>> live from Washington D. C. It's the Cube covering a ws public sector summit by Amazon Web services. >> Hello, everyone. Welcome back to the cubes Live coverage of a ws Public Sector summit here in our nation's capital. I'm your host, Rebecca Knight. We're joined by Raghu Rahman. He is the director of Fin Row, the Financial Industry Regulatory Authority. Thank you so much for coming on the Cube >> fighter back. Good afternoon, but happy to be here. >> So we're angry. This is the 10th annual public sector. Somebody should have said so Tell us a little bit about Finn Ra and what you do. They're >> sure Fender itself is the financial industry Regulatory authority way our private sector, not for profit institutions. Our mission is investor protection on market integrity. Way our member funded on DH. We have a member driven board board of directors and we engage in ensuring that all the stock market operations in the U. S. Capital markets play with rules. So that's the essence of who we are. >> And all of those stakeholders have a vested interest in making sure their rivals are also playing bythe. So you're here giving a presentation on fraud detection, using machine learning and artificial intelligence. That's right. What was So what were you saying? >> So, Brenda, we have a very deliberate technology strategy on We constantly keep pace with technology in order to affect our business in the best possible way, way. Always are looking for a means to get more efficient and more effective and use our funding for the best possible business value so to that, and wear completely in the cloud for a lot off our market regulation operations. All the applications are in the clouds. We, in fact, we were one of the early adopters of the cloud. From that perspective, all of our big data operations were fully operational in the cloud by 2016 itself. That was itself a two year project that we started in 40 14 then from 2016 were being working with machine language on recently. Over the past six months or so, we've been working with neural networks. So this was an opportunity for us to share what? Where we have bean, where we're coming from, where we're going with the intent that whatever we do by way of principles can be adopted by any other enterprise. We're looking to share our journey on to encourage others to adopt technology. That's really what why we do this >> and I want to dig into the presentation a little bit. But can you just set the scene for our viewers about what kinds of how big a problem fraud is with these financial institutions and how much money is on the table here? >> Well, I don't want to get you to the actual dollar figures, because each dimension off it comes up with a different aspect to it. Waken say that in full in federal, we have a full caseload year after year, decade after decade that end up with multiple millions of dollars worth of fines just on the civil cases alone. And then there are, of course, multibillion dollar worth problems that we read in the media cases going as far back as Bernie Madoff. Case is going through the different banking systems so that our various kinds of fraud across the different financial sectors, of course, we're focused on the capital markets alone. We don't do anything with regard to banking or things of that nature, But even in our own case, we franchise composed of nearly 33 100 people on all of us, engaging the fulltime task of ensuring that markets are fair for the investors on for the other participants, it's a big deal. >> So in your in your presentation, you told the story of two of your colleagues who are facing different kinds of challenges to sort to make your story come alive. Tell our viewers a little bit about about their challenges. >> We spoke about Brad, who is an expert. He's an absolute wizard when it comes to market regulation, and he's being doing this for a long time on DH What I shared with the members of the audience earlier today. Wass He can probably look ATT market, even data on probably tell you what the broker had for breakfast. >> That >> scary good on. We also shared the story about Jamie, who is in the member supervision division offender, a wicked, smart and extensive experience. So these are the kind of dedicated people that we have a fender on guy took up to Rhea life use cases sort of questions that they face. So in the case of Brad, it is always a question of Hey, we're good. But how do we get better? What is the unknown unknown there? The volume of transactions in the market keeps going up. How do we then end up with a situation where we can do effective surveillance in the market on detect the behaviors that are not off interest that are not for doctor? That might be even. Don't write manipulated. How do we make sure that way? Got it all, so to speak? That's Brad's thing. >> That idea about these? No, these unknown nun note Because we know we have no no known unknowns with the unknown unknowns are even scarier. >> Exactly. They are, and we want to shed light on that for ourselves and make sure that the markets are really fair for everybody to operate him. That is where use of the latest technologies helps us get better and better at it. To reduce the number of unknown unknowns to shed light on the entirety of market activities on toe, perform effective surveillance. So that was a just off our conversation today. How we have gotten better in the past 45 years, how machine language machine learning based technologies have helped us how artificial intelligence that we started working with specifically, neural networks have started helping us even further. >> Okay, okay. And then Jamie had a problem, too. >> In Jimmy's case. Member supervision, if you will. The problem is off a different context and character. They're still volumes of data. We still receive more than 1,000,000 individual pieces of document every year that we work with. But in her case, the important aspect of it is that it is unstructured data. It makes sense to humans. It is in plain English, but the machines, it's really difficult. So over the past two years, way have created an entirely new text analytics platform on that helps us parts through hundreds of thousands of different documents. Those could come from e mails it to come from war documents, spreadsheets, evenhanded and documents. We can go through all of those extract meaningful information, automatically summarized them, even have measures off confidence that the machine will imprint upon it to say how confident I am. I that this is off relevance to you. It will imprint that. And then it represented Jamie for her toe. Use her judgment and expertise to make a final call. One thing that we are really conscious about is way. Don't let algorithms completely take everything through. We always have a human. So we think of a I as really assistive intelligence on. We bring that to a fact for our business, >> and I think that that's a really key there, too, for the for the employees is to know that this is this is this's taking away some of their more manual, more boring tests and actually freeing them up to do the more creative, analytical problem solving >> you hit you. I think you hit that nail right on the head. All the tedious work the machine bus on. Then it leaves humans to do like you said, Absolutely the creative, the inter toe on the final judgment call. I think that's a great system. >> How much to these solutions cost way >> generally are not pricing these things individually, however overall, one of the things that we did with the cloud was actually reduce our overall cost ofthe technology. So from that perspective, we don't look at Costas, the primary driver, although many times these things do end up costing less than the prior system that we would be in. However, the benefits that offer to our clientele, the benefit that it offers to our business, to the people that are investors in the stock market, that is tremendous, and that has a lot of value for us. >> So what is next for Finneran? I mean, this is This is a really moment for so many industries in terms of the the rise of cyber threats, the end and fraud being such a huge problem. Privacy thes air the financial services industry more than, I guess maybe is equal to healthcare. This's really sensitive stuff we're talking about here. What what are some of the things that you have on the horizon? What are some of the things that you're hearing from your members? >> So all of our members treat data security really, really special on really carefully on wear, very deliberate and very conscious about how we treat the data that is interested to us way have to obligations. One is to treat it securely. The other is to extract appropriate insights from it because that's the purpose of why we're being interested with the data. Wait, take both of those dimensions very seriously. Way have an entire infrastructure organization. It's composed off experts in the field way, headed by a chief information security officer with a large team that looks at multi layered security right from the application defending itself all the way to perimeter security. We go off that we have extensive identity and access management systems. We also have an extensive program to combat insider tracks. So this type of multi layer security is what helps us keep the data secure. >> And >> every day we do notice that there are additional track factors that get exposed. So we keep ourselves on the edge in terms ofthe working with all the vendors that we partner with in working with the latest technologies to protect our data as an example, all of our data in the cloud is completely encrypted with high encryption, and it is encrypted both at rest. I'm during flight so that even in the rare case that someone has access to something is gibberish. So that's the intent of the encryption himself. So that is the extent to which we take things very seriously. >> I want to ask you to, but the technology backlash that we're seeing so much and you're you live here so you really know about the climate that does that technology industries, air facing for so long. They were our national treasure and they still are considered it all in a lot of ways. The Amazons, the Googles, the facebooks of the world. But now we have a presidential candidates calling for the break up of big tech and and they And there's been a real souring on the part of the public of concerns about privacy. How What are your thoughts? What are you seeing? What are you hearing on the ground here in D. C? >> With specifically with regard to where we operate from Infanta? We've tried not to access or use any data. That is not for regulatory purpose. Wear Very careful about it. Way don't sprawl across and crawl across social media just on a general fishing expedition. We try not to do that. All of the data that we take in store on operate technology upon we are entitled to use it for by policy are my rules are my regulation for the specific purpose off our regulator activities. We take that very seriously. We try not to access data outside off what we have need for on. So we limit ourselves to the context and that, if you look at, is really what the public is trying to tell us, don't take our data and use it in ways that we did not really authorize you to do. So So the other thing is that franchise on our profit, not for not for profit institutions. We really have absolutely no interest beyond regulatory capability to use the data. We absolutely shut it down for any other use way are not so that way. We are very clear about what our mission is. Where we use our data, why we use it and stop. >> Great. Well, Raghu, thank you so much for coming on the Cube. It's been a pleasure talking to you. >> Thank you. Thank >> you. I'm Rebecca Knight. Please stay tuned for more of the cubes. Live coverage of the es W s public Sector summit here in Washington. D c. Stay tuned. >> Oh,