way that we've been collecting and we're fine.
And now the nice to have of sure, we would love to have a very easy tool to be able to communicate and collect in an efficient process seems to be still sitting at the nice to have for the institutions. While it is becoming very, very clear from the consumer side of things, it is a must have [00:18:00] to make those payments, faster and easier.
Are you seeing the same thing where it definitely is a trend to me and I've seen it, all the way through the scale.
[00:18:08] Alexandre Semenov: I'll pick up on one of the last words you've said. Easier.
[00:18:13] Alexandre Semenov: I think the requirement from the consumer as a general statement is for the payments and the processing of transactions is to be easier -AKA when I log into my banking app, they use face recognition, so I don't need to remember yet another password.
When I do a payment. I don't wanna go through 15 different steps when I wanna send 20 bucks to Michael, right, because he got me coffee. Yes. Simple stuff. And the more simple it gets, the more user-friendly, it gets, and the more customers you end up having. Because coming back to [00:19:00] my mantra, our world being increasingly complex and us as professionals and as just regular folks living their lives, we are being bombarded with additional requirements in order to protect our data, protect our credit bureau reports so that nobody can affect in a malicious way our day-to-day life, right?
Hence, the demand analysis to have multiple passwords. Now, we cannot have simple passwords because those are easy, easily identifiable and or predicted and whatnot. And that, trend seems to be accelerating. So for me, as a consumer, I welcome simplicity. I welcome solutions provided by repeatable institutions that allow me to feel safe, yet achieve the goals that I wanna achieve, can make transactions, safeguard my digital persona [00:20:00] and whatnot.
So simplicity, I think, is the key for interactions with consumers.
[00:20:07] Michael Pupil: Couldn't agree more. And you know, consumers tend to move, I guess the word is trends, right? Consumers follow these trends and I think, with the retirement of a significant amount of the baby boomer stage, it's certainly changing the landscape and has been changing the landscape for a number of years.
We've definitely seen that impact from recruiting talent to succession planning at the executive level, technology and the growth of technology. And we talked a little bit about this at the beginning, how you speak to an executive team and leveraging that SME, how do you bridge that gap and take into account all of these trend demographics that are taking place.
And, and what do you see the gap there? Like how do you tackle it?
[00:20:52] Alexandre Semenov: Well, what I'm seeing is there, the newer generations who graduate from universities,[00:21:00] they often know, programming languages and those programming languages, are very often now a requirement, right? Because there's so much data out there, one needs to have a certain level of understanding of how to process this data, how to handle this data, how to do things with the set data. As a result, many graduates come with, a pool of skills that they've acquired in universities, for example. So this pool of skills needs to be immediately utilized by the companies because, so imagine this simple situation where you have an expert who's taken the retirement. That expert used to do job X, Y, Z. Now you're hiring a new grad full of potential, full of talent, but you need to get this grad up to speed with regard to how the company works, the duties, and so forth. So, you wanna start again from a management position, you wanna start realizing the [00:22:00] investment in this new employee right away.
Now grounding this in reality, we all understand that the getting up to speed for a person, no matter how smart the person is, may take time. So you wanna minimize it and how do you minimize it? I'll throw the question to you. If the person, let's say, learned R programming language in a university and is now required to use Python in the new job, well you, you may tell me that, oh, the person needs to learn Python or whatever the tool that's required.
And while, yes, there are ways to bypass that altogether, so I'll give an example of a technology that I personally know well, SAS via, for example, is an agnostic platform. So as a data scientist programmer, you can code in SAS , R, Python, or you can do point and click, and [00:23:00] all those options are equally available to you.
So coming back to our scenario, a newly graduated person can join a company and start utilizing their skills right away. And that makes the management happy because they're realizing they're ROI faster, and that makes the teammates happier because the person's able to contribute faster.
So everybody wins. So it all boils down to again, technology lending a hand to humans where we're starting to fly kind of above the hard requirements or the thematic requirements I wanna call them, of knowing a language X or Y, but rather we're starting to understand that the talent of a person. I'm gonna use this analogy, whether I'm building a house with a blue hammer or a green hammer shouldn't make a difference cause the hammer is a hammer. So by simplifying those entry requirements, we are making everything better internally and externally.
[00:23:59] Michael Pupil: [00:24:00] Yeah.It's funny, I think very often in terms of technology, and this is gonna sound very strange because it's more of a hardware piece, but it's definitely technology, I think of the evolution of telephones. And it always starts with, the old rotary phone where you'd have to spin the digits and then it would go to that almost backpack type phone.
Then it was the brick that was massive. And then ultimately to those little flip phones that were the big thing. And now obviously the smartphones and, through the years and as the generational, changes had happened, we picked on a couple of words. Things like ease of use were easier.
Well, those rotary phones, while they're nostalgic and they're really fun and a great challenge for any older generation to challenge the newer generations to make a phone call on and see if they can get it to work. Uh, the ease of use to one of those things, even for somebody who knew how to use it, was a pain if you messed up the last digit and had to start all over again. And a telephone is a telephone and what the purpose of that tool was, was to make a phone call. And even in something [00:25:00] as simple as red hammer, green hammer, or telephone and telephone, um, there is technology that is adopted to there and that advent of that technology is what really drives the market.
And that's where, my point was, The nice to have is no longer, applicable. The need to have is really coming from the consumer side. Nobody wanted to, kind of spin those digits again, they wanted the flexibility and the mobility from the telephone. And that's why those older phones came with a cord that was 75 feet long and always tangled.
And that's why we came up with the brick to help us walk around. And then of course, those were too big, so they had to get smaller to fit in our pocket. And I think the same thing happens. You know, on a lot of different financial services, um, technology, right? It's why the advent of the tap to pay or the scan to pay, became a big thing.
And again, onto the collection side, I think making it easier for individuals to engage in that transaction and have that [00:26:00] choice is what marries empathy to technology. Normally there is an awkward state of mind frame when there is collections involved and allowing an individual to self cure is a really powerful thing for them.
If you had an opportunity to say some advice to an executive team or a CEO, specifically managing, a financial institution, what would be the first kind of thing that you would point their way?
[00:26:27] Alexandre Semenov: To have a flexible mindset with regard to how customers desire to interact with institutions. I'll try to frame it through an example. Through various studies that I've seen, we definitely see a shift in the mentality across the generations. So for example, the younger folks who tend to spend a lot more time with regard to the older generations on their smartphones, they prefer to [00:27:00] interact let's say SMS or email versus an in-person call. So grounding this in the collection reality if Michael sends to Alex, and Alex is a millennial such as me, an SMS, I may be more prone to open it versus if Michael calls me and says, Alex, you you owe us money, you need to pay now. And I see that it's Michael calling me, I may not pick up the phone because I'm in the meeting, I'm busy, or I just do not desire to have a human interaction at this moment on a Monday, right? However, if the system that dials allows for a flexible interaction, AKA, let's say there's a prompt that sends me a text and says, would you like to engage in a telephone conversation, SMS conversation about the repayment of your debt for X, Y, Z, and I say, yes, click yes or no. You [00:28:00] say, click yes. Then like, would you like to make a payment in full? And I say, no. Then, the smarter prompt would be, would you like to make an installment payment because we have that option?
And then I'd be like, you know what? Okay. How about I give you half today and the other half with the installments over the next three months, and that flexibility is something that may fit my budget, my wallet, and my preference with regard to communicating with an institution. In the studies that I've seen, the flexibility of channels have helped companies have higher recovery rates, and we've seen it.
Even, even earlier institutions who were collecting data on when to call a person, so let's say call on a Monday and Tuesday, but not after 5:00 PM so having that data and having monetized that data, those institutions were able to achieve [00:29:00] higher cure rates, higher recovery rates, and higher customer satisfaction rates.
Because at the end of the day, some of us are just forgetful payers. And that's behavior for, like for me, for example, so I may go on a vacation and just forget my bill, and I would not necessarily feel bad about it because I know that my credit score is still gonna be high, but I will opt for having a peace of mind and being somewhere on a sunny beach and not answering collection calls or emails.
So I'd rather have a peace of mind and wait for 10 days and make payment. But that's a personal preference. But again, that personal preference, I would appreciate if somebody was to account for it.
[00:29:44] Michael Pupil: Yeah, I mean, talk about a great backdrop to being late on a bill on a beach somewhere sunny especially for, for two Canadians on the line. I think you're a hundred percent right. I think that the strength, Alex, that you work within SAS is the flexibility and just the way that you,[00:30:00] see from a very high level, you know, the problem statement, in isolating the path to possible solutions, which is probably why you were so successful at what it is if you do. Um, and obviously you've said that you've been late on a bill, and what's really interesting about that is that it's not even a financial reason as to why you are late on a bill. It's more of an, I'll call it an administrative task, and all the more reason to leverage things like technology in order to engage you at the right time, in the right space in order to execute the transaction when it's going to happen.
I think that's what you were boiling down to.
[00:30:36] Alexandre Semenov: Absolutely. And I frankly love the offer, the ability that the banks are now offering is to have automatic withdrawals from your account. And to me personally, I think this feature should have been implemented years ago, uh, across the industry because it's a simpler reminder.
t's bigger than a reminder, but in an essence, it is a reminder [00:31:00] just with the automated payment made for you to pay your bills. That's it. So instead of you typing on your calendar and making yourself, notes and meetings and what not to do, X or Y, that task is just being done for you.
And that's a very basic, but it is an example of technology. So now bring that, elevate that to a different level where you have AI doing that for you based on a prediction that was generated through an algorithm that analyzed two years of your calendar and picked up a pattern that you do your cell phone payment on every 17th of the month.
[00:31:39] Michael Pupil: Yeah, and that's the trigger. And there's the marriage of machine learning to AI, to technology to ease of use. Execution of transaction, very low cost overall. I'm so excited that you were able to join this, podcast, Alex, and maybe just as some final comments, I'd love to ask you maybe something a little bit more on the personal side.
What are you ending, 2023 with? What [00:32:00] initiatives are you tackling and of course, what can you speak about that you're passionately, tackling over the next six months of the year?
[00:32:07] Alexandre Semenov: I am knee deep in modernization journeys across several financial institutions and wearing my professional hat, I'm happy. And frankly, privileged to be working with a level of individuals that whose skillset is just amazing and astonishing because those folks are just so smart. So the combined, talent and brain power of the individuals I work with is what makes certain projects successful and slam dunk small teams, very capable individuals that make things happen. And being a part of that journey is truly humbling and frankly amazing because in a ever so complex world, a phrase that [00:33:00] you heard me say three or four times today, it takes different skill set, it takes different expertise all combined together to make things happen. Now, wearing my consumer hat as Alex in day-to-day life, I'm also happy to see the modernization efforts that are happening across the industry because me too, I have my worries about the data protection and security and whatnot, and I'm very happy to see the steps that are taken by the institutions to make sure that the consumers are protected from a data standpoint, from an integrity standpoint, and ultimately those investments will lead to a greater consumer satisfaction.
[00:33:42] Michael Pupil: Yeah. Excellent, excellent answer. And, just to leverage your first one, I think you make this podcast better and it's the fun part of my job is to bring on people that are wildly intelligent in that, Alex, you definitely qualify in that category. Thank you for [00:34:00] simplifying some complex, questions, scenarios, and what we're seeing and breaking it down into what I'm hoping is consumed as simplified answers and an ability to make decisions to drive ourselves forward.
So thank you sir for joining us. Um, a pleasure as always. We still have a lunch that we need to, to get together with, so we're gonna be coordinating that fairly soon and I really look forward to that. So thank you for coming.
[00:34:24] Alexandre Semenov:Thank you for your time, Michael, and I look forward to speaking with you again.
[00:34:28] Michael Pupil: Wonderful. For those listening, this uh, concludes our latest episode of Connect and Collect. And, be sure to tune in next month with the newest edition. We look forward to speaking with you again. Thank you everyone for attending.