On a Mission to Humanize the Credit Collections Industry: Abhishek Goel, Dasceq
Lee Razo talks with Abhishek Goel, founder of Dasceq and "Chief Behavior Expert" about how Dasceq makes use of its unique combination of deep industry knowledge and experience with the latest AI and machine learning techniques.
…at that time, AI was coming up and I already had all the necessary background of AI. And I was doing a lot predictive work, and then I had to really switch to AI and machine learning and all of that. But what I really realise over a period of time as I started working more into Dasceq, it's a like a tool, as you said. It's just one piece of the entire component.
And that's the reason, as we deepen the product, and as we have been in the industry, as we've been through the COVID, we have constantly worked deeper… we constantly engaged with our clients, with our prospects, with the pain points of the consumer.
Trial by fire and the role of the "Chief Behaviour Expert"
How technology improves customer experience and in turn improves collections efficiency
Compliance is an opportuntiy to create new channels of communication with consumers
How Dasceq works with customers to gain trust and deliver results
Protecting consumer privacy by using alternative data sources for AI analysis
From the finance industry to AI: The story of Dasceq
Welcome, Abhishek Goel from Dasceq. Very pleased to have this opportunity to speak with you today and learn about your past, about Dasceq, Dasceq's mission, and just talk about, you know, technology and finance and all kinds of fun things.
I'd like to start with, actually, your title, CEO and Chief Behaviour Expert.
That's a very interesting title. Curious about that and a little bit about your background and how you got to this line of business you're in now.
It's crazy, Lee, why - how I got into this business. Let me answer that, and the title also will come from that.
You know, about 16 years ago, roughly 16 years now, going up to be 17, I essentially started my first job out of grad school. And I was in Minneapolis at that time and I was working for one of the largest bank in the U.S. And, you know, my first day on the job, I was so pumped up, right? I just got my Masters in Applied Economics.
I'm a big believer of data.
I was fortunate to start when the rave was starting about data analytics, and all these different kind of KPIs and all these Oracles and Sybase and all these really cool visualisation tools were coming up, or "reporting tools", as they used to be called at that time. And I got into my first job, first day, and I'm so pumped up. And my manager says, "Hey, Abhishek, go and talk to Rodney.
Rodney is our collection manager and he needs help with analytics." And I was like, "Yes! There I go." And I go and knock the door of Rodney.
He was just right across the corner in the same floor.
I knocked the door and Rodney looks at me like, "You seem like a new kid out here." I'm like, "Yeah, I just joined today.
I'm just fresh out of university. And my boss sent me over to you." I'm like, "Why?" And he was like, "Why?" And I'm like, "Because he said that you have some analytics job that we can help you with and that will help you collect more money or something." And he was like, "You know, get out of my office." He did not even offer to give me a chair or stand or something.
Like, "Just get out of my office and close my door on your way out please." And I'm like, "What the hell is going on here?" I was completely, I don't know, surprised? Or completely sad? Or like my big, big Eiffel Tower of "I will do so much good" just crashed, right? And I had no words at that time. And I never thought that one experience was going to shape Dasceq, actually. And what happened is that I thought about it.
I went to my manager. And he said, "Hey, Rodney has been hard and has been difficult and he doesn't like to work with us." And I'm like, "But why are you sending me? Why I need to be the chicken in the story, you know? Why I need to be the scapegoat out here? I'm the goat right now." And he's like, he has given me up already.
It's my first day and I'm like, I don't know what to say and I don't know what to think. And the next thing I did is I walked up to Rodney's office and I said, "I understand there's a lot of background and past out here, but I am new and I would love to take this opportunity to work with you and help you in any way." And he's like, "You know, Abhishek, since you are the first one who came back second time and third time and fourth time to me, I will offer you this.
Why don't you sit with my collectors for one week and see what they do? Before you come up and give me advices and data and all that stuff, why don't you understand what we do?" And that was my first lesson of life in analytics.
Like, you need to listen and understand others before you want to be understood. And this is a classic data science problem.
Most data scientists think that they are the Wizard of Oz.
They think they know everything.
But, unfortunately, we don't. And that was my kind of distinct - and I went in and did my - you know, there's a saying that you have to pay your dues before you get to hear - the people hear you, right? So I went in and paid my dues. And I sat with them for seven days and understood a ton-load about collections and their problems and processes and all the different things. And then Rodney gave me one or two problems to solve and I got going. And from there, I worked on a hundred-plus collection projects.
A lot of them with lot of large banks, and then a lot of large companies as consultant as PwC. As I worked with these companies myself directly, so I've worked - I've been fortunate to work for top 10 financial services companies - fortune 10 financial services companies and few top 5 print-to-tech companies in my career. And one thing that I consistently saw was there was a gap between - in collections, there is a opportunity to put more analytics, put more AI, put more machine learning. And when things are coming together for AI machine learning, I thought, instead of making solutions for different companies, I can create a product that can really help all the different collection shops.
So that's how I started Dasceq.
My whole focus is to bring the next generation - not just the technology, and that is where the behaviour expert comes in.
Because collection is a very high-touch sport.
You cannot say that, "I would not get my hands dirty." And, in this case, "hands dirty" means I would not talk to a consumer.
There are pieces of technology that can be helpful, but technology is only going to do so much. And that's where I thought that... I became... Over the period of working with collections and other project, really the core of any product is how much do you understand a consumer? And what we have done at Dasceq is we look at consumers from different aspects. We have started deep research that brings behaviourists - consumer economist, behaviourist, that are going deeper and understanding why a person is not picking up the phone.
What is going on with our digital world? What is going on with our compliance? What is going on with technology, right? And we bring all of that together.
So that's the reason I like to call myself a "consumer behaviourist expert", because I like to understand behaviours and bring together a solution. And we have multiple groupings that we work with our clients to - technology is just part of it, but how do you engage with the consumer to make sure that we deliver the results? We like to call ourselves - we help collectors solve the deepest problems in collection. We help collect from the deepest - we help collectors collect from the most difficult consumers.
So, yeah, not just an AI company.
Yeah. I really love the story, your trial-by-fire story.
You know -
In this day, you know, you can't really help someone solve a problem until you understand how the problem got to be there in the first place.
There's usually a good reason to know why it exists.
So, I mean, what a great way to learn. And I think that the best technologies and the best solutions come from, you know - they source from that very audience, that very problem, where it comes from.
So one thing I didn't really go into a lot in the introduction was what Dasceq actually does, which you're using basically technology to improve this process, to make it more efficient.
Can you tell us a little bit about how that works? You've mentioned AI and big data.
Is that what you're focused on?
No, we are just focused - core of Dasceq, we as a company help collection executive collect dollars from the most hardest consumers. And as part - in order to really fulfil on our goal, on our vision, for our clients' collection, we use different AI technologies.
AI is part of it. We have our own technology stack that helps us to connect customers digitally, that helps us to understand the consumer behaviour. We have a pretty deep data set. We have more than eight billion data finds that we have collected by moving around 140 million delinquent accounts.
So we have very deep technology using alternative data, that really helps us understand those consumer, prepare our algorithms. And we also have behaviourist experts who really sit and understand how do you engage with consumers? What is there and how do you engage them? So that's why I'm saying it's a full-fledged company.
It's a full product.
It's not just about just giving you a score, like lot of other companies, and then, "Here, you figure it out." We have really built a blueprint of how do you really engage consumers? And we have different products, depending on what our clients are looking for, so that they would be able to get the max out of the most difficult customers.
Or difficult consumers, I should say. And the most important thing about Dasceq that is there is we are empathy-driven collection shop. We believe in consumer experience. We understand consumer. We have the voice of the customers.
Most of those consumers were delinquent. We understand what stage they are, what they're going through, and how do we approach them, how do we collect money from them.
AI is just a component of it.
Right? So that is where Dasceq is different. We really focus on first-party collectors. We really don't work with debt collection companies. We are highly compliant, hundred percent auditable.
A company that can really back you up at hundred percent validation and hundred percent audit data, which I don't think a lot of companies are able to promise.
But we promise and deliver on that.
Yeah. So you're really looking at the whole picture, basically. And then the technology is a tool, which I think is exactly the right way to look at it.
You know, I'm coming, myself, from the tech industry, but I know that we tend to get caught up on the tools themselves. And, in fact, they're really just a means to an end. And so you're coming from a very holistic view of finance. And then using what you know about data analytics and collecting data to apply it to these use cases.
Exactly. We basically say that we have a 3D framework, a three-step framework that we apply, depending on where the clients are and what their needs are to collect the hardest dollars.
One is technology and AI.
Second is what we call "strategy".
The third is what we call "compliance".
Because in different industries in different states of their collection, there are technology - there's a different need of the technology, there is a different way to approach those consumer that is you need a different strategy. And then the last piece is the compliance part plays a little bit of a different role.
But we go all-in. We have this little three-legged stool that we have implemented with multiple clients and got massive results that was not expected.
So we have a full-fledged - as you said, technology is just a part of it. And we have this blueprint or framework that really helps you to collect massive dollars from your hardest consumers.
Yeah. And then, actually, compliance is a really interesting topic because you're really working in some of the most regulated industries in finance and healthcare.
How do you go about that? And how do you actually add value in that area?
When I was talking to you earlier, you mentioned that what is our deepest challenge? And what is your deepest opportunity? And I say that that is regulation and compliance.
That is the deepest challenge, but that is the deepest opportunity.
Another reason I say it as... Today, there is so many new laws that are there which are basically helps the regulators to regulate the industry for the favour of the consumer.
The regulators are constantly thinking how to really make sure the consumer is not impacted due to some bad actors.
I'm not saying everybody in the industry's bad, but there are some bad actors for which the whole industry is blamed. And we understand that. And that's the reason we do it in reverse.
In understanding the consumer behaviour, in understanding their digital behaviour, in understanding why people tap their smartphones 2,200 times on average a day? And how does that change their psychology? What is the stimuli that Apple and Samsung, that Facebooks and TikToks of the world are giving to those consumers? And how our lenders, how we can leverage those stimuli from a collection perspective.
So that's the reason it goes beyond technology.
You have to understand. And that is what we are doing.
We're understanding stimuli. And coming to back, there's opportunities because now everybody has a level of playing field.
Things that you knew 20 years ago has no relevance today. We can keep calling the customer for 1,500 times.
The guy has logged your number.
You can call him thousand times, it doesn't matter anymore.
Right? So compliance is the biggest opportunity, as well as the biggest stress. And we take it as an opportunity because there's a new regulation which allows you (audio breaking) connect with consumers digitally.
I think it's the biggest opportunity out there because now you can really send email, text messages, and connect with those consumers.
But the question that we are struggling everyday, there's a lot of information out there, but there is a dearth of quality information.
There is a dearth of how to apply that in theory. We all read so many books. We all can be jazzed up with so many great quotes. We can read - but the question is how do you implement in your daily life? And that's what Dasceq does.
It does not just talk about the great AI technology.
It does not talk about AI behaviours.
Let's talk about how to engage consumers.
But we have proven it again and again with multiple clients that what we are proposing, we make it happen. And those are the clients that we really love to work with, people who are consumer-centric.
They love consumers, they want to engage consumers.
They want to go deeper in technology.
They are looking for a new way.
Right? Because if you just to the old way, you'll get the same results.
So Dasceq is all about a proven way that can help you get results.
Yeah. And I think when a layman like myself, who's somebody who's not in the finance industry or the debt collection industry, when I hear about debt collection, my first thought is to think of something very Orwellian.
You know, like they're going to come and collect from you and find you.
But, you know, it's a system that -
The baseball b at.
Yeah. On the other hand, though, the more efficient that that system is, in fact, the better it is for good consumers.
You know, the easier it is to get loans, the easier it is to service them and to deal the creditors.
How do you work with your customers to ensure that the customer experience is kept high using these methods?
Number on, we show it by dollars that we are collecting. We show it by efficiently that we are collecting.
The second thing is the number complaints that they get reduces dramatically.
The third way also is, you have to understand that, if you are calling a customer 10 times, versus you're calling a customer one time and collecting more dollars, which has got more consumer experience? Who wants to get called 10 times today? I don't know anybody who wants to make - so a lot of time my customers, like - a lot of times some of the folks that I get into this argument in the industry that they say, "Oh, you know, Abhishek, this is not right.
How can you prove consumer efficient?" I'm like, "Shall I call you 10 times in a day and not call you? Do you want to understand the difference?" Unfortunately, there are not a lot of quantitative measures in the industry.
Right? But my customers who work with us, they don't land up in a CFPB complaint list or their number of complaints have gone down significantly over the months or years that they work with us.
So that is another way to say that - that proves that this is a proven method that gives bottom-line results because I know ROI is the most important thing for a business, right? Even for us, ROI is important.
I'm not going to deny that.
But, at the same time, how do you really take opportunity? And in order to take that opportunity, I just want to bring back that conversation that you have to do things differently.
Be willing to do things differently, right? And be willing to do in the right way.
See, there are hundreds of company, thousands of company that can send an email to you, email to any customer.
There're thousands of technology. And people have been using them, but you don't see results, because how email used to be perceived even one year ago is different than how it is perceived today by the consumer. And that is the reason you need a different approach.
You need a different partner who can understand what is going behind the minds of the consumer.
Right? It's just not an - if I would be a collector and I would be doing this for 15 years, the first thing is, "Hey, pay my bill." But now just saying "Pay my bill" has no value. And I cannot just keep saying "Pay my bill" 15 times and somebody's going to engage with me.
So the rules of engagement has changed. And that's what our product does.
It continuously evaluates people during COVID.
I call it "post-COVID" or "post-lockdown", however you want to say.
But what is changing and how do we really keep with that change? That's the most important thing any company has to do to make sure it's able to collect from the hardest consumers.
Yeah. Yeah, it's true.
The technology itself is changing so fast, that how people are engaging. And I think you mentioned one thing that reminds that, you know, the long haul, technology itself is the easy problem to solve.
It's the trust -
You have to build in it.
You know, that people actually have to trust it. And that's the long work.
That's the hard work.
Yes. And we do have very simple processes that we have known that are used for delivering more than 300 projects, which really does not require a lot of effort to really to get this going and test it out.
So we are not a company - we are a company that will always come and validate and show you that our process, our methodologies, our product works, right? And we invest into our clients. We invest pretty heavily into our clients and we explain to our clients why you are not going to see an overnight impact.
Because if there's an overnight impact, it will be an overnight drop also.
A lot of customers think that way.
The reality of life that we are living, this is a very hard problem you're solving.
It's not a simple problem.
Yes, we have packaged it, we have made it efficient, we have made it great.
But it does take a little bit of time to show the impact.
You cannot just plug something in and then see an impact tomorrow.
You have to validate it. And then if you see an impa ct, but is it consistent? Once we plug in our technology, people will see the impact within first month.
But then I say, "Guys, please work it for second month hard before you start changing your entire strategy because you need to believe in a ton.
Believe doesn't happen by one month or two weeks of performance." And a lot of people really come back and say to me, "Oh, it's working for two weeks.
Let me change the strategy." And I'm like, "No, please don't do it because we are not fully tested and kicked all the tiles.
Let it run for another month and then I would be more happy." So I'm the person, because I've implemented this many times and I understand that sometimes that my clients become very excited and they want to just go all-in, and I'm like, "I want you to build trust.
I want you to do it the right way.
I just don't want to do something and regret after one month."
Yeah. And anything this important, this complex, takes time, obviously.
Yeah, that's very important.
Yeah. So I'm curious as well, what is then the Dasceq technology? Is it a software that you put in? Is it a service? How does it actually work? How do you engage with it?
There are multiple products that we have.
Some products are as simple as a scorecard, but we kind of work with our clients to understand what is their deepest problems and where they want to really - or what are the deepest opportunities and how - in our experience, using the score - using the AI product would help them to get results? So AI scores are one piece offered. And then we have implementation blueprints, where we work with our client to change their strategy, to change their compliance, make sure that we are fully compliant based on their standards that they have set internally.
So we really have a full implementation plan around it also.
So it's not just giving them the score and say, "I'm gone," but it's about really implementing the score and implementing the new set of strategies and doing and walking them through that process so that they can see the end of the tunnel.
They can see the results. And then we kind of let them take it from there. We also have what I call a "digital collection service product", which completely takes over. And we work it on the early-stage collection. We do it on debt collection.
It's the product that really takes care of the entire collection soup to nuts.
They don't have to anything, full integration.
Also they just give us their account then, hey, we take it from there. And then we also have other products like, which we understand, what are the best time to call? What is the best agents? How to deploy agents? So we have multiple products around it based on what they are looking for.
So it's a lot of - definitely the idea customer interaction, as well as the hard numbers, the hard finance data.
Both are there.
Yeah. So you're collecting data on all these sorts of things, and then you're processing it with various methods, including, you know, machine learning and AI, and then coming back with a certain score or, basically, some kind of a result for the collectors.
So we have what I call a "full buffet".
So we have a full buffet where we actually give you the next best action across all your channel, that could be email, text, online interaction, push notification, your website banners also.
So it does engages a consumer full-on. And then we have pieces of the same product depending on where you are and what is required.
So we have the collection score, we have the best time to contact the customer, we have best channels to contact the customer, we have best agents to work on certain customers, we have optimisation product. And then we also have this full suite, which basically engages the consumers in person and does their entire job, like the entire digital collection is done by us well done.
Okay. And what kind of industries do you work with? What are the sorts of customers that you're engaged with?
That's a great question. We work with banking, lending, fintech, healthcare, utilities.
So we pretty much work - in the industry we call it "first-party collectors".
These are the people who are trying to collect any kind of consumer debt, but they are trying to collect themselves.
Or their using a third party or they're using a collection agency, but the collection agency uses their name to collect their dollars.
So we work with anybody that basically uses their name to collect the money. We do not work with any, what I call, debt collection agencies.
These are third parties who basically buy their debt and try to collect money.
Because there are a lot of bad actors out there and we really want to make sure that we are also really bringing consumer satisfaction along the way. We want to make sure that we are empathetically collecting and we are doing the right thing.
See, with AI, there is a lot of wrong things that you can do and try to get away.
But those are all shot down.
So in order to really protect ourselves as well as our clients - or I should say, in order to really protect our clients, we decided we want to really work with first-party collectors because they care about their customers. We care about our customers. We care about those consumers that are in collection.
Because we all know that in different parts of our lives, in different stages of our lives, we might have been there or we might have come very close to being in the same process and how it feels.
So, again, I cannot change the whole industry, but every interaction that we are doing is bringing us some kind of a positive impact to the consumer as well as to our clients.
Yeah. Yeah. Basically, everybody has some skin in the game in the long term.
In this case, you like the reputation, the customer relationship.
It's not really about going in there and being another middle man in this area.
Yeah. Exactly. We want to use the technology to improve industry, rather than to use that tool to run it in a wrong way.
Yeah. So, you know, I work a lot with IT infrastructure companies, which nowadays means cloud computing technologies, you know, multi-cloud, all sorts of data in the cloud. And one of the biggest challenges in the last couple years, especially where I am in Europe, are things like GDPR, the protection of personally identifiable information.
How do you deal with that? Because I imagine that in this type of data that you're working with, there's loads of that sort of data.
You know, how do you use it, make the best use of it? But also as - or more importantly, how do you actually protect the privacy of the people involved?
That's a great question.
I'm glad you asked that question. We do not get any personal information from our clients.
There is absolutely no Social Security Number, no bank account information, no birth information.
Not even an address.
Not even a phone number or an email that we collect from our clients. We can do that because when we started the product, when we started the company, I understood the pain of the banks and any company to give out the data set with the privacy protection, so I created a system that really does not require personal information. We rely on alternative data that is email-form text data. And this data set nobody was using - still not a lot of people.
Most people do not use this data set, which is really a treasure trove.
So most companies have relied historically on bureau data.
But then in bureau data, there are certain gaps that are there and you're not able to proactively connect with customers because you don't know who's a bad guy until he has busted multiple accounts.
If somebody has really researched this, there is hardly much you can do around that.
But if you can figure out, based on your portfolio data, based on all this interaction data, their behaviours, then there is not more that can be done.
There's a way you can engage those consumer, and that's what we do.
Though it's a compliance thing, but it also helps us to really have a superior product that nobody else is really working on.
Okay. And so, basically, you're saying your sources are different than traditional, where you say bureau data, like going to Experian something like this, your (overlapping conversation) -
Perhaps sentiment analysis or other other kinds of analysis on other types of data.
So we collect hundred percent of our data from our clients, but we focus on their portfolio data, the last 90 days, 180 days of payment histories.
The contact information of that customer for last one year, 180 days.
That gives us - and it creates profiles after that.
So that's where we have more than 400-plus variables that we have created to understand this relationship.
So what I'll give you a simple example is, let's say we work with an auto lender.
So we will basically - we understand.
A person who drives a red car, never had a payment problem.
What is the likelihood that will give the payment in the next 10 days? And, more importantly, what should I do to get that payment?
Yeah. That's very interesting.
Because there's so much data out there now that we're only just now learning to tap.
It's like oil, we're just now starting (overlapping conversation), right?
So this gives us a really rich profile of consumers to work on.
Nobody was really tapping into this data set that we are tapping into. And that is where our product is really powerful.
Though we do not take any third-party data sources, that's absolutely fine.
Yeah. So, you know, another thing that I've learned along the way as I've learned more about topics in finance in general is that there's a very strong notion of something called "opportunity cost", you know, the cost of what you didn't earn or money that you didn't make because you made a different choice. And I often use that analogy when I'm talking about technology and data in particular.
You know, what's the opportunity cost of not doing a data-driven project in your company, etcetera? A potential customer of Dasceq, a collector or somebody in finance, what kinds of things should they be looking at? What should they be measuring in their own situation to decide if this is something that could help them?
See, the biggest thing that they should be looking for is what is their goal for this year? Or what is the goal that they want to achieve next year? And what are the new paths they can find to achieve that goal? If you are able to take the same path, then it's going to give it the same results, right? So if you're looking to really... If you are not looking for a traditional AI company, right, then we are the right choice.
But if you are really thinking that using the bureau data you would be able to gain some other additional efficiencies, probably that is not true.
Those efficiencies should have already been gained in the process, right? So we really like to work with the clients who are looking for high growth, who are looking for collect more dollars, who really have this tough problem, that, "Abhishek, I want to really work on this specific segments of customers.
I have not been able to collect with them.
I want to try digital with them." They are one year old, they are six months old, they are two years old.
But I really want to make sure that I work with a partner who can show me the long-term collectivity of this customer.
Not just collect the lowest hanging fruit, but how do we really go and collect long-term? So the opportunity cost out here is not doing something.
In collection, the good thing is that you already have a pool of accounts.
You already have big debt that needs to be collected.
The opportunity cost in terms of that can be an interest payment, can be that the same consumers coming back and taking a loan from you.
You know, because if you have really big set of accounts that are just sitting idle and you're not regiving loans to them, you are losing money.
Right? So if you're not settling them or the second opportunity cost can be legal cost because if you're given sold those debt out to really what I call "rogue people", then there's a huge loss that is just waiting.
It's a ticking time bomb, you know.
Because one day some of these customers will convert into a complaint and that will convert into reg action and then it's the regulatory legal fee that is coming your way in boatloads.
Yeah. Yeah, no, I've seen that in so many different industries.
But I think I like the way you're looking at it.
To me, it sounds like that the differences you can try to improve incrementally what you've been doing all along. And, you know, often the customers that come to you have tried this approach for some time and see that it's not making the progress. And the other is to try something completely different, maybe even transformational, in a good way, you know? Obviously, they could try out your product without disrupting their ongoing operations.
They can as well do the thing on the side. And so you're finding, like, younger companies are often open to this, but the larger ones have the biggest opportunities to make value in this area.
We find both. We found both kind of companies. We were working with some of the largest banks in the country within U.S. And we are approached and we are in talks with some of the largest banks in Europe also about our product.
So people do understand the difference that we bring because we are not just - like when we talk about technology, when we talk about AI, we really can't walk them through what we do really comfortably because we know that what we do, nobody else can do it. We know that it comes from 16 years-plus of my experience and the industry knowledge that we can walk them till the T that why we think this is going to work. And we answer all their questions to their satisfaction. And we are willing to work deeper to show them those answers that we have. And that is where I think people like to work with us.
So we really like to work with folks who want to understand the details, who want to understand how is this going to work.
Was it somebody who just walks in the door and he is just shopping around for the cheapest product or the coolest technology, right? We may not be the coolest company out there, but we would be the company who would deliver. And we can give you the list of our clients and you can go and talk to anyone of them.
One thing that our clients keep coming back and telling us that, "Dasceq did not stop.
Whenever they faced a problem, they acknowledged it.
They came and opened up the discussion, and then worked on the solution and make sure they delivered." Which most of the vendors run away when they see a hard problem. We don't run away because we have dealt with many hard problems. We understand every portfolio, every company, is a little bit different, but our methodologies are really stackable and we can work through them for any kind of client with any kind of problems that they're having.
Yeah. And I think that's a key result, a key benefit of the fact that you really came from this field yourself, you know. And starting with your story of trial-by-fire, the very beginning of your career. And, actually, that's something that I find really interesting as well.
I think it's probably a good theme to explore a little bit as we close this out is a little bit about your journey.
I did a little bit of research.
I saw that you began very much in the finance industry with some of the biggest institutions, and evolved yourself into leadership roles, and eventually into the technological side, bringing you to founding Dasceq in 2017.
How did your journey take you technology in such a profound way? How did you get to that state?
That's a great question, Lee.
I started Dasceq with only one goal, that I want to make difference in the industry.
I want to make a difference in the lives of the consumers that I work with.
Right? And what happened is that, when I was working with multiple companies, one after next, I kept facing the similar challenges between each of those companies. And, again, I will go and build it up, and then, again, I'll implement. And it takes time to build this technology.
The technology that we have is not a simple plug - we, today, can plug and play because we have built all the technology.
But doing this in-house for our in-house team takes 12-plus months, not to mention all the resources and the budgets and whatnot, right? So I was doing it again.
Right? There was nothing to pull upgrade at that time.
Because I was constantly building, and then I'm putting in the technology, and then again the conversation starts happening.
Budget and industry changes and recession and this and that, right? And privatisation.
So once I did it enough number of time, I started doing consulting for some of the clients. And when I started working with lot of the mid-tier companies, the budget became a constant constraint. And what I realised is that if I build a product that can mitigate all these concerns and I can really give it at affordable price, let me help the entire industry, and eventually will help the consumers.
So that's how I started Dasceq, with my vision of really taking conviction, really smarter ways of collecting, and really being able to collect the hardest dollars out there smartly. And in a consumer-efficient - in an empathetic manner because that is possible.
That is truly possible. We are doing it again and again everyday. And that's where the vision, when I see it clear, when I really saw it clearly three years back, then I said, "Okay, let's start a product company. And let's not just limit and - " The good thing is that at that time, AI was coming up and I already had all the necessary background of AI. And I was doing a lot predictive work, and then I had to really switch to AI and machine learning and all of that.
But what I really realise over a period of time as I started working more into Dasceq, it's a like a tool, as you said.
It's just one piece of the entire component.
It's just one piece of it, right? And that's the reason, as we deepen the product, and as we have been in the industry, as we've been through the COVID, we have constantly worked deeper. We just did not say, "Well, we are an AI tech company.
I just want to make scorecards." We constantly engaged with our clients, with our prospects, with the pain points of the consumer, like we did about... Just recently, three months back, we did a customer survey to understand what customers are looking for. And actually some customers really said, "Why can't we do FaceTimes? Why can't we do Skype or WhatsApp calls? Like video calls.
Why can't we really set up an appointment? The time that works for me so that I can have this conversation." And we are really implementing those things. And when we talk about some of these things to some of our lenders, or some of our prospects or clients, and like - it takes so much effort to really implement something that's simple for them.
But we already have the technology. We can plug and it's very easy for them to just implement it.
So that's where I think the technology keeps changing.
Consumer behaviour keeps changing. We need a company that is on top of it, right? We cannot just follow the old system. And we cannot wait for things to change and then wait for them to catch up, right? The waiting time - because if you are waiting, you're losing out.
Your opportunity cost is becoming bigger and bigger everyday you wait.
Yeah. And you mentioned this is the thing about FaceTime, yeah, I think now more than ever since this last year of lockdown and pandemic, we've become very used to a remote - you know, Zoom and all these different things. And I think they're going to - you know, the regular consumer is going to start expecting this sort of interaction.
And I was reading some surveys, and I think the last 18 months or 15 months have done more for digitalisation than it did for five years.
Yeah. Yeah. That's been profound.
You know, suddenly - right? And there's a huge amount of opportunity, and that's what we do, right? That's what I was saying.
The biggest challenge is compliance.
The biggest challenge is how do we collect? But that's the biggest opportunity because the world is changing rapidly and there are lots and lots opportunity that any lenders can use and take with the help of Dasceq to really reduce and collect more dollars.
Yeah. But one thing is for sure, you're going to be very busy because things change so fast.
So whatever you have now, it's going to have (overlapping conversation) soon.
Absolutely, absolutely. And that is what I love, actually.
I love to be able to create and bring more solutions to the industry.
I look forward to it.
I look forward to talking with you again and keeping track of all the things that you guys develop in the coming months and years and decades, hopefully, in this area.
So, yeah, it's been great.
Yeah. Appreciate very much, Abhishek, your time.
Like I said, I will include links to Dasceq in the comments here. And we'll look very much forward to speaking with you again and having more content from you guys.
Thank you, Lee.
Thank you for the time and the opportunity.