BTOES Insights Official
September 05, 2022

Nividous Webinar - WEBINAR SPOTLIGHT: Intelligent Document Processing.

Courtesy of Nividous 'Alan Hester', below is a transcript of the webinar session on 'Intelligent Document Processing (IDP) - What is it? What are the benefits? What is the correct implementation approach' to Build a Thriving Enterprise.



Session Information:

Intelligent Document Processing (IDP) - What is it? What are the benefits? What is the correct implementation approach

This is an introduction to the new era of enterprise data processing, which, as anyone who struggles with back-office efficiency can tell you, begins with documents. Documents still exist in large volumes, varying formats, and quality in almost every business. Processing documents that are in semi-structured or unstructured formats can often be fragmented, error-prone, and require frequent human interventions.

Intelligent Document Processing (IDP) solves these problems by combining the power of Artificial Intelligence (AI) technologies—such as Computer Vision (CV), Natural Language Processing (NLP), and Machine Learning (ML)—to efficiently capture data from documents and categorize and extract relevant information for further processing.

IDP delivers exceptional benefits allowing businesses to compete at scale and that explains why the IDP market is expected to grow at a compound annual growth rate of nearly 37% through 2026, reaching a global value of $3.7 billion that year.

In this session, gather insights on:

  • Understanding intelligent document processing and its benefits.
  • How to leverage IDP to address today’s document processing challenges.
  • Real-world IDP use cases across different verticals and business functions.

Session Transcript:

Welcome everyone, to today's webinar, focusing on intelligent document processing. I'm Kayla and our speaker today is Alan. If you find any difficulty with the audio or video, please just use the chat window to post your queries.

You can also use the question window, which is a separate window to share your questions related to the webinar.

Please raise your questions whenever you feel like we'll spend some time at the end of the session addressing them, and if we don't get the chance, we'll definitely get back to you via e-mail.

Additionally, there are also handouts available for your download and evidence from the very beginning, our aims to help businesses work at their peak efficiency with our intelligent automation platform.

one of the key differentiators of the platform is that RPA, AI and BPM capabilities are natively developed within the platform. Is key differentiator, has enabled us to help our customers meet their end to end automation needs. Are also being recognized for our work by leading industry analysts globally, namely Gartner or Forrester in the Everest group. I'm now going to hand it over to our speaker. Alan.

Thanks, Kayla.

And thank you everyone for joining us today.

Our goal today is to walk through Intelligent Document processing.

And, I want to get started with just a quick look at digital transformation, in general.

And some of the challenges that we see with digital transformation are related to unstructured data.

And the reason why we want to focus on this, initially, is, this kinda leads us into the intelligent document processing discussion.

So, many of you probably already realize this. So, I'm not telling you anything that you don't know. But, it's a huge challenge.

Dealing with all the unstructured data, that is part of your different work processes.

There's a huge amount of data already, and that amount is growing each year.

And dealing with this data causes a number of problems for your company, in terms of the amount of time that people have to spend on it.

Btog CTAAs well as the multiple systems that you might use to interpret that data, and get it into other systems, In ways that you can really use it.

And it all results in a huge cost and time sink for most companies.

So let me stop right there, and let's just do a quick poll.

And this is going to ask you the question about whether or not you're currently using any form of intelligent automation in your business.

So, by intelligent automation, we're talking about any, any form of automation that you're using, RPA, or other or complimentary technologies, currently.

And then, that'll give us an idea of where the audience stands in terms of implementing and knowledge of these kinds of solutions. And then later, we're going to come back, and I'll give you another poll to ask, specifically about intelligent document processing.

So, intelligent automation, or automation, in general, and intelligent document processing, are two very different things.

And that's something that we're going to spend a good amount of time on in this session, covering what those differences are, and why it's important to you to know those differences.

So, we have a good amount of responses coming in, and it looks like 59% of you are saying that you are currently using intelligent automation tools, so that's great, so everyone has a good amount of knowledge and history in this space already.

So, maybe some of this will be review for you.

So, just kind of ride with us, as we make sure that everyone gets up to speed, so that when we start this conversation about intelligent document processing, we can make sure that we're all on the same page.

So, let's look at, within different sectors, You can see here that we have a bubble chart that shows by industry, which are the functions where handling different kinds of unstructured data is commonly prevalent.

And then you can see by the underlined areas, those, those spots where it is most common within each industry.

So, for example, if we look at the biggest bubble there, the banking and financial services, you can see that the most common areas are, know your customer, and insurance claims, and then within healthcare, it's patient on boarding. within finance. It's invoice processing, So probably many of these will resonate with you if your industry is shown there.

But if your industry is not shown, don't think that that means that this is not relevant to you. Because I'm sure that you are still dealing with lots of different kinds of documents leading to all kinds of different data. Much of it unstructured, but then also some that structured, or what we call semi structured.

And we'll talk more about that as we go along here.

OK, so, when we start looking at intelligent document processing, I want to be clear what we're talking about, And this is a good point for me to put up the second poll.

Screenshot - 2022-01-14T203043.632And just take a minute for you to answer.

If you're currently using intelligent document processing, where where is it that you are using that today?

And I know it says on there. Well, it says if you're running or planning to use.

So if you don't really know, I would say just check the others or check whatever is the one that seems most relevant to you right now.

As we go through this session, hopefully it will become clearer to you, what are those areas where it would make most sense and have most impact within your organization.

So, we have a good amount of answers coming in here, and it seems like others is the leader, and followed closely by customer facing operations or processes.

So, I'll give it just maybe 1 or 2 more seconds here.

And customer facing operations are processes, is gaining there.

They're pretty even now between that and others and then followed by finance and then supply chain, and a little bit of a chart.

So let's close that off here.

And what I want to do now is look at some of these differences between what, what IDP really is and what it isn't.

In many cases, I know there's a lot of misinformation out there. Right?

It's maybe not so much misinformation as different people have different things that they mean when they talk about intelligent document processing. So I'm going to tell you, from the ...

standpoint, what we mean when we're talking about intelligent document processing, and contrast that to maybe some of the other things that you have heard as potentially being intelligent document processing as well.

So, in our case, we have a single platform that allows you to extract structured data from either unstructured or semi structured data or documents, pages, however you want to look at it.

And, it uses a combination of artificial intelligence technologies to do this.

So, it uses things like computer vision, natural language processing, machine learning, deep learning, also has the ability to create your own custom models, As well as use some standard models within standard use cases. And also allows integration to other systems as needed.

So, this is kind of our version of what intelligent document processing is.

And the contrast that I'm trying to draw here, is that it's not just a standalone RPA solution, it's not a standalone OCR solution, And it's also not a combination of lots of different tools and technologies together that need a bunch of integration.

So, with many of the solution providers out there that you might talk to in this space, you will find that, in order to really accomplish your end goals, they're going to require you to use lots of different tools and technologies. Maybe. Maybe all provided by them.

Email Graphic Virtual Conferences-1But really separate products. Or maybe it's a combination of things provided by them. As well as some others.

And in our view, in order for a company to be really effective implementing intelligent document processing, it's certainly easier, faster, and less expensive if you can do everything that you need using a single platform.

So looking specifically, then, at the contrast between RPA and intelligent document processing, you probably already know that RPA is all about using software bots.

two, automate basic routine, repetitive tasks.

And RPA is really great at doing that, and it doesn't really care what the underlying applications are that it's working on.

As long as there is a user interface, the bot is able to simulate the same kind of actions that a human user would do.

The other side of that, though, is that the bot needs set rules to follow in order for it to be effective.

So we use the word deterministic here, which basically means that it's rules based.

So as long as you can outline what the rules are for the bot to follow, then RPA can be a good solution for that.

So RPA can do things like screen scraping and it can also use a bunch of pre-built automations for doing some things that are parts of intelligent document processing.

But the differentiator is that intelligent document processing is using these advanced artificial intelligence technologies that we've talked about before. Now, within the ... Platform, these are all integrated, and these are all built by ....

But, as I said before, with other providers, you might see that similar kind of functionality being provided through external libraries, or external products.

And so, it's a combination of, uh, lots of technologies that allow you to use, uh, this advance to artificial intelligence, to gain more or better understanding of those unstructured documents, to make it easier to process.

And then the other part of that is, it can provide the ability to deal with more complex tasks, because of that artificial intelligence functionality as well.

So whereas on the RPA side, the things that you're doing have to be highly rules based.

You'll have a little more flexibility, once you bring the artificial intelligence into the mix, because that allows you to, to use those AI technologies to help make those judgements.

So you can use those same kinds of model based learning that you're using to pull data out of the unstructured documents. You can also use that for decision making within the process itself.

The other big differentiator here is that, within intelligent document processing, the models can learn and be trained over time to do better.

And all of that can happen without any additional programming work being done, because it's really just a feedback loop for giving the models more data, so that they have the benefit of things learned for future cases. And we will see some of that when we look at the demo.

I'm going to give you a quick demo of this that will hopefully bring this all more into focus for you, because I know a lot of this seems a little bit arbitrary and kind of like magic when we talk about some of this AI function of functionality.

But but you will see that really it amounts to taking mounds of data and using that as a way for being able to determine what should happen next or what is coming next.

So, you know, that's another form of this artificial intelligence functionality that you have access to. Which is being able to do predictive analytics analytics as well.

So, when we look at these technologies in more detail, and look at what are the end to end process steps that it takes to perform intelligent document processing.

You'll see that we have this broken down into six steps, but you could probably break it down feiner, where you could probably come up a level, as well.

But it involves classification, or coming up with what?

What is the document type that you're dealing with?

And, again, you will see some of this in the demo because we're going to go through classification and extraction, in our demo.

And the reason why this is very important is that there's a huge amount of manual effort that is typically spent on just understanding what is the data that you're dealing with?

So, we have talked to a lot of customers, and worked with a lot of customers where they will get a bundle of documents.

Maybe it's a single PDF file that contains 100 pages. That could be 100 different documents, Or maybe it's 100 different PDF files.

But, in either case, typically, someone is going to have to go through all of those and do this classification process, which, basically, if you think of it in, in manual terms, imagine that someone comes to your desk, and they put a stack of 100 pieces of paper on your desk. And they say, Hey, you know, this is, this.

is various things that we need to act on.

Can you just sort them out, so we know what to do.

So you go through those pages 1 by 1, and then N one in one pile, you would put, OK, here all the bills that I have to pay.

Here, here are all the invoices that are outstanding with our customers.

Screenshot (4)Here is all the Customer Feedback Surveys and the data associated with that, and what are What are all those different documents that you deal with in your business, and in your different business processes, you would sort through all of them, and put them into those relevant piles.

And that's what we're talking about when we say classification is going through that process of figuring out, know, how how those documents need to be categorized, and then once you do that, then, for each of those document types, you can have a specific model built for being able to extract the data from them.

And the reason why it's important to do the classification before the extraction is that, when you know what kind of document it is that you're dealing with, you can use a more specific model to extract the data.

And so then, the extraction is going to be better.

So you will get better data out when you follow this approach, as compared to trying to do the extraction first, and then figure out what kinds of documents it went into.

And then obviously the next step is being able to validate the data that has been extracted, and you can have a number of different rules that that can check things in various ways.

Maybe based on the type of data that you expect to see in that given field, or maybe by cross-checking data against itself.

Maybe from page to page, there, there are a lot of different mechanisms that you can use for validation.

And then you can also do things like enrichment.

So, once you have some key piece of data, maybe someone's at count number, or their patient number, or whatever that might be, you can use that as the key to pull in data from other sources as needed to make the process better, further downstream.

Then, obviously, you have the ability to verify by human interaction when that's necessary, And we'll show you how that works in the demo itself, and then also, the ability to integrate with various systems on both ends.

So, the data can come in from various systems.

And then, once you have that unstructured data turned into structured data, then you can send that to various places and various systems as well. And you can also do lots of other interesting things, like being able to report on that data in various ways, being able to provide dashboards and notifications, if you see discrepancies or if you want to set filters or flags. There are a lot of different things that you can do once. You have the data in some good, structured form that you can't do with that data when it's unstructured.

OK, so we've talked about some of this already, but just to give you an example of some of the big things that you get out of intelligent document processing, you can eliminate a lot of those manual and time consuming processes.

Get to the point where you're doing straight through processing, and doing it quickly at a reduced cost. And all of that can lead you to better customer experience. Better employee satisfaction as well.

And just operationally reducing your business processes down to a way that has them truly optimized.

And all of this gives you just a better return from your investment in the human resources that you have within your company.

So allowing people to focus on higher value tasks, rather than wasting a lot of time in these manual time consuming processes that are associated with the classification and extraction of unstructured data.

So we've talked a little bit already about the Knative.

AI technologies that are built into the ... platform. And I'm not going to go into any great detail on these.

So, if you are an artificial intelligence expert, you might be expecting to get more substance out of this, And we would be happy to go into more detail with you, in a separate session, but we're going to keep it pretty high level here.

So, just looking at computer vision, within the ...

platform, our OCR is always powered by computer vision, which allows you to get much better results from the OCR, extraction, than what you can get using OCR on its own.

And so, let me explain a little bit about why that is. So typically, if you take a page, and you scan that in, and you run that through an OCR engine, that's going to give you a dump of all the text that's on the page, and, and maybe, if it's a really good OCR engine, it will keep some of the spacing and formatting, and things like that.

But it's not going to associate any of the name value pairs that are on the page, or do any kind of checking or anything like that.

To make sure that, that, if you take that data, and you paste it into any other system further downstream, that you will get the kind of results that you expect. So you can build all of that on top of it. If you're building some kind of workflow that's using OCR as at its base. But what we take a little bit different approach where we use computer vision upfront.

Screenshot - 2022-01-14T203043.632So that you're doing the extraction based on regions of interest, on the page that are determined on the fly.

So, it's not like you say, Hey, on this page, I want you to look on the top, top left-hand corner and usually, you will find a customer customer number up there. That's not how our engine works. So, in the ...

platform, you will define a template and it will say, Hey, we're looking for something on all of these pages called customer number and sometimes it will be customer number that it's all spread, all spelled out, Customer number, straight text. Or sometimes it will say customer numb. Or maybe it will say customer NO, or maybe it will be customer hash with some other kind of abbreviation, and you can build all of that into the template.

And so then what our system does is using computer vision.

It will find areas of that page as it scans it, to determine, OK, here, is that spot, where I think you might find customer number, and then it will extract that associated data as the customer number.

Which gives you a much better extraction than if you're doing it the complete page at a time. So all of the data that you're extracting from that page, it will take this same kind of approach, so that you're going to get a much higher quality extraction of the data.

But also, it makes it so that your process is much more resilient.

So minor changes in the document that's coming in aren't going to break anything.

Because you've already accounted for that, and how you have built that template.

And so we will see some more of that as we look at the example, too.

But this gives you a great advantage, as compared to just using OCR on its own.

So, one of the other interesting things to point out here Is that the Nimbus Platform will also run on device directly on mobile devices.

And the reason why this can be important is you might have some new use cases where you have people out in the field where they're gathering information.

one of the cases where we have seen this used extensively is for, um, for power technicians who are out there, reading instruments in the field, or reading meters in the field.

And they have to maybe take, take a picture on the mobile device of a meter reading. And that is used in some systems, for either billing, or, or for measuring efficiencies, or whatever, whatever that data might be useful for.

But the problem lies in the fact that many systems that will give you that kind of functionality rely on some back end processing in order for the data extraction to take place. So if you're in a remote area, or if you're in some kind of environment, where the connectivity is, is not good or is weak, then that's not going to work effectively. And you're not going to get out of the process what you want.

So in our case, since all of the extraction is operating on the device itself directly, you can, you can take that picture and do the data extraction.

And the device will store the extracted data.

And then can upload it to the server, or whatever backend systems are necessary whenever it has connectivity restored.

So it leads to a much more seamless end to end process than what you would see if you're relying on a bunch of back end processing in order to do the extraction.

So, that's all computer vision, then we'll talk quickly about natural language processing, and predictive analytics.

This is the technology that gives you things like text classification, entity extraction, predictive analytics, as we talked about. And then, also, things like sentiment analysis.

This is commonly used for call center type operations, where you will use the text of someone's e-mail, or even that the text of phone calls to analyze what a customer is feeling based on what they're saying. So, so are they angry? Is this a case that should be escalated to a higher level? Are they happy?

Is this a case where, you know, maybe we should get some kind of, uh, customer cert satisfaction survey in their hands, or or something like that?

Or, you know, there are lots of different things you can do with this, depending on the business process, that you're using, to take advantage of that sentiment analysis that could be done, based on the data coming in.

So, let me quickly just give you a, at a high level, the different components of the ... platform.

I've already talked about the fact that this is a single, integrated platform that will run within your enterprise.

Email Graphic Virtual Conferences-1So this is something that you don't have to worry about, there being any security issues because data is, is leaving your environment, and going someplace else to being to be processed. Everything will run within your data center or within your private cloud instance. We do have a studio, which is the development environment. If you want to do this kind of work in house, you can buy copies of the studio.

You can have people trained, or maybe you have your own vendors. Or we also have partners that you can work with on implementing this if you don't really have the right expertise in house and you don't want to build it.

But the great thing about this is that it doesn't require a bunch of programming knowledge.

So it's not like you have to hire people that are already really experienced programmers that would be high cost and you don't have to worry about re-allocating your expensive IT resources that you already have in house.

You could train people that just have the right combination of knowledge of the business process, as well as some good technical acumen, and they can do this kind of work for you with no problems. So the studio is the single development environment that you use for building all of these things within the vividness platform.

The control center is the place where all of the monitoring and execution, it's done. So this is a portal interface that lives on a server.

There's only a single control center instance within your enterprise, Typically, this is where all your dashboards and reporting will live. This is also where you can see a full audit trail.

So, one of the nice things about implementing intelligent document processing, using our platform is that you'll automatically will get a full audit trail of everything that's happening across the process. Who's doing what. How long that takes, why that was done.

As well as detail about the data, as it flows from one step to another.

The Control Center also gives the ability for humans to interact with the process.

And we talked about how, how humans can be used for validation of the data and correction of the data, as well as that feedback loop.

And we will show you some of that in the demo in just a minute when we get to that.

Then the other piece are the bots themselves.

So the bots will live out there on end user class machines, and even though we talked about what are the differentiators between intelligent document processing and RPA?

The two can work in concert as it is being done here on the ... platform to achieve great results. The final piece is what we call the Smart Bot Library.

And this is where our, all of our artificial intelligence functionality lives.

So, all of those capabilities that we talked about in terms of the natural language processing, machine learning, predictive analytics, sentiment analysis, all of that is enabled by this Smart Bot Library.

And that, again, is, is living on a server within your infrastructure, which you only need one of. So, once the smart about library is in place, all of your processes have the ability to leverage that.

So, it's a one time investment for you.

All right. Let me circle back for a second and just kind of cover quickly a few other things about Vividness, and then we'll move right into the demo.

So, many of you may be familiar with ..., or maybe this is the first time you've ever heard our name.

And so I just wanted to give you some confidence that, even though you haven't heard of us, we have been working with the top analysts out there that have been talking to us, looking at our products, and also talking to our customers about the work that's being done.

So we have gotten very good feedback on our RPA product as a standalone, as well as within the intelligent document processing space. And we're one of the few vendors that has great coverage in both of these areas.

Because we have a single integrated platform, it makes it very easy for us to come up with good end to end solutions.

And one of the things that we do to kind of leverage that is that we use these Solution Frameworks as a way too, accelerate your progress in particular areas where we see it over and over again.

So, things like accounts payable, automation, or invoice processing, service desk automation, or other can other call center type applications like what we were talking about before.

New customer onboarding or onboarding in general.

In some cases, it might be new partner, onboarding or new vendor onboarding. The processes are very similar.

In the insurance world, a lot of different reconciliations happen the heavy use of spreadsheets.

And so this is great area where we have done a lot of work.

So have some good solutions already in place. Revenue cycle management within healthcare.

And then banks, they were key reconciliation within financial services.

If you have any questions on that in more detail, I'd be happy to go into more detail on those solutions if you have specific questions, let me just quickly walk through a couple of case studies here.

This is a customer that was doing.

Customer onboarding, using artificial intelligence, machine learning.

This is an insurance company, specifically using artificial intelligence. And this, previously, it was a very manual process, that required data entry.

For hundreds of records each day, we were able to not only automate things, but also improve the accuracy tremendously, and reduce the amount of manual work that is required in order to achieve what was then more accurate results.

We also had a case within insurance where there was a manual process of being examining new business proposals, and this was a huge task that required a large team that was focused on doing this work, working through 15 to 45,000 cases per month.

And, uh, the amount of manual work that was done resulted in a lot of delays in the processing.

And a lot of difficulty in being able to track where things were in the process and how long it was going to take.

And it resulted in the potential of not meeting the SLAs for turnaround time.

So being able to implement it, automated solution, saved a huge amount of manual effort, reduce the turnaround time.

And also, again, reduce the amount of human errors.

And then the last one we'll look at here is a case for loan processing.

And in this case, there's a legacy system in place that had no APIs that made it difficult to integrate with other kinds of systems.

And so this is a great example of where combining intelligent document processing with RPA can really be handy, because you can use RPA as a pseudo API for being able to extract data from legacy systems.

Screenshot (4)And so that's what we did in this case, And the result was great reduction in the amount of manual effort and a reduction in the turnaround time and faster resolution to customer plates.

So, let me stop there, and let's just launch into the demo, and then we can come back and we can talk about some more case studies after this.

But I want to make sure that we have time to adequately cover, cover the demo, because I think it's really it's really interesting, um, see this walkthrough in front of you, how the process works in terms of the classification and they extract enough data.

So, what we're going to see here is an example of a document, similar to what I described, where we have a PDF file with lots of different pages.

And you don't know going into it what the different pages are.

But you have some pre-built models that say, Hey, here are all the kinds of documents that we deal with.

So we're going to tell the system to take this particular PDF file, and look at it, page by page, and come up with the classification of what you think each page is. So you could see that we've gone through that process.

And it's broken down for you by page number, how those documents have been classified, and how many pages are associated with each document type.

So you can see in this interface here, it will tell you, for the first document, it's page one and page two, and this is a 10 98.

So this is, um, this is a mortgage use case.

So that's what that 10 98 is a document related to that.

And you can see within each of these, you have the ability to dive down into the details, so that you can also look at the data that has been extracted.

You'll also have the ability to change the classification itself.

So if the system has looked at this document, and misclassified it, you can change the classification to what it should be.

And you can also change the data extraction if there are some errors with that as well.

So you can see here, there's a lot of different document types that take different action based on the document type itself.

So in this case, for that disclosure form, it really just needed to know that's what it was and then no data is extracted.

But for the credit score, There are lots of different fields that get extracted because that has already been defined up front, when you set up that templates. So, as I said, you have the ability to change or correct any of this data on the fly.

But then you also have the ability to go through that feedback process that we've talked about before.

So if you find some data that is incorrect, or if you find classifications that are incorrect, you don't just correct it.

For this one case, you can make that correction, and then you can feed that back into the model so that, in the future, those kinds of mistakes won't happen again.

Screenshot - 2022-01-14T203043.632So, you can see those buttons there that say: Classification, model, update, or extraction model update. Those those will take the changes that you make, and they will improve the models for the future.

And so, when you make a correction, like what, where, what, we will see, an example of in a minute where you redefine for the system, you say, Hey, you took this information, but you took it from this place. And I really want you to take it from another place.

So, in this case, we're looking at company name.

And it was correct, but we're just showing you the process here that you redefine that region, and then you can update the classification model and the extraction model, so that, in the future, the chances of the system being confused in a case like this are Glick are greatly reduced.

So, you have the ability, not just to do this and make the corrections so that you don't have the errors introduced, downstream this time, but you'll also have the ability to reduce the errors in the future. Sure.

So that you don't ever. again.

Let's go back two the presentation here.

And we've already talked a little bit about mortgage, so I don't want to spend any more time on this.

And also, we have A Yeah.

We have a little bit of a time limit here.

So let me talk.

Actually, let me just skip over these these case studies and let's take a second and see if we have any questions coming in.

Kayla, do we have any questions that have come in from the audience yet that we can address?

Couple of minutes that we have left.

Yes, so the first question we got, was that, if I have a file room with our old documents, who scans them, gayla, Are you there?

Can you hear me?

Can you hear me now?


Oh, yeah.


I guess what we will do do that.

Sorry, Kayla.

Here, I'm going.

OK, our first question was, what if I have a file room with our own documents, whose scans them?


OK, apparently, somewhere along the line, we're having some some difficulties here.

So, I apologize that we're, again, we're not gonna get to your questions, but I assure you, we will answer all your questions via e-mail.

And we appreciate you joining us today.

And if you have any questions that you didn't post, feel free to reach out to us after the fact.

And we hope that you found this session to be informative.


About the Author

Speaker Image Size (6)-1Alan Hester,

Alan is a Proven leader and entrepreneur who has a wide range of business experience in large and small companies. He has over 30 years of experience applying technology to solve real-world business problems.

Alan comes to Nividous from Freedom Mortgage where he ended his tenure as the Senior Vice President, Office of CIO. While there, he built a global technology team that delivered award-wining results that helped to grow business revenue dramatically while reducing staffing requirements through automation and process optimization.

A focus on process-centric thinking to drive innovation and strong sponsorship from the executive team allowed for these impressive results to be delivered in under four years. The hybrid team concept which combined onshore and offshore resources and was largely responsible for the success at Freedom Mortgage is now serving as the model for growth and expansion of Nividous in the United States.


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