Background Checks Without Bottlenecks

Key Takeaways

1. Only 31% of CHROs are confident in their ability to prevent fraud in the hiring process — meaning two out of three organizations likely have fraud in their pipeline without knowing it.
2. Fraud enters through the gaps between disconnected systems. Every handoff between your ATS, video conferencing, background check provider, HRIS, and onboarding tools is a potential point of injection.
3. AI has scaled fraud at the same rate it has scaled productivity — fraudsters can now generate far more fraudulent applications, resumes, and identities than they could two years ago.
4. Checkr's three-layer identity verification approach — document validation (including DMV verification), liveness check, and device fingerprinting — establishes a consistent identity that travels through every subsequent step of the hiring process.
5. The Ashby and Checkr integration eliminates the swivel-chair problem: background checks are ordered from within Ashby, candidates are engaged automatically, and all status updates flow back into Ashby so hiring decisions can be made without leaving the system.
6. Checkr turns around over 80% of background checks in less than an hour, dramatically reducing the time between offer and start date.
7. Trust doesn't end at hire. Continuous monitoring and moonlighting detection are part of a living trust layer that extends through the full employee lifecycle.

Transcript

Note: transcript is auto-generated and may contain minor inaccuracies.

Amit Fernandes

Thank you. Hey everyone. Nice to see all of you and nice to meet you. I lead enterprise products at Checkr, and one of the things that I've learned over my time working with enterprises is that they hate switching between apps. They love for their business processes to be seamless and connected. So today I'm going to start by talking about why Ashby and Checkr are the right partners to bring you this conversation. Ashby really transforms the way you attract talent and engage them. And Checkr, on the other hand, is a leading background check provider as well as a leading identity verification provider. We work with hundreds of thousands of companies. Together, we actually see a candidate throughout their journey, from their very first click when they apply for the job to the point of time when they get onboarded. And that is the kind of visibility that you need into your hiring processes to make sure you're hiring the right folks.

Amit Fernandes

Checkr has been in the industry for coming up on 12 years, and we do a few things really, really well. We went and started talking to a set of our CHROs out there, and we tried to ask them what's going on, what's top of mind for you. One of the things that we saw that stopped us in our tracks is when we learned that only 31% of CHROs are confident in their ability to prevent fraud in the hiring process. That is quite insightful — that's basically two out of three people saying that fraud is in our process, and maybe we don't know about it. Or maybe we haven't caught it yet. The other thing that we noticed is that a lot of these CHROs are also wrestling with the fact that they want to put fraud prevention mechanisms in place, but they do not want to make the candidate journey too frictional. In general, candidates — being one myself at some point in life — if I feel I'm being surveilled through the process, I'll disengage. So they're balancing two things: prevent fraud in hiring, but at the same time, don't make the candidate experience frictional. It's a tough balance. Fundamentally, this is not a technology gap. This is really a strategy gap.

Amit Fernandes

Why is fraud getting into the hiring process? If you look at the process, it's probably anywhere between four to six systems involved — an ATS, a background check provider, a video conferencing app, onboarding tools, I-9, an HRIS. There are so many tools. All these tools are rarely ever connected. Companies have spent years trying to drive integration projects and they never get done. Fraudsters have also figured that out now. They know that these systems are not connected. There are seams and gaps in the process. And they keep hitting the process until they find where they can get in. And arguably, a lot of what is happening now is happening partly due to what AI is doing on the other side. Fraudsters are using AI to scale the practices they already were aware of. They can churn out more resumes today in the same amount of time that they churned out one resume two years ago.

Amit Fernandes

Hiring was never built as a process to prevent fraud. It was built for operational scalability. Most talent acquisition professionals are always thinking about how do I identify the right candidate, bring them through the pipeline faster, close them quickly. None of them has been thinking about how do I prevent fraud while I do this. What you're looking at with Checkr's Trustly is our approach mapped across the entire hiring process. At sourcing and application, we verify who someone is, whether their resume holds up, we re-verify their identity and go deep on credentials and employment history. We flag any fraudulent documents that come across in the process — fake passports, fake education transcripts, fake I-9s. And then post-employment, the work doesn't stop — we get into continuous monitoring. There's an emerging conversation around moonlighting: people working multiple jobs without informing both employers. Now there's nothing fundamentally wrong with doing multiple jobs, but if your employer doesn't know about it, they don't know if there's a conflict of interest. This is a living trust layer. Fraud does not stop after a person is hired in many cases, because people's behavior and future can change.

Amit Fernandes

We've been in business for about 12 years and we've run millions of background checks — we process close to two million checks a day. Over the past decade-plus, we've actually seen about 270 million-plus people. So we understand their identity. Beyond background checks, Checkr has multiple lines of business — lending verification, personal checks, tenant background checks. As we see people through different phases of their life, we are building out their profile. We understand who they are. So we don't just ask, is what this person told me true — we ask, is the totality of everything we know about this person, does that make sense? And we combine that with behavioral signals, device, IP, use of VPNs. That is what gives us a 360-degree view about an individual.

Amit Fernandes

One of the solutions I'll walk you through is our IDV — identity verification — solution. There are three patterns to identity fraud in the hiring process: synthetic identities (made-up identities), document manipulation (fake driver's licenses), and proxy candidates — one person shows up during the interview, but a whole different person shows up on day one. And you have no way to correlate those two other than your hiring manager saying, that's not the person I saw on Zoom. What we do is three layers. We first do document validation — we get a valid document such as a driver's license, and we validate it against the DMV. We then do a liveness check to make sure that the image of the person matches the document they presented. And then the third thing is device fingerprinting — trying to understand what is the IP, where is this coming from, is this IP related to a list of known fraudulent actors. IDV is the essential foundation. Once we have that, we take that same information and run it through the subsequent steps of the process. The same name, the same date of birth, validated upfront, flows through. So the points of injection are blocked right there.

Amit Fernandes

Let me walk you through how the Checkr and Ashby integration works, because this integration is really, really easy to use. In order to order a background check in Ashby, you go to a candidate, within that candidate's context you look for the background check and select a package. What this does is kick off a process through Checkr where the candidate receives an email to come participate. They go through IDV. Once they do, we collect additional information from them. All of this is happening behind the scenes. In Ashby, what starts happening is you start getting all the status updates feeding in. As the candidate is participating, as the background check runs, things get completed — whether it's the criminal check, employment history, education history — all of that comes back into Ashby. This really prevents that swivel chair that usually happens between multiple apps, where people are going from the ATS to the background check provider and back, or trying to type information from one system to another. We eliminate all of that. The talent acquisition team is then focused on making sure they're hiring the right candidate versus doing the menial task of status updates or data entry.

Amit Fernandes

The other thing that we're doing through this process is truly reducing time to hire drastically. We got the candidate's information from Ashby and from the candidate themselves, combined that, started running the background check. Our background checks largely turn around in over 80% of cases in less than an hour. And if you expand that to a day, that's a really high percentage. That allows us to bring that information back where people can make decisions quickly in Ashby. You see all the statuses in Ashby, you can make a hiring decision right there. You don't have to wait to go into Checkr or do anything there. Of course, you'll always get the link back in Ashby if you want to pop out and see more detailed information. Three things to think about as you take this back today: map your hiring handoffs — every gap between your systems is an opportunity for a fraudster; connect Checkr to Ashby — it's one integration, you kick off everything in Ashby, your data lives back in Ashby, you make your decisions in Ashby; and take the power of what's happening with over 130,000 customers — every check you run is based on what we've seen before, so we can help you drive things more accurately and faster. Thank you.