Leadership Insights: Documentation Automation

Transcript

Amanda Razani: Hello, I’m Amanda Razani with Digital CxO. And I’m here today with Aashish Mehta. He is the CEO of nRoad. How are you doing?

Aashish Mehta: I’m well, thank you, Amanda. Thanks for actually having me.

Amanda Razani: Glad to have you on the show. So can you share a little bit about nRoad and what services you provide?

Aashish Mehta: Sure, so nRoad is actually very focused on, hyper focused I call it, on unstructured content processing. Let me step back a little bit; let me kind of explain to you what is involved in this whole unstructured business and social content business. Unstructured content is one of the largest content sets that is actually getting created as we speak. Large enterprises are grappling with the onslaught of unstructured data, which could include documents, texts, voicemails, videos; the scope of the unstructured content can be very, very, very fairly large. IDC Seagate, predicts that the global data sphere will grow to about 163 zigabytes. I can’t even count that, by 2025. And 80% of that is predicted to be totally unstructured, as I just mentioned, the definition of that is. In regulated industries, such as financial services, this challenge is going to be even higher because regulations are imposing some data to be some unstructured content to be created, or semi structured content to be created. And that is where nRoad actually is, that is what nRoad does. nRoad has actually focused itself on automatically processing the unstructured content and generating relevant insights from it. And that’s what we do.

Amanda Razani: Okay, so, what are, from your perspective, what are some of the biggest challenges that are facing real estate investors today? And how can automation help, especially in the area of funds in particular? And how can they grow?

Aashish Mehta: Great question, right? So the real estate investment community in general, by definition, by virtue of being regulated, has lots of documents flowing through the system, and what it does – the documents or the unstructured mess of it, it slows down decision making at the front office level – not only that – it also slows down the middle office and the back office. So one of the clients that we are working with, we’re currently working with right now; we have them in production – they are a small fund, we’re talking about maybe a couple million dollars worth of real estate funds – the back office of that particular fund, just couldn’t scale, so that they can actually attract more funds to be invested. Unfortunately, they were tasked to process close to 20,000 documents a year manually. And so that is some of the – again, I’m answering the question from a very myopic focus on what we do, and how we actually have been able to help that. So in that particular scenario, we were able to actually come in, automate that process, take that bottle out, instead of having eight, nine resources working on it on a monthly basis; now we have one person working on it using automation. So we have achieved two things. We relieve folks from doing mundane tasks, we increase the scale and basically time to market. So those are the challenges I think some of these firms will have, and they continue to have it.

Amanda Razani: And so it comes down to efficiency. So I asked the question about the real estate investment market, because we had talked earlier about that. But how does this relate to other businesses across many industries? How would this help?

Aashish Mehta: It is, and by the way, great question, this problem is actually across industries. So one of our other financial services clients – they have 50 million documents, pieces of documents coming in to them every year. They are not able to do anything with it. They’re just taking the documents categorizing, you know, and just stacking it somewhere. They know that there’s a lot of information in those documents that needs to be read through or analyzed or utilized for something else, but not been able to process them. So in our view, this challenge, this is not a realistic focus. This is actually across industries. And for the industries that are heavily regulated, this problem will be actually even higher.

Amanda Razani: Yeah. So you declared a war on documents, recently deploying a machine learning model for a Fortune 100 fund. And you saw some really measurable outcomes from that. Can you share those outcomes?

Aashish Mehta: Yeah, firstly, first, let me actually address this hashtag “war on documents.” I know it’s pretty violent, but we really mean it. We are going after these documents. And we are maniacally focused on getting those documents processed, automatically processed, now, so pardon if this is actually too violent for people, but this is really what we need. In regards to the recent model that we released, that has actually – let me give you the before and after. Before, this company, that Fortune 100 company that we’re talking about, was using about 175 people processing about 40 or 30,000 documents a year. Every analyst was taking about eight, nine hours per document, in terms of reading, extracting, manually extracting data, and then generating some variables from it. They were doing it using, as I said completely manually, eight, nine hours per document per analysts. Here comes nRoad. nRoad takes that and reduces that time to about under three hours per document. And by doing so, we’re able to actually cut the people’s time by over 60%. Now, none of these people are displaced, just to be very clear, okay? What they are doing is actually, now they are relieved from this mundane task of typing, copying and pasting. Now they’re adding value at work. Now, instead of processing 40,000 documents or 35,000 documents, now they’ve been tasked to process even more documents. So technology – that’s the benefit we are creating here. It’s not just, you know, cut jobs, none of that; it is about how do we scale? How do we scale effectively? And how do we scale while retaining the institutional knowledge within these enterprises versus outsourcing it and getting, you know, getting the jobs done by someone in China or India? That’s not the answer anyways. So that is the value; we’ve been able to create some of the, you know – using some of our models.

Amanda Razani: Awesome. So looking down the road, because of the bottleneck that occurs – and you said this freed up so much time and space to get through these documents – do you think as automation develops even further, we may not even see manual processing of documents down the road?

Aashish Mehta: I would hope so. That is our goal. Our goal is to actually automate some of the documents so that document processing, you know, we don’t have to read these documents; just read the read the variables from it. Now, what is more relevant? Then again, you know, we are not there yet. We’re not there, and we will not be there for a little while. But at least that’s the goal we’re working towards. We are trying to create a frictionless business process, automated business processes that are fully automated. If you look at any business process today in enterprises, they break whenever there is unstructured content, there’s an encounter with unstructured content; that document comes into the picture, everything stops, somebody has to open it, click it, read it, make sense out of it, and then put the relevant variables from it into the system, and then the process actually executes. Our goal is to make that as frictionless as possible. And now certain documents are a good candidate for automation – certain documents are not. So again, we’re very, very focused on areas where we can add value, and then we’ll take it as it comes.

Amanda Razani: Fantastic. Well, I want to thank you for coming on the show and sharing your insights about this. And hopefully, more and more businesses will continue to automate this process and free up time.

Aashish Mehta: Great, Amanda. Thank you so much for having me on the show and sharing my thoughts with your listeners. Appreciate it.

Amanda Razani: Thank you