Brainfood Live On Air - Ep416 - The Next Generation of Background Screening

0:04You know, genuinely innovative in terms of how they're reformulating and their business, their processes and everything. But then I speak to my friend who works at a very famous bank and he's saying, yeah, all my time is spending doing reports.

0:17Ben Mones: like they say that they say that like uh my venture investors are like 50 of my companies are absolutely screwed right now 50 are thriving so i think there's gonna be you know uh a good amount of weird winners and losers that you weren't expecting over the next couple years it'll be like a pop of a big exit or funding round and then that company went out of business or that company was acquired with absolutely no metrics or price announced in the press release. So yeah, in any event, it's a super dynamic time. And I think just because OpenAI and Anthropic, and they do crazy stuff, crazy stuff.

1:02Ben Mones: Don't get me wrong. Things that I'm surprised about almost every week. But just because these companies are massive doesn't mean that HR teams at a 2 ,000-person company are like, we need to go fully agentic this year. That's just not happening. Yeah, and there's different companies, different degrees of complexity as well. What kind of business you are. I think, again, the people who are dominating the discourse are software companies that produce software solutions, etc. Okay, that's big, but it's like maybe 5 % of the global employment rate of people that work in those companies. I've got a good friend of mine.

1:40He's a head of TA at a sugar beet company. Sugar beets. and I make a rule speaking to him every month because he keeps me grounded because I'm asking him, hey man, what are you doing with AI? And he's saying, listen, I can't speak to you. I'm on a tractor right now.

1:56Ben Mones: Yeah, yeah, yeah. It's like, okay, I get it. Yeah, we're going full. Well, that could be a big company, AI for Sugar Beats. That could be what the next big thing is. You know, that could be the next job. It is a huge company, it has to be said. But because of the diversity of job roles they're hiring for, it's a very complicated business. It's not just a bunch of people staring in front of the screen, you know, calling AI agents to do stuff. You got, you know, all kinds of different types of roles there. So super interesting. Anyway, folks, welcome, everybody, to Bradfield Live on it. Are we live?

2:28I know. Are we live on Facebook? Of course we are, Ben. You know, we just cold launched this. Cold launched, man. Let's go.

2:36Ben Mones: I don't need a transcript. I don't need a beat. I don't need a beat. Just give me the mic. They don't help, you know. I mean, I think transcripts, sometimes if they're done well, they really help. But these days, they're so AI gen that we stop reading them. And it's like, I'm not reading that. And then, of course, we don't need to read it. It's like, I come on. I'm like, all right, hon. Three stories, one impact. Let's talk. You know, it's like, come on. You sound like ChatGPT. You sound like ChatGPT. That's it. And this is, you know what? The audience knows what it wants anyway. And it's basically listening to your take on not only the state of the software dev world, but also the state of background screening, which is, of course, your lane.

3:23And I don't see many people that have seen that journey as close to the coalface as you in terms of building products or solutions according to customer demand. So it's going to be a great conversation. folks if you're watching this on crowdcast let me know whether the audio and visual is okay you should see myself and Ben Moniz CEO of Fama on screen we should be live streaming this by the way on a number of LinkedIn lives certainly my LinkedIn if you're watching this on my LinkedIn let me know I think it's on my LinkedIn too

3:54Ben Mones: shout out my LinkedIn network what's up what's up everybody all of Moniz's fan base is yeah yeah my like my like 20 people that exist in like my my family and uh you know like one other yeah yeah something like that's it you gotta rope you gotta rope the uh the extended famine to okay crowd cast is good i think it's good we're ready to roll thank you very much allison kevin michael appreciate it um okay let's do some intros because ben folks might not know who you are worth just you introing okay who are you what it is you do and tell us a little bit about pharma i'll skip the philosophical who am i question because that could be a whole other podcast between us but everybody i'm ben i'm the founder and ceo of a company called pharma we sit in the talent screening world you might know that know us as the uh gen z background check millennial background check we bring who people are online into the talent screening process helping employers identify behaviors that they've long cared about but instead of doing it through assessments, interviews, or other screening.

5:05Ben Mones: We do it through who people are online. So that's me 11 years in. Fun to be back on the pod with you, Hong. I'm out of LA. It's 7.06 a.m. here, so I got the lights on. I got my double shot of espresso in my belly, and I'm ready to party. Yeah, fantastic stuff. And it's worth saying, background screening has been around for a long while, right? Everyone's been doing that. Yes, let's confirm who this person worked for. Let's make sure they haven't said things that are untrue. But there's that style of background screening and then your style of background screening. And it was always very different.

5:43When Fama launched, it was always a different place. So can you tell us a little bit about why it was different then? And then we're going to talk about why it's going to be even more different going forward. Yeah.

5:52Ben Mones: Background screening started, many say, in the 80s with lie detector tests. I talked to some of the executives who'd been around in the game for a long time, and they would literally lug lie detector machines to companies and giving that as a pitch to employers about whether or not somebody was who they say they were, right? And so over the years, I think it was kind of throughout the 90s, something of what I'd call a bit of a middling industry, sort of like there were people who were doing background checks, but it certainly wasn't nearly as common practice as it is now. 9-11 in the United States really changed a lot for companies around security in the workforce, making sure that the people they're hiring are, again, reflective of who they say they are.

6:33Ben Mones: They don't have a criminal who's coming to work for you, right? So I think, you know, historically, it's been a lot of what I would term, you know, database lookups, people who are, you know, querying national criminal databases, employment verifications, education verifications. And it was done on fax machines, you know, historically, right? This was something that had happened on paper formats for a long period of time. And so I think over the years, what we've seen is sort of a shift in background screening as a function of the changing workforce, both in demographic and technology. And I point to an obvious one that I think a lot of listeners are probably familiar with.

7:14Ben Mones: But it's never been easier, you know, for people to create a fake identity, right, to use a deep fake, to appear to be someone that they are not. And that technological change that's really happened in the past four to five years has spawned this massive IDV industry, this identity verification industry that is now big companies like ID.me, SoCure, but every background screening firm we work with now is now offering identity verification because this candidate fraud is a big problem for a lot of people. Right. So background screening firms tend to kind of skate to where the puck is, as they say, you know, and they try to as really what are effectively managed services firms.

7:57Ben Mones: These aren't really technology companies. They do have technology, but they get their social media check from Fama. They get their drug test from LabCorp. They get their work number from Equifax. Right. They bundle that all together into a single SKU. So, you know, I would say the firms themselves have kind of evolved along with the market. Social media screening, our world, has really evolved as a function of the demographics and the workforce. You know, I often point out, like, today we've got 50 % of the workforce today. That is Gen Z and millennial. That number is going to climb to 70 % by 2028.

8:34Ben Mones: Six generations in the workforce right now. And, you know, for a millennial like myself and those of you listening, you know, like, we spent our lives online, right? I got my social media account in 2004, right? That rich tapestry, who we are, that sort of third space between home and office for a vast majority of us is that online space now, right? And so the behaviors, people acting like you were saying, in time of other people, evangelizing the use of illegal drugs, how somebody can act around a client, say something misogynistic. You know, those are things that you would screen for in other ways.

9:06Ben Mones: But because so much of the workforce is online today, background screening firms are selling social media screening as a component within the broader background screening process. So yeah, a long way of saying it's sort of the what has certainly evolved over the years. I'm sure we'll talk more about the how, but it's been a lot of dynamic. It's kind of like this boring part of HR tech too. No one ever talks about it, but everybody does it. That's the thing, right? Like no one wants to talk about it, but almost every company does it today. It was boring up until about 18 months or so ago, Ben. But then suddenly I think TA realized, hey, listen, whose responsibility is it if we start getting like fake candidates, maybe dangerous candidates into process, into flow?

9:55It's not on the hiring manager, folks. Who is it? It's us, right? It has to be the TA function. So suddenly talent acquisition, sort of suddenly this component of hiring, which I think was always at the very end post offer type of administrative check the box, that's now going all the way up to the top. And now from the top, it's actually now going all the way through the process, continuous verification. How do we make sure the person we verified at the beginning of the pipe is actually still the same person in the third interview? because guess what? There's like bait and switchers going on and all kinds of crazy stuff, right?

10:34Ben Mones: Crazy. I mean, the ID explosion has been really interesting to watch on the sidelines, right? I think there are a lot of new startups that are coming out. I've seen some pretty cool resume scanning of looking at the HTML and a resume and extracting metadata from a Word document or a PDF to try to determine where it came from. Of course, you have the actual, you know, identity management itself at the ID level. But background screening firms are also constrained in a way by the Fair Credit Reporting Act in the United States and the GDPR over in Europe. Right. So even being able to pull some of these cards, so to speak, of being able to offer identity has some of its compliance restrictions.

11:16Ben Mones: Right. So that's another thing I think to remember about this world of ID and also more generally background screening. You know, you look at the the workday case from last year, right, that hinged on this concept of explainability and letting a candidate know that they were being screened right as compiling public data about someone to make a hiring decision. Is that something that falls under the remit of the FCRA? So, you know, these questions of obtaining candidate consent, informed consent, how you use this information in the process, right? Do you share a copy of the identity match or failure thereof with the candidate when you say, hey, we're not going to continue down this process, right?

11:53Ben Mones: A lot of that plane is being built in real time. So absolutely, there's a fundamental change happening in the industry. But I think, you know, a lot of practitioners have realized that there's a compliance sort of framework that all of this is embedded within that has to be followed, regardless of the industry that you're in. So, yeah, it's super interesting that the law is not necessarily catching up with what's happening on the ground. Yeah, and one of the things I love about sort of Fama's sort of most recent approach is actually it seems to sort of bake in the company's policy in advance of doing the screening.

12:31So it's not a case of let's screen everyone with the same screen. And then it's up to the, I guess, up to the company then to figure out what the important signals are. how you guys do it is actually take the policy per company. What do you actually screen for? What do you care for? What are the things, you know, what are the values that you have? You're very pro against. What are the values you're very hostile to? And then from there, it's going to be able to screen the candidates. Tell us a little bit more about that, Ben. That seems like a new innovation, really.

13:02Ben Mones: You read the press release. I love it, hon. You're coming in prepared. And can you hear this like buzzing, by the way? Can you not hear any background noise or someone who's like using a table saw, I think, on the floor above me. I'm okay, yeah. I'm okay, yeah. Okay, good. Perfect. I was going to throw in my AirPods if so. But yeah, you know, so when we started Fama, right, 2015, I mean, I posted on LinkedIn sort of a drawing from a notebook of like putting the UI together early on, right? And so when we started the social media category, there was vendors in the space. You know, there were a few.

13:36Ben Mones: But they were like teams of analysts that would manually control F through a person's social media account for this kind of like fixed rubric of risk that they came up with. Right. And so when we took more of a, you know, my background, as we talked about before, is in enterprise software and, you know, working really within the cloud with enterprise clients on a range of different solution types. And so we took more of a design thinking approach to building the product. So a long way of answering your question. I think a journey of how we got there is useful, but we had to come up with the UI.

14:09Ben Mones: We had to come up with not just the UI, but the fundamental usability concepts, paradigms, and principles. And so for those of you that know FOMA, we basically offered this set of generalized flags and keywords. Flags being things like cannabis, intolerance, threats, harassment, violence, things that typically mapped towards what employers cared about when they were going through the pre-employment screening process, but giving employers the option to kind of turn certain flags on or off, right? So if you were in California where I'm at, you might turn cannabis off as a screening criteria, right?

14:46Ben Mones: You might leave that on if you're in the United Kingdom, for example, right? And so we gave customers the flexibility to kind of configure what they wanted to screen for. We let's say I wanted to see how people were, you know, criticizing my company, for example, you know, let's say I'm in a controversial hot space, big four consulting, just make it up. I want to see how people are, you know, railing against the consulting world. I would plug in a company name, right? And so that would leave employers kind of filtering through what I would describe as like generalized definitions of flags and keywords and comparing it to a policy that they created, right?

15:22Ben Mones: And that's very similar to how software companies have existed forever, right? The user takes the output from the vendor, from the product, whether it's an analytic, a report in our case, a dashboard, an insight, right? They act as the orchestration kind of translation layer between the output from the vendor and the desired outcome in our world, brand safe, culture safe, you know, reduced risk in the workforce candidates, right? That's what we do. That's our value prop, right? And so, you know, to customers, it was often this question of like, all right, well, we have to have a definition of intolerance that works for, you know, a big social media company like Bike Dance and a really conservative bank, you know, that we work with, a global bank, a hospital system in New York or, you know, Louisiana.

16:13Ben Mones: And same way with like a, you know, Saudi oil company or something like that, right? So you have to have these like general definitions of risk. And you can imagine that would leave users filtering through a lot of generalized results to determine what mattered to them. And that created interesting challenges for us. It built the market. It was what we decided to do. It spawned a whole set of copycats and other sorts of vendors that did the same thing. So we certainly created the category early, but But to your point, the art of the possible of technology fundamentally put us in a position where we could question that paradigm.

16:56Ben Mones: And we could ask ourselves, what if we flipped it around? What if we took the end state and began with that at the very beginning of the process? And that's what we call policy-based screening. And that's Fama Lens, the product that you're referring to, right? And so what that is, is the ability for us to transact with a customer, an enterprise customer, not at a, here's what our flags are, here's what they mean, here's how the keywords work, here's how you use them, here's what's in, here's what's out, here's what you should probably, you know, squint your eyes and just kind of look over. We could kind of get why we call that intolerance.

17:28Ben Mones: It removes that entirely from the process and allows a customer to transact with us around the policy, around what effectively today are things that a lot of enterprise customers. And I think this is kind of, you know, what you and I were talking about briefly in the pre-show, this sort of evolution of software. but it sort of closed this gap of orchestration and translation that a user has to do with the company's products and brings it a lot closer to the ROI statement, which is where software companies want to get to. But we always have to kind of like explain to the customer how to get there.

18:01Ben Mones: Right. And we try to make it as simple as possible. We spend a ton of money on enablement, on training, on content, on video, all sorts of things. We do podcasts, right? We do everything to try to make it easy to understand what we do and really functionally how we do it. But Lens essentially is a multi-agent consensus architecture. So think of it as like a series of agents doing work on your behalf that in our world of hiring, fully observable, fully traceable, fully auditable, no black box, every decision that the agents make on your behalf based on your policy traces back to a policy element.

18:38Ben Mones: So we never like showed a customer a log before. And for those of you that like, don't know my nerdy, nerdy ass world of software, what a log is, is like everything that happened in the software to get to the outcome, right? And so those are things that you would send to like data security or InfoSec as part of like a thorough cavity check to make sure your software does what it says it does. but now we're actually providing logs to our users. And we're saying, look, this is what the agents are doing on your behalf, right? And effectively what that's meant for us is that users now work with us on things that they're familiar with, which is policy, which is a Word document, right?

19:18Ben Mones: Which are the sorts of things that they're able to, you know, really easily wrap their arms around and kind of help coach them through if they don't have a policy, what that policy looks like. So it's a fundamental shift for us. I mean, it is, for people that have used my product before, I think they know what I'm talking about. But if you haven't, imagine being able to go from a vendor that you use to like a managed service almost to a workforce extension without the kind of higher labor component from the staffing firm, which I think is why we see in staffing right now so much softness. And, you know, so many different staffing firms beginning to get a little shaky and nervous because I think a lot of these AI agents, ours included, are coming for some of that labor around the product, so to speak.

20:07Ben Mones: Right. So, yeah, super interesting times. I'm having so much fun, by the way. Like I'm in this game for 11 years now at Fama. And I was telling my wife the other day, like, I've never been having more fun at work because I'm like fundamentally questioning everything that I thought to be true about software. And Fama Lens, you know, is like 98 % reduction in review time. Average decisional rates drop from 14 to one and a half percent. One of our customers saved$450 ,000 on labor alone. And they screen a lot of people. A lot of people. Don't get me wrong. They screen almost 10 ,000 people per year, but they save 450K just by mic.

20:48Ben Mones: And that's including what they pay us to do this work. So it's just, and it's not right now, it's not replacing that work there. That's added budget. They repurpose those people that they had to do other stuff. So we'll see how it all shakes out. But yeah, Lens is here. We're like, we just announced it at HR Tech last week. I'm sure we'll talk a little bit about that. But yeah, it's like generally available in two weeks. So we're jacked up. very exciting so i hope people can sign up to it on a wait list or something um because it's uh it sounds like you know it sounds like one of those things that is gonna it will feel different once you see it because um it's uh it's one of these where you've got essentially i mean i guess you submit your documentation is that how it works is that the flow can you take us through step by step Yeah, yeah.

21:36Ben Mones: Super. And again, you're right. It's going to feel weird. And it inspires so far this strange sense of you can do that delight, you know, kind of questioning. But the way it works now is we're like going to a customer and saying we can screen against your policy. And unfortunately, what I learned through this whole thing this past year, a lot of companies' policies are like very intentionally general, very intentionally vague and designed to limit the liability for a lot of large employers around what they do and what they don't do. So we had to actually stand up a team, Policy Studio team, and we have open roles for it right now.

22:18Ben Mones: If anyone wants to apply, I think it's a super interesting job. But basically, there is a team now that will take a customer through a discovery session. We have these very specific questions we ask, and they're challenging conversations, right? It's like for a lot of customers, this might be the first time they've talked about it. We'll say, hey, look, you said you want to screen for intolerance, but let's stick on that for a second. Is it somebody acting intolerant towards other people? Is it saying it about public figures? Is it illegal drug use? Is it someone who's talking a lot about people around them who are using drugs or just personal consumption of them using drugs?

22:55Ben Mones: And so it's hard questions that employers have never really had to determine exactly where this line is. And so basically what the flow looks like is like a 75 minute policy workshop. We transcribe the entire thing using, you know, granola or, or whisper any of these other tools that are out there. We take that, run it through a policy building agent. The agent builds the policy. And this is the part where humans are involved. I'm not going to act like it's entirely automated because it's not. And I wrote this on LinkedIn, but I think it's important that people know that these product lines are going to be like 50 to 60 % gross margin where average SaaS gross margin somewhere in the 80 to low 90 % range.

23:36Ben Mones: If you look at some of the publicly traded comps that are out there. And so we actually have to do a good amount of like corralling the policy building agent to really reflect what was said in the conversation. So the person who was on the call has to also know how to agent bring a little bit, right? Then we build the policy we created. We send it back to them. And this process takes two to four weeks, depending upon the size of the company. But they pass that policy around, essentially stamp it, send it back to us. That same person who built the policy works within the lens architecture and sets it up.

24:13Ben Mones: And yeah, when it comes to the client, we're like, you really don't. If you want to know how the sausage gets made, we'll show you the lies of what we're doing to create these reports for you. But really, when you go to the client after that point, it's like, we're just going to screen against your policy. We're going to come back in 30 days and we're going to take I'll give you I'll give you an example of an early customer of what that looked like. Right. So we have a big brand that does a lot of work with youth advertising. And they partner with influencers. So they have products that they sell to people who are under 21.

24:51Ben Mones: And they can't have any mention of alcohol, period. Right. So like if an influencer is talking about drinking or getting messed up online, that's a problem. And again, for most employers, it's like, who cares what people are saying about alcohol? You know, it doesn't matter at all. We don't have an alcohol flag. We don't scream for that. And most wouldn't have it in their policy. This happens to be a very big brand that sells things to younger kids. And we flagged on an influencer on their behalf an ice cream cake. And they were like, this seems like a false positive. Let's look at the logs of what the agent did.

25:25In the freaking recipe for the ice cream cake was rum.

25:29Ben Mones: It was like a rum ice cream cake or something like that. And we were like, do you care about this? And they were like, absolutely. We absolutely want to know about that. And so, you know, it's like this, we're all kind of doing it together and figuring it out as we go. But what these agents are able to do with the appropriate tuning, which I think is the most important part. I wasn't expecting the amount of hand-holding we have to do with these agents to get it right. Because, man, this is like a high-paid, pretty significant salary. You guys can look on the site. This is not an entry-level job.

26:06Ben Mones: This is probably, in what, Amazon terms, like an L5, L6 job. This is like a senior IC role at our company. It's cool. Yeah. Yeah, in many respects, though, I think people might be relieved to hear that, Ben, because, you know, we kind of, I don't know, I get the feeling that we do want powerful software, but we also want service as well. Like we want to have someone that cares, that's helping us sort of customize this, helping it make it right for us, and not having sort of us who are too busy, the customers too busy to try and then learn how to do it all. It's like, you know what, I don't have the time or capacity to learn how to do it all.

26:49I want to kind of a do it for you service that's personalized to me. And it looks like this is what the service is going to be. So that collaborative sort of approach to build against policy, again, that feels better than me just uploading a document into an AI and then having what seems to be an output. Maybe I don't trust my own policy, right?

27:12Ben Mones: Or your ability to ensure that the agent accurately reflects that policy and the work that it's doing. And what I think is really interesting, I was talking to one of our investors about this, is if the gross margin on this product is SaaS level, if it's 80 to 90%, meaning if it is purely automated, just like you said, and it's just token cost, Amazon cost, whatever or CloudCos, whatever CloudBridor you're using, you aren't really absorbing any real complexity or nuance in your customer workflow, right? Like you aren't real, you're really just launching another feature. You're launching another product that you've always offered, right?

27:53So I think in a way to show that you're absorbing

27:56Ben Mones: that complexity and that nuance, you have to have a person involved. You have to have a person that can control. So I would say like be, if you're out there buying these sorts of products, like ask about the level of human co-pilot that's associated with it. Because otherwise, if they're saying, oh, it's fully automated, I would just be like, then why don't you just make your product better and include it as a feature instead of like selling it to me as like an AI. You know what I mean? It's like, instead of selling it to me as like this AI agent, if it's just another feature, then why am I paying extra for it?

28:31Ben Mones: You know, like that has to be the question I think that a lot of folks at. I think so. I think the SaaS world has gone down the massive efficiency optimization route. And it's like, okay, let's get everything, you know, self-service as possible. But the more I think about it now, it's like, okay, we know the software is powerful. It's going to be great. But we're also paying for something. We want to pay for a service, right? We want to pay. I want to feel like someone's actually helping me. It's like, I don't want to have to handle this by myself. The AI agent isn't giving me the moral support I need.

29:06Ben Mones: Or the functional support of why we're here. I mean, the promise of this service as software and, you know, I'm sure you all know what I'm talking about. If not, just Google service as software and the word Sequoia. And you'll see this like seminal blog post that they wrote about it. But this services software thing promises an idea and the concept of not just like someone to be moral support for you, but like functional support, like ROI immediately. That's what this is designed to be is not you can take our product and use it to get the ROI. But you can take our product and get the ROI instantly.

29:49Ben Mones: That's the idea. that which it took me a while to understand what we were really talking about here but when i started to see with clients they're like you can do that you can screen explicitly against what we want and we're like extension of your workforce managed service that's that's what this is yeah fantastic i've got to ask about how this is affecting you on a business model point of view ben because at one point you know the sas world was always always stick your credit card in we're going to meter it based on the numbers of people among the number of reports you use. Is it now getting to the point?

30:22I mean, is that still a model for Fama? Or do you now see actually, if we're going to put all of this human ad, the human value added on this, we need to also think about what the service cost is going to be and so on people might, you know, willingly expect, okay, I'm getting a human being plus the tech, the service cost is actually going to be this instead of that. Yeah.

30:43Ben Mones: Despite the strategic implications of the change that we're talking about, you still got to deal with procurement. Still got to deal with that team that wants the price to be lower than it was before. Right. And so, you know, I think what I've kind of prepped my board for, my investors, and I can't speak for other operators on the same, but I've prepped them that this is going to be a 50 to 60 % margin product line and that we are going to sell in a manner that feels consistent with how we've sold this product before. We sell on a usage basis, meaning like 90 % of my revenue is usage based right now.

31:22Ben Mones: It's all based on how many checks people are running. And so Fama Lens will just sit on top of that as like a small added fee on a per check basis because of the agents that are doing the work on a check by check basis. So I think, you know, in simpler terms, per candidate, a slightly additional cost. And we're going to take a small hit on margin in the beginning to see if the adoption curve is there. We don't expect that that will ever get to SaaS margin. And I think if it does, you're not absorbing the real complexity that you promised to the customer, as I said before. So as far as we go to market, no, it's not going to change.

32:00Ben Mones: Our customers are going to, you know, in the same way we've launched a premium feature in the past, like it'll be a same sort of like up level in terms of charge. We're just going to take the hit on the other side. You know, there are different models. There are like there's people who are charging recurring SaaS fees now, usage fees. I've seen one time fees for agents and set up costs being covered, you know, along with a hybrid kind of set up usage fee model. But for us, it's not going to functionally change too much about how we charge our clients. But the margin profile for the business is going to drop a little bit.

32:37Yeah, yeah. Very, very interesting. Okay. What is the state of background screening in 2026, Ben? I mean, I guess it's one of these where, I mean, I did a poll on this. I wonder whether I could share some of this. It's a very, very brief poll. So it's not necessarily, it's only 100 people have only set up yesterday. but I thought okay let's have a look at how many people are using manual sort of referencing what's doing what and what have you so let me just do a screen share I might as well show this allow let's share with audio okay does everyone see my screen yeah we can yeah you see this right so okay the question is simply how do you currently do background checks and I just put four things here.

33:24Point Solution, which is a specific product you buy. ATS has some native solution in there that does something manual. Yes, you do screen, but you know what? You're picking up the phone, you're emailing people, you're doing a social media sweep on your own search, control F, as you mentioned, Ben. And then finally, you don't actually do background checks. Ben Moniz, what do you reckon is the most popular? Where's the distribution here, man?

33:51Ben Mones: give me your guess gotta be point solution number one don't do background checks number two ats3 manual before that's what i'm guessing right point one let's see show results let's see let's see how it looks here we go you're absolutely right on point two one and two ben okay point solution we have 39 percent bear in mind again only 100 votes but you know reasonable size 40 40 have a solution of some sort um don't do background checks is a quarter so literally don't do them um and i think probably because they don't have capacity um ats has something 13 it'd be interesting to see whether that figure goes up or down going forward and a significant number one, one fifth, uh, doing it, but you know what, that's gotta be super labor intensive, right?

34:46Um, grind, grinding through, um, uh, you know, I remember doing reference checking back in my day. Um, and it was, uh, it was tough cause you know, if you, if you wanted to speak to people, uh, wow, that's like having to recruit another job. It was like, can't get hold of this guy.

35:04Ben Mones: Yeah, yeah, yeah, totally. You know, the state of background screening today, it's been a lot of consolidation over the past couple of years in the vendor ecosystem, right? You've had some of the big players consuming another big player in the market. You've had some new players enter the market as well, some really interesting ones in the UK in particular. you know we've seen in like the reference universe as well some interesting you know SaaS you know companies get raised money and get acquired but you know I would say the current state of background screening is again it's like we're the unsexy part of the talent funnel you know a lot of people use us I'm curious what the dispersion of company size is on that poll too of like how many you know companies with a thousand people or more not doing background screening I'd be surprised if that number, you know, was more than 2 % of people who don't do background screening today.

36:02Ben Mones: But, you know, it's one of these things that, again, the industry itself is a reflection and a function of the demographics in the workplace, the technology that's available and the risk that exists, right? You know, I think we are looking at check types that have been around for a long time. I think we're starting to see, interestingly, the world of like education, verification and employment verification, which is a big business. Right. But I'm seeing more and more end users that we work with take that out of what we call background screening their package and replace it with identity or social media.

36:37Ben Mones: right like at the end of the day you know and i was talking with the executive from a a big retailer uh she ran she ran actually she left but she ran background screening for one of the biggest companies in the united states a big retailer i'm sure you guys know what company that is um and and she had said to me that she was nervous about people at her company using clear background check as a proxy for quality of hire, which I thought was like a really interesting comment in that background screening in general is designed to give the user of the product and the company a sense of safety and a sense of security, right?

37:20A sense of like the person that

37:22Ben Mones: I'm bringing in is, you know, absolutely a fit, you know, for my company. And there's nothing about this person that I, you know, they've said that I don't believe to be true anymore. And I think back in the day in the 90s, early 2000s, the only way to do that was a lie detector test, a drug test, right, to go pee in a cup somewhere to get a, you know, criminal lookup, where today still in some counties in the United States, less so over in Europe, but we still have courthouse runners that have to go to a courthouse with a person's name, social security number, date of birth, right? And look up information, right?

37:56Ben Mones: And that question, I think, is one a lot of us have to answer. If you are using background screening for those that affirmatively apply point solution, ATS, or even manually, right? Are the checks that you are doing, doing them just because that's how you've always done it? And that's what security and safety means today? Or are you screening in a way, is your package mix, again, background screening parlance, is your package mix reflective of the risks that you've sought to identify, right? So I think we're seeing a lot of evolution in the composition of a background check right now of what people are including and excluding.

38:32Ben Mones: Drug testing is definitely still a thing, but I'm seeing it more in like construction, manufacturing, oil and gas, where you have to have a clear mind physically and being on the job. But I think the question has to be, are we looking at risk the right way? are we just taking a big hammer and looking for nails with our background screening vendor and the composition so yeah yeah and some interesting comments in the chat here is quite right it's like what is the risk the risk is different per job per person per company right um you know it is you can't just say this person i mean i guess some people might objectively be more risky than than the average let's say i mean perhaps certain personality types certain behaviors you can say You can say that's the case.

39:17But that sort of risk will change based on the sort of person you're looking to hire. And you can absolutely imagine certain job roles actually might want characteristics of this type because it particularly fits that particular role, right? So, yeah, it's all contingent on what the company is. And I guess what we're talking about here is this behavioral assessment from online activity, which is another kind of angle which I want to just dip on here. It's not the background, it's the behavior. I wonder whether that was your genius turn of phrase, Ben, but I think I got it from you guys. Can you tell us what that means?

39:58Is it a description really of the evolution of background screening in the sense that, look, previously we were looking at just the milestones on the resume, but now we're trying to identify how this person actually behaves and what the person thinks and what have you. That's a better judge as to whether the risk is right for us here and for our business.

40:20Ben Mones: Yeah, yeah, for sure. Before we go there, I got to address Bob in the comments here. knocking mustache okay he says anyone with a mustache is suspect i actually agree with your first comment you can be a quality person well not being a quality hire for a specific role but i'll just say two things one bob my wife loves it and you know what hung also likes it before we started hung said he was into it so you know bro i'm pro mustache mate i was actually wearing a mustache at like a totally young age i had facial hair when i was like 13 um and my my mother told me not to shave um which is terrible advice because she because she said hey it's going to grow thicker don't shave it so i didn't shave it um so i had this bum fluff for most of my like 13 to 16 year old heroes it wasn't great you're buying beer for all your friends i'm sure yeah i learned to live with it and and to this day i think but it's interesting how that changes though then you know your physical appearance it's it what is good or bad this also changes with cultural cultural change and you talk to our new generations coming into the workforce for instance i mean i'm old enough to remember tattoos were like a blanket negative uh you know if you had a visible tattoo you were communicating criminality you're communicating okay this person is never going to work a professional job etc and now actually we can see loads of people i think 50 percent of people probably have a tattoo of some type.

41:46It's very, very common. And I think it's dropped away completely. There's pretty much no tattoo, unless it's on your face. I still think that's probably problematic to some, but it's an example of how basically cultures change. And you're referring to this generational change that's coming into the workplace. Guess what? These are all digitally wired up individuals. And a lot of the stuff that you need to learn about these folks is actually going to be in this digital exhaust, this digital life that they lead.

42:16Ben Mones: A hundred percent. And with the idea that we're all in this together and that these sorts of changes we're talking about permeate to all aspects of personal and professional life, I think even acts as a compounding factor for the comment we were talking about at the top and your 13-year-old mustache, which I think you should bring it back. But back to your question on behavior. You know, it does sort of and it is a topic. I wish I could could come up with and be the guy who said it. But we got plenty of smart people in our marketing organization who come up with that sort of stuff. But, you know, the general idea with this concept of behavior gets to what I was talking about before, both with Lens and sort of the evolution of the background screening industry, where, look, I spend a lot of time in this world and not just professionally.

43:07Ben Mones: But when I go home, what I think about, I'm always nerdily enough. I'm always thinking about the world of screening. And, you know, the whole point of candidate screening, I'm not even talking about pre-impact, I'm talking about candidate screening. It's trying to get a read on how this person is going to act when they join. What are they going to be like when the interview is over? Just in simple terms, right? Like we talk about quality of hire. We talk about, you know, Bob mentioned quality person in the comments, right? Like all of that at the end of the day comes down to how is this person going to treat their fellow employees in a meeting six months from now?

43:39Ben Mones: When they're on site with the customer, how are they going to act in front of them? Right. When there's a regulators in the room asking questions, are they going to be candid, direct and honest or are they going to misrepresent the truth? And we have all these different tools and ways that we go about answering that question. The social media screen is one of them. But, you know, for us, it simplifies the concept and the value crop to there are behaviors that employers have long sought to identify as part of the pre-employment screening process. And I'll give you one example for folks, you know, that have also been in the game for a while and are old enough to remember.

44:15Ben Mones: But I remember when I was coming out of college in the early 2000s, a big thing was the resume paper. how thick was the cardstock that your resume was printed on, right? It showed that you have attention to detail, that you prepare, that you recognize the seriousness of the moment, right? That you went to Kinko's, right? And you got the good paper and you printed your resume on, you handed that over, right? So, you know, that was a signal for employers around attention to detail, preparing, you know, being thoughtful about engaging with leadership, right? All components I'm sure that folks are looking for.

44:55Ben Mones: So we equate the behavior thing to a very similar type of concept where, again, for this workforce, if they have spent more of their life online than offline, I know that's a controversial statement, but I believe it to be true. I think if you look at your workplace today and you look at hours spent engaging with the world around them, especially in this digital world that we live in, you would see functionally more time spent on the internet than off the internet, right? And if that is true, then the behaviors that employers have long cared about, those things are appearing online because this online space is where we transact, where we live.

45:35Ben Mones: In other presentations, we talk about, you know, the workplace water cooler now being social media, right? It's where people engage. It's the new crossroads, right? If I'm in a hybrid or a remote company, even an in-person company, it's likely that I'm seeing what, you know, my colleague Hong is posting on LinkedIn and commenting on it, right? Maybe we go to an event together. We take a picture, Hong posts on, you know, Instagram. He says, hey, Ben, what's your Instagram? I want to tag you. He tags me there. I follow him. I see this crazy stuff about mustaches that he's been posting. I'm like, oh my God, mustaches.

46:07Ben Mones: So, you know, it's one of these things, right? That I think the, again, the evolution of the workforce demographic and where behaviors show up is why we try to message that as like, look, this is the behavior you care about. The behaviors are here. We can help you identify. That's where lens like even comes more into play because let's just talk about the behaviors you care about. We'll screen for those things, not flags and keywords. Yeah. I'm going to ask you some follow-up questions. This is super interesting, Ben. There's a question coming in here from LinkedIn, which is again, super interesting.

46:42Ben, does your software, does your product, is it able to look at behavior on the dark web? So this is where I guess these are anonymized sort of places. I don't think anybody's walking around the dark web is first name, second name, right? They've got at least a pseudo, a pseudonym on there. I'm assuming it's going to be impossible to track that and map it back. I I mean, maybe you can pick up presence, I guess, if they're using the same email. Yeah.

47:11Ben Mones: You know what I tell people? This question comes up a lot. We've looked at it. It's interesting. But we're not Palantir. We're not data miner. We're not trying to find the dark web networks or the criminal networks that are out there. we are looking for the typical person who has normalized in a public way, aberrant behavior that your fellow employees will see, that a customer might see. That's a signal of someone. If I'm posting on social media misogynistic stuff about women, that's something that if I'm doing it publicly, it's a point of view that I've normalized. It's a behavior that I've normalized.

47:54right that's more what fama is designed to do we're not here to catch the the criminal on on

47:59Ben Mones: the dark web or anything so we have seen the uses of the dark web but but really it's been like leaked personal information about people where someone could be an unintentional uh insider threat within an organization meaning like their information exists someone uses it to exploit them hey i've got you know your uh bank of america account or your barclays account uh and i'm gonna to take all the money unless you put this zip drive into your computer at work and download these files for me and send it to this address in this prepaid envelope that I'm sending to you. That's where we've seen the dark web is sort of the exposure of personal information that is then used and exploited by hackers.

48:41Ben Mones: But people who are like on the dark web doing stuff, no, not really. Yeah. Yeah. It's also probably a very niche group of people, I would say. I mean, in terms of just is there one in a hundred that would frequent those places i mean maybe cryptos increase that somewhat um i think it's even lower than that i think it's even lower one in a hundred is way too yeah yeah okay next part of it is um we are increasingly online but we're also increasingly talking to ai bet um so i mean is is there a way in i mean i wonder whether you can sort of saw this story. It happened about a year or so ago. And the TA team that asked for this got absolutely put on blast online.

49:22Because what they did was to, I think innocently, but they wanted to see the chat history of the candidate. And I think what they wanted to do is just to figure out how you interact with AI, like pretty much innocent. But the candidate had a problem with it. And that's absolutely outrageous. They put them on blast onto LinkedIn. Everyone's thought, oh my God, how intrusive is this? And I'm like, still half and half on this, because number one, yes, it's intrusive. But number two, it's actually a pretty good idea as to what this house person's thinking, right? Zero filter, you're saying everything to AI, you're sending it all to Sam Altman or to Dariah Al-Madei thinking that's all good, giving them infinite trust, right?

50:04And you're probably getting some real behavior there. Where do you stand on this, Ben? Is this a line you're not going to cross? Is this, okay, we're only on the public social? Or are you thinking, hang on, maybe there is something we can figure out with regards to how this person interacts with their AI?

50:18Ben Mones: I think anything that crosses into the line of a person's private life that was intentionally made private is a problem. And there are plenty of things that all of us do in private, every single person, right? Conversations, jokes we make with our close friends you don't make with your spouse, comments you make with your spouse that you don't talk about with your colleagues, right? Things you say to your best friend at work that you don't say to anybody else at the workplace, right? You know, everyone's got a filter for that sort of thing, right? And so where we like to look, so to speak, is in the public places, in the public forums, right?

50:57Ben Mones: Where that person, again, what we're using is the proxy between someone acting misogynistic, intolerant, threatening, talking about being high at work, you know, that kind of thing in a public manner, because they have normalized that behavior, right, that they bring that into the workforce that creates in this conduct impact. So yeah, in that particular case, the only reason I sort of raised an eyebrow, I would say, no, I'm not with it at all. But what I do like is the almost examining the use of AI. You know, I probably even had done like, I don't know if it was with you or somebody else. But I remember a few years ago being like, you know, candidates are using AI, recruiters are using AI, everyone's using it.

51:39Ben Mones: Well, now we're kind of in this world where certainly in RJDs, we're posting like familiarity with Claude ChatGPT. And, you know, I'm sure folks have heard this, but Anthropic has a rule for hiring that like you have to beat Anthropic, meaning like your use of AI has to be better than AI on its own of solving the same prompt or answering the same question, right? And so I actually like the idea of observing the usage of AI from a pre-employment perspective, just to kind of get a read. I could see an employer asking or an employee or a candidate using their GitHub commits for cloud code or Xcode or whatever it is that codex that they're using to show their adeptness and their ability with using AI.

52:29Ben Mones: But as far as looking at what they're saying to AI? Hell no. No, no, no, no. You've just created a path that I'm going to settle on, a destination I'm going to settle on here, Ben. You're absolutely right. I don't want to know the content, but I want to know the how. So maybe there's a way in which you could anonymize the content of the query, but it would be quite interesting to see how this person interacts. Do they stop at the first answer, for instance? Or have they intelligently queried it? Do they test the hypothesis? you know did it do all that stuff that would be like not not only valuable from a like an absolute bar point of view fluency point of view but i think also as we learn more about how we operate with ai we're going to develop different cultures of working with with ai um and we're looking at cultural compatibility with how this person interacts uh with this version of whatever model it is and maybe it could be model specific you know perhaps i'm a an anthropic house let's say.

53:28So I'm particularly interested in people that use Claude in this way. And suddenly that becomes a very important signal for me because I want smooth integration into team rather than have someone come in with some very different sort of ways of operating, which are totally valid, but different from ours. And actually there's no signal there. I don't see any way in which I can find that information.

53:50Ben Mones: You're probably not far off at all on this. I actually, funny you say that, you know, we, we actually do on our engineering team, monitoring of, of, of, uh, uh, AI usage on cursor, you know, which is the agentic development tool. Right. And so we require it and we see who's using it. We see who's not. It's part of performance reviews now. We want people using it. We need that 10 X factor. Um, on the other side of it, we've done like bi-weekly AI sessions, uh, and we're actually increasing the pace of them to bi-weekly. Now We did them a little bit less frequently in the past. But through that, you can kind of like, we just screen share.

54:30Ben Mones: People just open up their club and I'm like, yeah, well, this is how I do it. This is how I go about it. So I don't know if it's fair yet to be judging candidates or employees on the back and forth, as you said, you know, of using AI and how able you are to rabbit hole into a prompt. but in a year in a year and a half could you see that as part of a pre-employment assessment probably probably you know show me the whole prompt journey that you went on to solve this problem i want to see if you understand like we were saying at the top how to wrangle these things you know because that's what it is right i mean that's that's that's this is like taking a herd of cattle and getting it through a fence you know that's there's definitely something there i'm not these days i don't even know what makes a product but five years ago it would have been a product there can you actually go build that someone on this talk go go build that and send it to us that would be super interesting because increasingly the other signals are going away right cv resume application whatever and what we're heading towards now is that we're assessing for how this person uses ai and it's like it's not a fake test but it's basically you're creating a situation that isn't real, real life.

55:48And how do you actually know how this person behaves in this way? It's the classic coding problem, right? You set a coding problem. It's not how the coding environment really is. So you're not going to get a pure signal. But if you're able to get historical information anonymized, that's safe to use. Then I think that could be quite an exciting thing that people would be very interested in, given the fact that almost every company is hiring no matter what the role they're hiring for AI fluency, right? So, so yeah.

56:14Ben Mones: Yeah, for sure. Okay, Ben, listen, we're coming to the end of this show, but I wanted to leave you with a couple of final questions really on this. Firstly, Famalens, this is the big product launch. Is that right? Can you give us some like sort of fixed points? Like what's happening? You mentioned a couple of weeks. So right now it's in beta usage in some way. When's the general release? It's been in beta for the past couple of months. General release is November 15th. you can go to, I think you even linked it in the chat, but on our website, phama.io slash phamalens, I think you had that link right there.

56:51Ben Mones: You can go there, sign up, get on the list. Existing customers can register for their first policy workshop to get a policy bill. But yeah, sign up there if you're interested in learning more. It's at the very least, even if you're not a customer of our company, you want to learn more. I'd love to show you what this paradigm shift looks like because this stuff is crazy. It's wild what some of this stuff can do. And yeah, we're, we're launching it to the whole world November 15th, but we have, we've been in beta for a few months now with a few customers. Why didn't you live stream a policy workshop with one of your customers, man?

57:22Ben Mones: Great idea. Well, it's some heavy, you know what, you know, I mean, these are like real questions, real conversations, but that's a great idea. I mean, we can, we can do one. We can do one. I think that's a good, folks, is that a good idea? Like, what does it look like? I mean, maybe it could just show you what the flow is, you know, we'll get one of the, you guys in the team there, Ben. We'll just go through it. Let me know in the comments, folks. Is that something interesting or not? Live stream this sort of policy development workshop. And then we get a customer. Yeah, we get a customer. Firstly, customers got to agree, but you know, why would they not?

57:57You know?

57:58Ben Mones: Yes. Yes. Sounds good. I love it. Let's do it. Brilliant stuff. Okay, Ben, let me let you go. Brilliant work. I love chatting with you on all of these things. It's great to get your take on the state of the world. Also, I totally get the enthusiasm. I love the enthusiasm you're seeing at this moment of product development. I don't think there's ever been a more exciting point because so much is available to do. I look forward to seeing more innovation coming out of you guys in the coming months. But yeah, Pharma Lens coming out in November the 15th, correct? Correct. When is the big drop? Is there a time?

58:37Is there something that people need to wait for?

58:38Ben Mones: Well, I put a bunch of videos and stuff on LinkedIn last week. We did a sizzle reel. We got it all. So the announcement happened at HR Tech last Tuesday. And yeah, just check us out. Check me out on LinkedIn. Fama.io. You can see everything there. Google Fama Lens. We'll figure that out. Shout it from your rooftop. Someone will come for you, pick you up in a helicopter, and bring you to Fama Lens. But all jokes aside, man, you're such a legend in our industry, and you really lift all of us up. And challenging conversations, interesting discussion. and I think all of us really benefit from just hearing your perspective and it's always a learning moment for me when I jump on with you.

59:13Ben Mones: So I appreciate you taking the time today. No, much love, Ben. Brilliant stuff. Okay, Ben Moniz of Fama Technologies, thank you so much for your time. Really enjoyed having this chat. Look forward to the big drop in a couple of weeks. Until next time. Thanks, everyone.