Red Violet, Inc. Common Stock 17th Annual Midwest IDEAS Conference

Good. Up next is our client, Red Violet. They are trading on NASDAQ under the symbol RDVT. Red Violet is a leading identity intelligence and analytics company that helps organizations verify identities and mitigate fraud and financial crime risk. Presenting on behalf of the company is Camilo Ramirez, Senior Vice President of Finance and Head of Investor Relations.

Thank you, Errol. Thank you, everyone, for joining me. As Errol said, my name is Camilo Ramirez, SVP of Finance and Investor Relations. I had asked Errol not to schedule the first meeting of the day in hopes that everyone has had their coffee, ready to engage. I like to keep these very informal. Feel free to ask questions. I feel like they are much more productive in that manner, as opposed to just me lecturing to you guys up here. So today, I will go over what we do. I will give you a little bit of management history, our business model, a couple use cases, and run through our financials and so forth. Excuse me. With that, so Red Violet, what do we do? We like to say we are all things identity. I will not really flip through a lot of these slides, but I will just stop on a couple.

Like I said, it is much more conversational. I will just leave it on our company journey for the time being. So all things identity, what does that mean? We say we are applicable to every transaction that occurs in the U.S. We want to understand the individual on that other side of that transaction. With that, management has been together for call it about three decades. In the late 90s, they started a company called Accurint, ultimately sold that to Reed Elsevier's LexisNexis. I apologize. The product name is Accurint, company name was Seisint. Sold that off to Reed Elsevier's LexisNexis for about $750 million. Non-competes expired, got back together, started TLO. Ultimately, one of the founding members ended up passing away. His name was Hank Asher. He is credited to creating this space, what we like to call data fusion.

Aggregating those disparate databases and creating that profile on that adult individual within the U.S. He ended up passing away abruptly. They were still going through the product development phase. They had not commercialized just yet. Ultimately, sold that to TransUnion for just under $200 million. Non-competes expired again. As you can imagine, technology changed drastically from the early 2000s to call it around 2014. There were a couple bidders on that second iteration that lost out, approached management team. "Hey, do you guys have another go in it? If so, how would you guys do it differently?" A couple of differentiating aspects was with the technology. You had the early innings of large language models. Everyone likes to think, hey, AI, this new feature today, right? New technology, but it is all based off those large language models that has been around for a number of years.

We like to say our platform has been AI embedded from the very beginning. As opposed to our legacy platforms, they are built out in the data warehouse room, so they cannot scale up or scale down depending on peak productivity hours. I will jump into a couple use cases, kind of explain what we do. We aggregate those disparate databases. We say we have a 75-year longitudinal identity graph on the adult U.S. population. As you transact through society, you are graduating from college, purchasing your first home, purchasing a vehicle, getting a cell phone, and so forth. You are leaving that digital transaction within society, and we want all that information, aggregate that information, bring it in-house, create that profile on that individual. With that, we like to say we serve five verticals. Those five verticals, I will start with the easiest to understand. It is going to be collections.

In collections, let us say you have a debt buyer. They buy 1 million records from Capital One. They need to understand right party contact information. Is anyone deceased? Because they are not going to waste their time on that. Has anyone filed for bankruptcy? Because they cannot call and collect on that, or they get fined, call it $10,000 per call. They will batch over that information over to us. We will append the information, send it right back. How we win in that space, it is typically the waterfall effect, right? With their files, they will go to their tier 1 provider. They will send those 1 million records. There will be some fallout, call it 15%. They will send that 15% to their tier 2 provider. It has been scrubbed over. There will be some amount of hits that were unsuccessful. We will come in third tier.

We say, "Hey, we have high confidence in our data asset." We will come in tier 3 on that data that has been scrubbed over twice and ultimately have a high hit rate. We will move up the tiers from that perspective, right? Because they want the highest hit rate at tier 1, so they can have economies of scale, get the best pricing there. Second industry, we will go to investigative. In investigative, that is going to be law enforcement, private investigators. On the law enforcement side, about two years ago now, we brought over Jonathan McDonald. He was basically credited with building the public sector revenue at TransUnion from zero to where it is at today. We built a team around Jonathan McDonald, and we verticalized that team from what we call Fed and SLED. Federal go-get and then state, local, educational, and law enforcement on the SLED side.

I will give you a couple use cases on there. Your most obvious one is going to be law enforcement, right? They need to understand the criminal actor, and there are a couple differentiating ways you can interact with our platform. One of them is going to be our mobile application. There was an accident or some crime committed. A witness says, "Hey, it was a red F-150. I have a partial plate. I have two letters." Within our mobile app, you can basically just drop a pin, do a mile radius on that, and start entering Ford F-150. It will drop pins. Red, it will remove those pins that do not match. Here is your partial plate, and you are left with about, call it three pins, right, that match all that criteria. You can go investigate. On the law enforcement side today, we are powering about 1,000 law enforcement agencies within the U.S.

To scope out the space, there are about 15,000 to 17,000 law enforcement agencies, depending on what source you cite. Derek, our CEO, just on our earnings call, announced that we won the largest law enforcement agency in the U.S. That is a well into six figures contract. It was a really exciting win. We won that actually from the competition. We came in, priced it. The competitor basically came back at the last hour, said, "Hey, we will reduce price for you. We will compete on price and beat Red Violet's IDI product." Ultimately, the law enforcement agency said, "Hey, we truly do not care about price. What we care about is accuracy and completeness." Ultimately, they signed that contract with us, and it is a multi-year contract as well.

We are really excited they are proving out our data quality as opposed to, hey, we are not going to go compete on price, essentially. A couple other unique use cases on the SLED side are going to be homestead exemption fraud, right? You have individuals saying, "Hey, this is my primary home residence. I live in South Florida," and truly, they do not live in South Florida for the majority of the year. We are validating that that is their primary residence, or even on the educational side, it is going to be a residency verification. A lot of times they will say, "My son lives with his grandparents who is in the better school district." You have overcrowding in certain schools, and then other schools, they are underperforming. Their attendance rates are down essentially because all those kids are going to the better school districts. These school districts will do address verification.

Hey, is this child truly supposed to be at this school? If not, then push them back to the appropriate school and so forth to balance out that attendance across school district and they are not losing budget dollars for specific schools and having to close them down. Or even veteran benefits, something like states want to understand their inbound and outbound population of veteran for benefit purposes, because they get budget dollars based off of the usage there. Reaching out to those inbound veteran individuals, educating on the services the state provides and so forth. On the federal side, we say SLED has been performing very well, been performing ahead of schedule from what we have laid out with Jonathan on the fed slide.

We always get the question, "Hey, how is your federal business doing?" Today, it has fallen behind of that expected schedule, but I believe it is part of we did not appreciate that long procurement cycle. Some of these contracts are multimillion-dollar, multi-year contracts as well. You can take a look at some of those RFPs that are made public. What we have been seeing, some of those nationwide initiatives have been pushed down to the state level as well. We have been winning those state contracts as a whole. On the fed side, what we have seen, they are basically just pushing down the can. Hey, we have this RFP. It had an end date. They have extended that end date and so forth. If you take a look at our pipeline today versus two years ago, that pipeline has grown orders of magnitude larger than we have in the past.

And some of the contracts that we are getting in front, we would never been able to get in front of those contracts without the appropriate individuals. Some of these do not even go through public RFP. It is who they know, and essentially, it is an outreach program. They will reach out to certain vendors, say, "Hey, here is the RFP, bid on it," and so forth. We are really excited about the pipeline on the federal side as a whole. Third industry, I will go to financial and corporate risk. On the financial and corporate risk side, that is going to be like KYC, know your customer, background screening. So background screening, good use case there that compares us to competition. Early on, we won a customer called Innovative from TransUnion. What they do, let us say you are Walmart. You have an applicant come in, they list four addresses. Walmart will send that to Innovative.

Innovative will call out to us, say, "Is this data accurate and complete?" We will say, "It is accurate, but it is incomplete. They left off that fifth address." And that is usually where the criminal history lies. So they will pull that criminal history, send that information back to Walmart. Ultimately, Innovative was purchased by Appriss. Appriss was then purchased by Equifax. As you can imagine, Equifax has way more data than us, significantly larger balance sheet. That Innovative contract was up for renewal. They came, said, "Hey, can we get a three-month extension?" I said, "We understand. We are here to help. We understand you are trying to execute on synergies. If there is anything we can do, let us know." So they asked for a couple more three-month extensions.