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AI-Powered Risk Analysis with Veejay Jadhaw from Provenir

For industries such as Fintech, SME lending, retail, and auto financing, instant application approval is essential—but risk assessment is complex. While consumers have come to expect a seamless process, businesses need to perform complex analysis to protect against losses and maximize earning potential.

Provenir offers a sophisticated platform to manage that risk. On this edition of UpTech Report, Veejay Jadhaw, Provenir’s Chief Technology Officer, explains how their system works, and how they’re helping major brands like Klarna, General Motors and Hitachi make smarter decisions faster.

More information: https://www.provenir.com/


Veejay Jadhaw is the Chief Technology Officer at Provenir,a global leader in risk decisioning and data analytics software. Mr. Jadhaw has more than 15 years of experience working in technology, fintech and financial services industries. Prior to joining Provenir, Jadhaw was the Chief Technology Officer at Finastra. He also served as the Global Head, Core in Cloud at SAP, and held various technical and leadership roles at Microsoft Corporation. 

Jadhaw holds seven US Patents, is a published author, and has spoken at various technology conferences around the world.

Provenir helps fintechs, financial institutions, and payment providers make smarter decisions faster by simplifying the risk decisioning process. Its no-code, cloud-native SaaS products make it easy to rapidly create sophisticated decisioning workflows. With a global data marketplace for seamless integration, powerful AI and machine learning models, and real-time insights, Provenir has supercharged decisioning speed.

Provenir works with disruptive financial services organizations in more than 40 countries and processes more than 2 billion transactions annually.

DISCLAIMER: Below is an AI generated transcript. There could be a few typos but it should be at least 90% accurate. Watch video or listen to the podcast for the full experience!

Veejay Jadhaw 0:00
So, novo development platform coupled with our domain expertise based content that sits on top of our platform further enables and expedites the deployment of new solutions or change to an existing solution at a lightning speed, right, you can deploy a new chain in a matter of days, not weeks, you know.

Alexander Ferguson 0:24
Welcome to UpTech Report. This is our apply tech series UpTech Report is sponsored by TeraLeap. Learn how to leverage the power of video at teraleap.io. Today I’m joined by my guest, Veejay Jadhaw, who’s based in Princeton, New Jersey. He’s the Chief Technology Officer at Provenir. Welcome Veejay. Good to have you on. Thank you, Happy to be here. Now Provenir is a risk data analytics and decisioning platform. So you guys are are focused on using machine learning in particularly in the finance and banking area to help basically companies make smarter decisions when it comes to the risk that when you’re lending money that they get that correct. Yeah, absolutely. That’s correct. Right. Okay. So how many to up to this, this, we understand that arena that you’re playing and, and you’re actually helping enterprise mid market, even startups in this space? What do you see is the real problem that that businesses are facing when it comes to dealing with finance, banking, having to make smarter decisions with risk?

Veejay Jadhaw 1:21
I think what we’re seeing right now is like a two fold story number one is really all about speed to market as is institutions wants to serve their customer better, and acquit away and deploy innovative products at a much faster rate. So that’s one one genre would say one element of the specific problems that we solve for, we enable customers to automate their business processes, whether it’s buy now pay later, or whether it’s credit risk financing, and in consumer finance, or middle market, credit card management, the insurance, payment space and fraud. So any any business process that they want automate our product, enables them to deploy those offerings to the consumers at a much faster rate. So that’s one, one problem that we solve, or the other one is really all about data. We is since we orchestrate most of these business processes that are talked about whether it’s lending related to you know, credit risk associated with credit card or buy now pay later or even financial payments, and so on so forth. We sit in the middle of a lot of data. So our platform and our product enables customers to make smarter decision not just operation reporting and analytics from a historical point of view, but also, how can they embed that analytics in the business processes. So they have predictive analytics in real time available to customers to make smarter decision and real time decision as the transactions taking place. So those are two high level categories. Now, within that segment, there are multiple use cases or multiple subs, sub segments that we solve for right, I think the biggest one that we’re seeing right now is in the area of buy now pay later, you have tons of demands. In fact, we were one of the few, if not the only company in the world that actually enabled, buy now pay later years ago, when was relatively a new concept. So we are also very innovative in that standpoint, we like to partner with customers who are on the cutting edge, and enable them to deploy new innovation at a much faster rate. So our platform solves for multiple things, both on the transaction side where it’s really more of a business process automation layer to credit risk financing, as well as on the analytics side, which is really all about making smarter decision in real time. And all of this is all underpinning machine learning and AI. So, you know, so I hope that you’ll

Alexander Ferguson 3:51
be able to use it taking a step back for a second and the challenge of both launching a new product, you said being able to do that faster, or being able to make smarter decisions with the race risk, and the data is coming through. Obviously, this isn’t new, I mean, finance companies, they’ve been doing this for a while. So what’s what’s the shift then? Like? What what’s the change is is they’re just looking at doing it this now differently because of machine learning? Is that is that how it’s changing the game?

Veejay Jadhaw 4:19
What I think is both number one, you know, traditionally, when you looked at the platforms or automated business processes, like let’s say, loss management system or origination platform from from a lending perspective, they had to build those systems in house and generally took months if not years to build those platforms, any single time that you make changes to the business logic, or if the industry changed, or there’s a new regulations coming up. The speed to market was substantially low. Right. So without product, we it’s a no code development platform so that the customers don’t necessarily need to go through the whole development lifecycle, and spend months in development and amounts and testing to roll out a minor change whether it’s a change because of a company To force or change because of any regulatory requirements that is coming up. So that’s, that’s one aspect of it that we enable quicker or faster speed to market because of a no code platform that we have that automates pretty much any business processes. And we have out of the box capabilities by an operator fraud, that enables them to do it much faster compared to a lot of our competitors. The other aspect is really all about data. So yes, the, you know, the analytics part, the left side of the applecare, the technology, which is really all about reporting and analytics on there for the last 20 3040 years. But the but it’s grown a lot in terms of data and availability of data. Right before I was even if you go back five years, on the five years ago, or 10 years ago, it was really looking at, you know, single dimensional data related to a transaction and then providing some analytics in terms of how did the business did or did not do or what are the operational inefficiencies in the business processes, and so on and so forth. Now, it’s really more about making smarter decisions. So with the availability of very large data sets that’s available along with alternate data regarding, for example, mobile, as well as tracking of, you know, internet usage and mobile usage, just to name a few. And the further adoption and maturing of machine learning capabilities, you can actually create models that are much more closer to achieve the predictions that historical view generally does not provide, right. So there’s been a lot of advancement in machine learning and availability of data that enables that, that has further advanced the analytic side of the world as well. So it’s not just about looking back, it’s also about taking the data that exists historical historically. And then also leveraging all the alternate data that’s available, there was not necessarily available even 10 years ago, and then developing the right machine learning models to predict or and the probability of the prediction is much more accurate because of the data and the and, and the algorithms available with, you know, machine learning models. So there has been a substantial shift in terms of how now customers and technology companies and financial institutions are looking at, you know, building and deploying new capabilities. It’s not just about, you know, a closed loop. Business Process Automation is about an open system that can enable quicker change due to regular regulatory requirements or real competition to accommodate competitive forces, but also looking at data and how we can better serve the customer that much better. And a wide array of data that’s available now, as compared to what existed in the past,

Alexander Ferguson 7:35
proven here Friday correctly, was founded back in 2004. And I imagine the environment has changed a lot since then. And you joined on, I think, 2018. Correct. So to be able to lead this further, what can you share of your knowledge, both of the history that proven here, where it started, and then you coming on of seeing how it’s changed of maybe even the challenge, the initial challenge that was being solved to where it is now.

Veejay Jadhaw 8:03
So, you know, even before my tiny I think Prunier did have made a very smart decision to actually look ahead of its time, I would say about a decade ago, where the no Goldblatt development platform was really more at its infancy, right. And our founder and CEO actually made a conscious decision to sort of divert a bit and move away from the traditional origination platform to more of a product that enables flexibility in deploying new innovation using graphic technology. I think that was a that was a fantastic decision. And today, we call it, you know, no code development platform 10 years later, but 10 years ago,

Alexander Ferguson 8:40
I mean, it didn’t have a name. And Larry, Larry Smith, right. Absolutely.

Veejay Jadhaw 8:45
So I think that’s the starting point of an amazing journey. I think since then we have grown double digits year over year, because our now the industry is seeing the value of of no code platforms. No, what is it do?

Alexander Ferguson 8:58
Like No, what is no code? How does it change? Is it just being able to provide access to more people to create a solution versus having to create a having a programmer? Do it? How would you describe the value of no code?

Veejay Jadhaw 9:12
Yeah, I think it’s twofold. One is, you know, it enables a much larger demographics to automate your business processes. So now you’re not just limited to, you know, very low level detail. c, c++, Java programmers writing business processes. So now you have diversified your, your talent pool, to build these capabilities. So I think that enables, you know, it gives opportunities for many other people who are not necessarily programmers and engineers, to write software to automate the business processes, which I think has a much larger social impact. So that’s one aspect, which is a benefit, I think, for the society as a whole. And for companies, of course, from a talent retention and diversification standpoint. And the other piece is really all about speed to market say the era has gone where we can actually deploy implement new products which can take anywhere from 12 to 18 months. I mean, that doesn’t exist anymore, right? So no core development platform coupled with our domain expertise based content that sits on top of our platform further enables and expedites the deployment of new solutions or change to an existing solution at a lightning speed. Right, you can deploy a new change in a matter of days, not weeks, you know, compared to what it was before. So I think those are the broader topics, in terms of does has shifted the or are shifting the industry as a whole?

Alexander Ferguson 10:31
would you would you say that no COVID, basically enabling every knowledge worker to be a software developer, then they develop the software,

Veejay Jadhaw 10:41
and this one really becomes an understanding the business process that you want to automate, right? That’s really the required skill set, besides some basic understanding of the of the system and usage and things like that. So the opportunity for business analysts, for example, who has limited or no exposure to writing code, can also build can also build software.

Alexander Ferguson 11:00
So it’s the access to being able to have the tools to automate, but also access to the data, to be able to automate things that the two combined create a good result. Absolutely. A couple of minutes right. Now for you, you I appreciate you sharing from the history of proven, you’re already focusing on the no code movement 10 years ago, your own kind of what you see as a CTO and your your track record of the roles that you played in many different companies. What are you most excited about? Like what gets you up in the morning and just say, Wow, this space right now, I am excited about x, what drives you?

Veejay Jadhaw 11:41
So I love innovation. You know, I’ve always been very interested in innovation. And technology is something which I’ve been, you know, involved with vital my child. So I just love technology, and what kind of specific industry and customer bonds that you can solve with the technology. But at the heart of all that is innovation. That’s what just keeps me going at Prunier, VR. I mean, our organization DNA is very entrepreneurial organization. You know, we a technology company, and innovation is on the forefront of everything that we do. So we are always ahead of the market, always ahead of our time, in terms of what we can do better to improve customer experiences, to be ahead of the competition, and also in generally what we can, what things we can do that are that is probably going to be disrupted, providing it gives me the opportunity to sort of use that as a playing ground. And sort of ground me with innovation. And we have a fantastic team. And everyone is very innovative and and want to deliver new software to our customers.

Alexander Ferguson 12:42
Appreciate your your your love of just innovation and being able to grow things. You mentioned this entrepreneurial, spirit or mindset of the company. I’m curious from your history and what you’ve seen and proven here. As a leader, if someone wanted to institute an entrepreneurial mindset, inside of their organization, like what are you guys doing? What have you found that has worked? Well, to keep that mentality, that mode of an entrepreneurial mindset instead of a business?

Veejay Jadhaw 13:14
So we want so I think there are two folds with number one is people and then the process of the world, right? So we have top down enablement and empowerment of employees, right. So everyone, everyone at the table doesn’t matter whether you’re the founder and CEO, or whether you are the chief technology officer or general manager, leading a region, everyone contributes. So so it’s a level playing field for everyone. And then contribution is very important. So it’s very self empowerment, kind of a culture that our founder has enabled. And that’s been there in the company since its inception. And in my last few years, I’ve seen that actually continue to grow. I mean, as our teams and size are doubling and quadrupling on a year to year basis, I am seeing that spirit not going away because it starts with people setting the right tone and establishing the right culture, which is you know, empowering people hiring the right people empowering them and enabling them to do what they do best. So that’s one. The second piece is processes, we want to want to retain a culture of innovation and entrepreneurship. So that means you know, we want to you want to be quick and agile in deploying new innovation to the market at a much faster pace, we want to make sure that our processes are lean, we uses agile methodologies, so that, you know, we’re not stuck in bureaucracy of doing a B and C in a in a, in a linear fashion. We We are very agile in our thinking. So our processes represents our thinking, you know, with the, with the mindset that we have, we are also a large company with growing so we also want to maintain scale. So we have enough processes to sort of enable scale and make sure that we you know, we have all the right discipline in place to scale up our core innovation and goal is how do we improve customer experience and how do we get to market faster with the world class products with Rate quality. So those are two things, right? We keep our processes, Lean and Agile. And we, we will live by that on databases. And then on the people side, we hire the kind of people that fits our culture, which is entrepreneurs, entrepreneurial hands on, and then a leadership all the way down is enabled to, to contribute on a day in and day out basis. So I think those two axes are enables us to maintain and sustain the entrepreneurial spirit that exists that exists in this company,

Alexander Ferguson 15:29
bringing innovation to the technical to the financing making area, I imagined that has its own challenges, because I feel like it’s in some ways a giant that doesn’t want to move, or rather, there’s so many restrictions and regulations around that hold it back. Which, how do you see that?

Veejay Jadhaw 15:49
Well, I think financial services a very exciting place to be right now. And not just right now. But I would, I would say in my last 18 years that I’ve been in the industry, I’ve seen a significant shift in terms of the innovation that’s happening in the financial services space. I mean, there’s tons of money, magic out money available for financial services, that is significant amount of startups that are that are being created in financial services sector all over the world. And then not only that there are you know, large financial services institutions, they are actually acquiring a lot of startup companies to sort of enable that innovative set of a culture within that or large noddings exists under observation,

Alexander Ferguson 16:29
you don’t see any restrictions or anything, you think you just see it as an open book could open opportunity.

Veejay Jadhaw 16:33
Well, you know, so there’s, of course regulations, I mean, we have to comply with the regulations, and the banks and institutions have to comply with the regulations. But that regulation, those regulations are, are not necessarily affecting innovations in the area when we engage with the customer, right? I mean, there are some regulations that we have to the day the banks have to instill or implement, like, for example, lending or your customer and all the processes that goes around. But that does, but you know, what technology and innovation is doing is making them much faster and even complying with those regulations. So so it’s a regulation is not impacting financial services, ability to innovate at all. In fact, I think it’s quite the opposite. If you just look at just recently, it wasn’t the announcement was made by Apple, and Goldman regarding the the buy now pay later, and there’s tons of startups and other institutions venturing into the buy now pay later paradigm, which is, you know, relatively new concept if you if you think historically, right, so, and then, you know, if you look at the financial services as a whole, the era of brick and mortar, financial institution is kind of fading away, right, I mean, the concept of financial services changing quite a lot. So they can digitize a digital experiences. And where financial services essentially is going to be like, kind of like super Amazon, where you have multiple products offered by different institutions who specialize in specific products. And then there are these, these fintechs, or these digital companies that enable them to set up to sell them or provide a digital storefront. And therefore, in the end, you know, there’s a women story for everyone, I think. And I think the consumer wins in this space, because now the consumer has, has much more choices across multiple, you know, across multiple products, they don’t necessarily have to buy all the products from one institutions, they can buy one product from this institution, because this institution provides checking account capability, one of the best checking account services was another institution that provides the best, you know, mortgages for example. So they able to shop and the consumer wins. And the and and also on the consumer side actually encourages competition, and therefore, the pricing gets affected, right. So that’s the reason I think it’s a very dynamic industry now. And with all the innovation that’s happening, and all the venture capital that’s available and has been invested over the last five years, I think it’s just showing right now in terms of what the impact selling, I mean, we get calls from, and, you know, institutions that are not just large, multi billion dollar valuation companies, but also, you know, startup company out of XYZ countries is calling. So there’s a slew of investment and a huge diversity of customer bases that we are seeing in financial services. And we are right in the middle of now.

Alexander Ferguson 19:14
You see, I love you painting the picture that there’s a right for innovation, if anything, the regulations actually push to innovate even further. And there’s a lot of startups that are are making it happen. Are the traditional institutions, the large banks of different cases, are they keeping up with with the startups? Is it a neck and neck race? Is it is one lagging behind the other? And what do you see is the space?

Veejay Jadhaw 19:40
See? Yeah, I think there are certain advantages that the large financial institutions have in terms of, you know, the infrastructure and the operations, infrastructure that they have. I think the deposit volume is significantly higher. I think there’s a lot of advantages over there and a lot of advantages, even on startups that are so I think, so I don’t see that. That the debt is a complete substitution of financial institutions by the startups. But I certainly see that the the startups are creating a disruption in the present in the traditional financial services market where I mean, yeah, you have also jamie diamond from JP Morgan Chase have said this about eight or nine years ago and continues to talk about how Silicon Valley is disrupting the traditional financial services, a very open statement that he made, and it’s true. So but that doesn’t mean that they will take over right, but I think where it is serious enough, and interesting enough, where large financial institutions are, are taking a serious look at it, and also trying to change their own internal cultures towards innovation, either by, you know, changing the policies and procedures and how they operate. Because it is difficult to operate as a startup, any very large organization, right? I mean, it’s a very different paradigm. And I’m also seeing a large financial institutions also going out in acquiring startups to not only to acquire the product, but also to embedded the innovative culture that comes along with the startup isn’t so. So I don’t think one is better than the other. But I think in the end, the consumer wins, because now you have much more diversification of choices, and expressing and then from a consumer standpoint, or customer standpoint, you have much many more choices.

Alexander Ferguson 21:14
Let’s imagine someone’s watching here, right now, that works, decide whether it’s a larger enterprise or a smaller startup, and they’re wanting to be able to roll out a solution like prevenir, or being able to get better insight or analytics on the risk management options. Is there any, but is this going a different way? Is there any bad recommendations that you’ve seen people are making giving to these companies that you’re like, Oh, don’t listen to that, that’s not right, you should really be doing this.

Veejay Jadhaw 21:42
I mean, we, we get pulled into by our customers all the time for best practices, right? There’s a customer for example, that that we are that we are partnering with right now, they are very much interested in, in, in developing fraud monitoring capabilities. And so, we are not there to sort of audit anyone else’s work, but we are we are we are brought in as subject matter expertise and insight experts in the area of financial services, business processes related to credit risk decisioning buy now pay later payments and insurance and so on and so forth. So, we are we are asked to consult credit got a few times because of our diversity of of customers all around the world, right? We got customers on one end, there are large, multi billion dollar in asset valuation, financial, you know, large financial institutions, as well as you know, startups and pretty much all across the world. So we have a wide array of experiences in terms of how, what other customers are doing and what markets are doing. And we get consulted quite a lot in terms of how do we deploy a buy now pay later solutions much, you know, quicker. Same thing and fraud as same thing and, you know, any kind of credit offerings,

Alexander Ferguson 22:50
if you had to give a piece of advice on being able to roll a solution out faster way, as far as that process that you do, give me a nugget, just a very tactical nugget that you that you’ve been providing, maybe recently, in some of your conversations,

Veejay Jadhaw 23:06
I would say no good platforms are fantastic, they’re much more powerful. We are generally asked by our customers to implement our products at a much faster rate. And the reason why our customers come to us is because of speed to market. So I would say not shy away from, you know, not shy away from a no code development movement. I think it’s here to stay. And it’s actually growing at a much faster, much faster rate than anyone anticipated. So my advice would be to actually look at no code development platform like proven here, but not just just as a pure technical platform. Right. I will say that couple with domain expertise. So for example, our platform product is industry agnostic, right? I mean, I we can use a product across any industries. But we chose to be specialists in financial services. So our services organization is an expert in a product organization is an expert in financial services, because we have partnered with customers all around the world. So my advice would be not just look at the generic, no quarterbacking platform, but also select a partner who understands your industry very well. So you’re able to be relevant for the customer.

Alexander Ferguson 24:16
taking just a second to dive save a little bit deeper into the tech itself for those that do look technology. Can you share a bit about the the some of the machine learning that you use and how you where you’re getting your data and how you’ve worked on that itself? For those that are a bit more curious about the what’s behind the technology itself?

Veejay Jadhaw 24:37
What drives our differentiation? In our I would say our drivers around technology is around you know, parameters around innovation but customer centricity speed to market a relevant domain content underpinning machine learning. I mean, those are all the specific was a product later drivers that we that we look at as we are building our roadmaps and building our differentiating technology. You know, our core technology, however, is state of the art, your cloud native across multi cloud with Amazon Azure, Google. We are also tool agnostic. So that means that you know, we can leverage models anywhere from you know, Python or Spark ml to TensorFlow is very fully built on like Java stack, with the with the true microservices architecture, which is enabled with the consumption based infrastructure. So you know, you can scale up scale down on demand. And more importantly, you know, we can onboard a customer very, very quickly. So that’s a nutshell in terms of you know, what our high level tech stack looks like. underpinning all of this stuff is data. So we are not just a tech platform, but we actually have a global digital marketplace, which actually consolidates in in a single use easy to use API, any kind of data set relevant to the, to the credit markets or credit industry, right. So whether it’s credit bureau data, whether it’s alternative data, whether it’s name, and address verification, email verification, any data that’s required for decisioning related to lending or credit, is available on a global market means so so we so easy to use. So that really means a one stop shop for customers to when they’re building a startup or you know, have a new start, they want to build a new algorithm or new capability and by an operator or whatever, and when in financial services, they can actually go to our digital marketplace and look at, okay, these are all the separate different kind of data that’s needed and available on single click of a button and embedded in embed them in your business processes to go to market much faster, right. So if a customer’s actually, you know, agreed to regaining the data, right, then we, you know, we will retain customer data fully secured and fully encrypted, of course, and with all the, with the highest level of security available on infrastructure on Amazon, as well as Google and, and, and Azure. And that data is available. So combined with the the data that I talked about, that’s available in a marketplace with alternate data, as well as the transaction data that’s coming into a transaction processing for a specific customer. Because the you know, that that relevant customer can they use that data for, for analytics for reporting, as well as for retraining their models, right, and making the model as more close to reality on a on a near real time basis that that’s needed, right? We provide an infrastructure, and capability that stores all the data also, and then also enables, you know, model management capabilities model monitoring capabilities on top of a product as well. So, so it’s very comprehensive. And it’s not just generic, which is a risk service services, a horizontal market is very financial services. Right? So it’s very relevant to financial institutions to to look at a product because it’s, it’s very relevant for them.

Alexander Ferguson 27:55
Being able to see this, or imagine this in play, can you just kind of wrap this up in maybe you can give one of the use cases right now, where from end to end? I don’t know if you can name a company as well just help us walk us through, like how does it look in place, who’s the person that would be using this in there, and then how they would actually roll it out.

Veejay Jadhaw 28:21
So, I would say, you know, best example, is probably one of our customers born out of the MENA region has used a product for me has been using a product for many years now. And they were probably one of the innovators are the early adopters of buy now pay later capability. You know, they always say they provide the payments after delivery, assuming the credit and fraud risk so so that the sellers can be sold. So the sellers can be paid so and are they utilizing our product to enable the entire buy now pay later solution that they have, and they are a leader in the space of plan essentially selected, but when a decision in cloud to develop custom onboarding, Virgin onboarding, and the end the due diligence of applications. Customer onboarding was configured to process hundreds of transactions per second to support rapid business growth, again, without compromising the control and accuracy of the operation. So you know, Atlanta now is the leading provider of invoice based payments for retailers, you know, employing over 3500 people, and servicing servicing something around I think, 45 million customers in 18 countries. And we are the underlining product that enabled all that right from its inception.

Alexander Ferguson 29:33
What do you see on the on the roadmap kind of ahead for that is coming up that you’re excited about, since you love innovation, that you can they can speak about?

Veejay Jadhaw 29:44
I would say when I look at from a roadmap perspective, our focus is really more about how do we improve the lives of a customer and how do we we anticipate customer needs and market needs. I mean, that’s how we look Good things. So in that context, you know, innovation is a primary core competency of our organization. Right. So we allocate a large percentage of our revenue or capital expenses, or capital budget on innovation. I would say the area that we are heavily focused on right now. So we’ve been doing, we’ve been implementing, by now, like I said, by no period of fraud, you know, credit card auto financing for years, you know, for a decade plus, we’ll be doing that we know that market very well. And we have implemented tons of solutions in that area, what we focus on right now is how do we actually make those capabilities even more faster? Right, so that’s one area of our innovation. So providing an add to make it as turnkey as possible. So our system can be implemented in at any customers at a customer site anywhere from, you know, matter of a week to maybe 90 days or less, right. And that’s I’m talking about large global enterprises, right, which is via saying, okay, that’s, that’s great, but we want to be, we want to be even much more faster. So how do we have an offering for let’s say, fraud orchestration, or by an abelian, or where to be a near turnkey solution where the customer can go on, you know, online, and essentially, you know, onboard them with buy now pay later in near real time basis. So making it near turnkey as possible. So we’re doing a lot of work in that area. So so we already have the unlearning platform. So it’s not really about the platform and technology, this is really more about the content. So that’s one area of innovation, where we are improving our further enhancing our abilities to make our solutions more turnkey and making it even more faster than one week implementation. And the other the other area, when we look at a roadmap is really, it’s more about how do we get focused on further expanding our capabilities in the data area and data science area? So we expanding our data science talent pool quite aggressively. We are, you know, we also want to further improve the data scientists lifecycle in terms of their work processes, and how do we, how do we make it easier for data science scientists to do their jobs on a day to day basis. And on top of that, you know, providing algorithms or on models for specific verticals or for specific industries, and package them as an API and our global market. But our focus right now is really more about data science, enabling data scientists providing much more much more diverse, we already have a lot of models, but much more diverse set of machine learning models, to service multiple segments. And as well as further enriching our product multiple content, right? Like, you know, we invest we are we invested heavily two years ago in a global marketplace. Now we have a significant amount of data vendors on a global marketplace. Now we we want to continue on that journey. And as well as build more content on our product to enable much faster speed to market with specific packages related to fraud, and by an OBE later and and things along those lines. How can we be ahead of the market, we definitely don’t want to be a me to provider. I mean, we just not in that, you know, I think 10 years ago, something we already disrupted the market, in our opinion. And we want to continue to do a disruption in this area within financial services, leveraging AI machine learning and data to further serve a customer much better.

Alexander Ferguson 33:16
As a technology leader, just taking a moment here, looking at the banking FinTech sector as a whole, so not just specifically what you’re doing as a whole, what technologies do you see on the horizon that will hold great promise to make some change, or improve the FinTech and banking sector as

Veejay Jadhaw 33:37
a whole? I think there’s quite a few. Right, but I would say the two that I’m really excited about. And, and we are, you know, also investing in those things. And number one is really all about automation, artificial intelligence, machine learning and data. So that whole paradigm, how do we continue to invest? I think that’s good. There’s a whole lot of promises, and the access to data scientists, even though the talent pool is quite scarce in this area, but because of availability of data, and then a lot of investment made of the academia level to, to sort of educate people in data science, I think. So there are a lot of data scientists coming out. And a lot of people are transitioning from, from x from the current sort of skill sets to data science, and therefore, leveraging the data. And I think that whole area is going to further enhance. But it all starts with the talent, right, but I’m seeing expansion of the talent pool related to data scientists as well over the next three to four or five years. And I think that’s just going to enable companies to deploy machine learning at a much faster rate and much, much more advanced capability. So I think that’s one area where I see financial institutions or any industry leveraging data and machine learning and artificial intelligence to improve their decisioning improve the business processes, improve the predictions, and be and being much more accurate, and that this is a very complicated, but it’s also maturing field. I think that’s one area which is very exciting. In the another area, in, in my opinion is really going to be all about interfaces. So by interfaces, I mean like, you know, voice activated interfaces, video, video interfaces, augmented reality, these are all areas where traditional keyboards are, you know, at some point in the future probably will will, you know, scale down and we’ll see other advanced more and more advanced interfaces, how we interact with computers, this is an area of a lot of research that’s happening, you’re already seeing the basics of that with chatbots and things like that and I see a huge influx of innovation in that area as well where you can sort of interface with people in augmented reality fashion as well as avoid voice activation and and video based interaction with the theaters and therefore experience the at all effects than the end user experience really making the interaction of the customer with the financial institution much part of their how they interact with day to day on a day to day basis with their friends, right so it becomes much more seamless in terms of how you integrate the physical world with the digital world. So the whole area of interfaces is something which holds a lot of promises in terms of how it’s going to enhance our daily lives outside.

Alexander Ferguson 36:17
Thank you for sharing your your view on the future I agree with you on the interfaces that’ll be fascinating to see where that goes next. But also the the insight of where proven error has been from and the challenges that kind of have been faced in this area. But you guys are focused on solving it bringing data and the analytics and that the the machine learning to bring new insights I really appreciate your time today for those that want to learn more you can go over to Provenir.com. That’s PROVENIR.com prove in the year prevenir. Thank you Veejay again for the insights that you shared.

Veejay Jadhaw 36:52
Thank you so much for the time designer. It was a pleasure. Thank you.

Alexander Ferguson 36:55
And we’ll see you all on the next episode of UpTech Report. Have you seen a company using AI machine learning or other technology to transform the way we live work and do business? Go to UpTech report.com and let us know

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