Background checking is an area we’ve never really covered on the podcast. In the past, it has just felt like a necessary but very functional part of the hiring process. However, with the explosion in our digital footprints and the power of AI, that has now changed.
So, how can background checks now help prevent workplace misconduct and improve the quality of hire? Where does AI fit in, and how can we ensure everything is legal and ethical?
My guest this week is Ben Mones, CEO and Founder at Fama, an online screening technology company working with employers worldwide. If you haven’t looked at what is happening in background screening for a while, this conversation with Ben will help you get up to date with what is possible.
In the interview, we discuss:
• The impact of workplace misconduct
• Talent screening
• How background checks have evolved in the last 10. years
• The “Cambrian explosion” of digital information
• Using AI to unlock the signal from the noise
• The importance of applying human expertise and judgement
• The dangers of hiring managers doing their own checking
• How does AI-driven screening work
• Legislation, Compliance, Ethics and Bias
• Improving the quality of hire
• What does the future look like
Listen to this podcast on Apple Podcasts.
Transcript:
Matt Alder [00:00:00]:
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Matt Alder [00:01:15]:
Hi there, this is Matt Alder. Welcome to episode 588 of the Recruiting Future podcast. Background checking is an area we’ve never really covered much on the podcast in the past. It’s just felt like a necessary but very functional part of the hiring process. However, with the explosion in our digital footprints and the power of AI that has now changed. So how can background checks not only help prevent workplace misconduct, but also improve the quality of hire? Where does AI fit in and how can we ensure that everything is legal and ethical? My guest this week is Ben Mones, CEO and founder at Fama, an online screening technology company working with employers worldwide. If you haven’t looked at what’s happening in background screening for a while, this conversation with Ben will help you get up to date with what is now possible.
Matt Alder [00:02:13]:
Hi Ben and welcome to the podcast.
Ben Mones [00:02:15]:
Thanks for having me. Great to be on.
Matt Alder [00:02:18]:
An absolute pleasure to have you on the show. Please could you introduce yourself and tell us what you do?
Ben Mones [00:02:23]:
But of course. My name is Ben Mones. I’m the CEO and founder of a company called Fama. We’re a talent screening company trying to reimagine what digital identity means and helping companies tap into. Yeah, one of the world’s richest data streams. To answer that big question, how’s this person going to act when they join my company around employees and customers? So yeah, we’re based out of the states, California to be specific, but clients all over the globe, particularly focus in the uk and I am very grateful, Matt, to be here. So thanks a lot for having me.
Matt Alder [00:02:53]:
Oh, as I said, it’s an absolute pleasure. Great stuff. Now you’ve got a really, really interesting approach to this area, which we’ll sort of go into in a bit more.
Matt Alder [00:03:02]:
Detail, but I suppose just to set.
Matt Alder [00:03:03]:
A bit of context about why this is important. Tell us about the impact of why workplace misconduct, of problems in hiring, those kind of things.
Ben Mones [00:03:13]:
Yeah, sure. So, you know, I think workplace misconduct is one of these interesting, you know, things that happens in the workplace that ultimately we are only looking at the sort of outcomes of workplace misconduct. The fines, the lawsuits, the firings, the difficult conversations, right? Those, those late night phone calls we in HR get, you know, about something that happened at a company event or something like that that is now suddenly a big issue for HR to deal with the next morning. So when we think about workplace misconduct, we try to go back to the very beginning, right? How do we begin understanding the root cause or the root signal of workplace misconduct so that we can avoid those sorts of outcomes, right? Because the one thing I encourage folks to think about, you know, maybe leaving this call or leaving this podcast here, is, you know, it’s not always about like doing a cost of doing business. When it comes to workplace misconduct. This stuff is avoidable. In other words, like, we know that when, let’s call it misconduct happens inside of a company, there’s a 1.6x multiplier effect, meaning that misconduct is contagious. In other words, if someone comes in, let’s say they act in a harassing, intolerant, threat, threatening way, maybe they commit some sort of fraud, seems light, maybe something that they can get away with. That, that behavior, especially with leadership, becomes normalized. And that’s something that in an organization, companies begin to sort of fester, if you will, and start to see. Okay, well, one, you know, example, one incident, workplace misconduct leads to 1.6x more misconduct. It’s that sort of normalization. In other words, kind of quantifying that bad apple will spoil the bunch. So generally, you know, I think a lot of us feel this when it comes to the workplace, right? A lot of us know that, hey, if I’m in a meeting and somebody says something that might be a little bit misogynistic, call it, you know, call it, or maybe even intolerant somebody based on what they look like? I’m not really focused on the topic at hand.
Ben Mones [00:05:05]:
Right.
Ben Mones [00:05:05]:
I’m not really engaged in the subject matter. In fact, you know, you actually have real data. This comes from a Cornerstone on Demand and Harvard from a 2015 study. But productivity actually drops by 40% in toxic work environments. And good people are 54% more likely to leave. Good people, meaning high performers, are 54% more likely to leave in that toxic work environment, environment. So again, I think a lot of companies just think, oh, well, it happens. It’s a part of doing business. Well, you know, I’m here to tell you it’s not. And there are real costs and impacts of this sort of thing going unchecked within your business. And yeah, you can quote like the $5 trillion a year that fraud costs us, the employee theft costing US businesses in particular. We have some US data on it, $50 billion a year. Sometimes those numbers just seem so big, it’s hard to kind of wrap your head around and understand, okay, well, am I really part of that 50 billion? The end of the day, I just say to folks, think about your company, you know, think about someone acting in a threatening or harassing way. What does that do to other people who are there? It has a negative impact. Good people want to leave and you’re not focused. So, you know, it’s easy to kind of get down to earth with this stuff.
Matt Alder [00:06:13]:
Absolutely. And when it comes to background checking, it’s something that everyone will be familiar with and use every day, but may not have that thought about it deeply for some time. If indeed ever. Give us a bit of context and tell us how background checking has evolved over the last 20 years or say, sure.
Ben Mones [00:06:32]:
So you know, background screening started really in the 1970s actually with lie detector tests, believe it or not, like before joining companies and some listeners might remember, you would have to get hooked up to a lie detector machine. None of which worked, by the way. Ask questions, you know, about you and your background. Right. And so this, this sort of question of background screening, you saw a dramatic increase in the practice following 9, 11, particularly in the states. And you know, seeing that feed out into the rest of the globe over the same period in the sort of early aughts, if you will. But the industry really around background screening, the evolution, you know, per your question, answer it directly, has really been about going from, call it paper fulfillment, you know, literally paper and faxing, to digital background checks. Going from call it on prem servers in the mid 2000, the 2010s, right. To cloud based servers, or going from offshore labor in 2015 to robotic process automation. Right. So a lot of the evolution of background screening, as I’m sure a lot of folks who again are listening can attest to, things have gotten cheaper, they’ve gotten faster, they’ve moved into the cloud. Hopefully it’s not too many more pieces of paper that folks are dealing with, but at the end of the day it remains sort of focused on the same concept of qualification. I need to make sure this person is qualified. Background screening did they go to the school they said they went to? Did they work at the job? Do they have in the UK the right to work, for example?
Ben Mones [00:08:02]:
Right.
Ben Mones [00:08:02]:
Very basic qualifications about, is this individual fit? What have they not done? To answer the question of what will they. So, you know, the, the background screening evolution. I think only in the past five to ten years or so have we started to see kind of this dramatic rethinking of what background screening could be. You see a lot of identity verification now happening. You see automated reference checking and, you know, solutions like ours which begin to sort of look at a person’s digital web presence to answer that question. You know, how is this person going to act when they join? And I think, you know, background screening is obviously, it’s very nuanced, but it’s part of the broader market segment of talent screening and talent screening tools, which are all really designed to again, answer that question, how’s this person gonna act when they join? And I think you’re starting to see more companies drift towards, okay, well, maybe I don’t need to run a drug test on somebody to make that assessment of how they’re gonna act. Maybe I should focus more on a psychometric assessment or skills based assessments, that kind of thing. Again, answering that question, how are they gonna act around employees and customers less, you know, are they qualified? So I think we’re shaping up for a pretty dramatic rethinking of what background screening is going to look like.
Matt Alder [00:09:13]:
Yeah, no, absolutely. And I suppose, tell us a little bit more about what’s changed, because with the explosion in data and content about people, people publishing their own content all over the, all over the social web, I mean, how’s that changed the dynamic.
Ben Mones [00:09:29]:
To quantify the sort of, I call it the Cambrian explosion of digital information. Right. You know, we’re, I call it data exhaust too. We’re exhausting about 1.7 million megabytes of data per second. I mean, and just think about that, right? Think about the amount of time that you’re spending on a computer each day when you’re not at a computer. How much time are you spending on your phone? How much time are you watching a smart tv? Are you looking if you have young kids at a smart baby monitor, right? What about, you know, the, the camera outside your home? So many of us are living now lives in the digital space so much more than in the analog space. And I think that’s especially true for the millennial and kind of Gen Z generation. And you know, by the way, millennials are going to be the largest segment of the workforce here in 2030, you know, by far. So as more, you know, boomers of age begin to kind of retire and enter into that kind of golden phase of life, if you will. So if you begin thinking about this sort of, like, concept of digital identity, who we are online, potentially becoming even richer, more detailed, filled with, you know, more potential types of insight than who we are offline, so too do hiring managers and recruiting teams begin thinking about how they can leverage that digital data stream when it comes to a hiring process. So company called Resume Builder pointed out that, you know, three in four hiring managers, this is a 2023 study, are using social media to screen candidates. And 55% of those folks use it to assess culture fit. 85% found content online that caused them, you know, not to hire a candidate. So I think we’re just seeing more and more this kind of acceptance of, okay, there is signal that exists out there. And that signal, again, is only unlocked by the power of artificial intelligence to be able to filter through a million tweets in a matter of seconds or read an image without any text associated with it and assess whether or not there’s a firearm or illegal drugs in that image.
Ben Mones [00:11:34]:
Right.
Ben Mones [00:11:34]:
And then, of course, that data, those insights get passed to, you know, the human to the human to the HR leader. Those folks who, you know, are kind of uniquely designed to apply their expertise and judgment to assess the meaning and implications of those insights. Right. So just keep in mind, like, HR leaders are doing this because they see signal and they have that uniquely human trait of being able to apply expertise and judgment to assess the meaning and implications of those insights. So, yeah, it’s a really interesting time we live in, and I think you’re starting to see HR leaders start grabbing for power tools, if you will, when it comes to talent screening and the online web presence is one of them.
Matt Alder [00:12:19]:
Yeah. And I think you’ve really outlined the complexity of all of this in terms of the amount of data out there, what things might mean, you know, how things are, how things are constructed. What are the dangers of hiring managers just doing their own screening, just performing their own data checks just based on what they can see?
Ben Mones [00:12:38]:
Yeah, we see this a lot. Right. And those numbers I just quoted, you know, often, you know, are folks doing it internally.
Ben Mones [00:12:44]:
Right.
Ben Mones [00:12:44]:
And I think one of the big dangers of doing this yourself, I’m sure, are going to be common and something that folks, you know, who are listening in have probably heard from, you know, legal counterparts in their organizations or folks that are Maybe senior to them on their teams. But, you know, if you do this yourself, meaning if I go out and I want to check the Facebook or I want to look up a candidate on Google or on Twitter or something like that and see what’s out there, I might discover protected class information about that person.
Ben Mones [00:13:11]:
Right?
Ben Mones [00:13:11]:
And that could be personal information that under the gdpr, has no, and this is the UK extension too, has no business relevancy or no reason to view, for example, me finding out a person’s gender, age, religion or disability status, even accidentally, even unintentionally, puts me as a user in a very compromising position where I might even unknowingly use that information and impart more bias than I otherwise would have in the decision that I’m making about whether or not to hire this person. So ultimately, doing this yourself, you expose yourself to things that you shouldn’t see, whereas turning to a third party, it essentially blinds you to what you shouldn’t see and allows you to say, hey, let me know if there’s anything out there that this person posted, you know, that’s anti Semitic, that’s hateful, that’s violent, right? Maybe I want to know if, you know, I’m. Call it the nhs. For example. We did, I’ll share a little bit of a case study here, but we did some work with the NHS back during the vaccine rollout of 2020, where they were hiring, as you might remember, 25,000 people in a matter of weeks to give the job, you know, when the vaccine rolled out. And it was a bit of a mess at the time, but, you know, I think Boris got a lot of heat for it. But in any event, you know, they used us to make sure that, you know, they don’t want to look at a person’s disability, their age, their religion, et cetera. But they did need to know, for the people that were given the jab, has anybody acted in a way where they promote anti vaccine conspiracy theories? Right. Do they have some hidden agenda coming in that, should it exist, you know, could be potentially damaging to the health of citizens in the UK. And believe it or not, 250 people out of those 25,000. So again, a fraction, 1%, but 250 out of 25,000 people were posting anti vax conspiracy theories online and didn’t get hired by the NHS because of the tools that we put in place.
Matt Alder [00:15:07]:
You kind of sort of mentioned quite a few things about tools that are available and what AI can do bring that together for us a little bit in terms of how do products like yours actually work and what do they do?
Ben Mones [00:15:20]:
Yeah, yeah, sure. I think the headline on the role of AI in this world is that this is not automated decision making in the world of HR and talent acquisition. Now there are tools that do that, but not what we do here at Fama. Not my personal philosophical view on the role of technology in the workplace. In other words, what AI is really good at is finding and analyzing a lot of information very quickly, meaning compartmentalizing, organizing, structuring, pulling out that kind of needle in the haystack, if you will. That’s really what AI is good at. And what it does is it organizes raw information and tees that information up for a human kind of, like I mentioned before, to come in and apply their expertise and their judgment. And that expertise and judgment is born out of human experience. That human experience that you can’t teach a machine. The edge cases, the, the, the unspoken moments, the body language, right, the reactions, everything that we’ve developed to hone, call it our professional skill set, to sharpen our professional swords, if you will. All of that then comes into play and is substantially, I’ll just say increased in scope. And it puts us in a position where as humans, we can now start making more informed decisions more quickly, but also spending more time doing that kind of assessment of the information, applying our expertise, applying our judgment about the implications of what this data means, what it’s telling us, how it’s going to affect us in the real world, rather than spending the time collecting the information, compartmentalizing it, organizing it, and then teeing it up for, you know, review and analysis. So I would say, you know, the big, big takeaway is that, you know, AI is not doing the decision making in our world, but what AI is doing is putting you in a position to bring you to the precipice of action.
Matt Alder [00:17:20]:
You mentioned compliance with various bits of legislation there, the dangers around this. And also there’s an ethical angle, terms of looking deep into people’s personal lives as well. How do you make sure that you stay on the right side of the line when it comes to, first of all, compliance, but also doing this in an ethical way, sort of a basic.
Ben Mones [00:17:40]:
Answer to the question, which is that there are pre existing frameworks across, you know, a lot of new AI legislation rolling out in Europe, the gdpr, of course, the FCRA in the United States, Pepita in Canada, a wide range of privacy legislation that essentially you got to be compliant with, not just to protect, you know, companies, to protect the service provider, but to protect candidates privacy. Right. So what does that mean in practice? It means that you have to get, you know, consent to run this check. The candidate has to have informed consent, know this sort of thing is happening.
Ben Mones [00:18:13]:
Right.
Ben Mones [00:18:13]:
You can’t look at private information, you can’t go into a person’s private life and start seeing the private messages or if their account is private, to fake it and you know, try to friend them. Get behind some privacy wall also to, you know, again, remove some of these protected classes from insight, you know, from the insights we provide and only, you know, highlight what customers deems potentially adverse or relevant to knowing about for that job at hand. And then you know, again, building the artificial intelligence in a way and the technology in a way that is as least biased as possible and reflected in the people who are building it.
Ben Mones [00:18:50]:
Right.
Ben Mones [00:18:50]:
So you know, at Fama we do a lot on ethical AI, but our approach is pretty simple, is that we want the people who are building the technology to reflect the communities and customers that we serve. So whether it’s our leadership team with this, which is 50% male and female, the organization which 50% identifies as non white, you know, our entire company again, 50, 50 on the sort of, you know, gender split, if you will. So again it’s a bunch of factors that go into, you know, developing a compliant and ethical solution. And one is just stay compliant within the guardrails of how a series of legislations from across the globe all kind of coalesce to become the single privacy framework. But also building technology in a way that allows us to feel confident that we’re not putting anything out there that’s going to induce bias.
Matt Alder [00:19:41]:
Lots of talent acquisition professionals will be listening. Tell us how this approach can influence the quality of hire.
Ben Mones [00:19:50]:
For us, quality of hire really means again answering this big question. How’s this person going to act? How do I limit my downside? When they join the company, how are they going to act around fellow employees, fellow customers? Right. So quality of hire, in other words, is like finding people with the right skills and right fit, you know, for the role. And just because somebody can do a job, meaning just because they’re qualified, doesn’t mean that they have that kind of fit that they’re going to represent the values of your organization internally and champion the culture that they’re going to, you know, extend the values of your brand in the eyes of your customer. So you know, really for us that means being able to look at patterns of behavior previously to joining the company where you get that insight again, not just from Social media, but also from a person’s, you know, complete digital identity.
Ben Mones [00:20:34]:
Right?
Ben Mones [00:20:34]:
So, you know, we’ve seen everything, if you consider this, like, proxy of how we act online is going to have an impact of how we act off of it. You know, we’ve seen everything, you know, from doctors selling body parts online, you know, people applying to camp counselor jobs who have, call it sex offender backgrounds, you know, around children. We have people who have posted about threatening to hurt fellow coworkers or employees, executives who’ve created boys club cultures. Maybe they weren’t named in, you know, a lawsuit, but there are articles or blogs out there about them that have been substantiated. Right? And so really, again, it’s about generating that signal to answer that basic question where so many of us are already doing this today, we’re already seeking to answer this question of how can I limit my downside? How can I ask the right questions in the interview process? How can I ask the right questions? Am I referencing workflow? How do I go through all of my validation steps to make sure that I’m limiting my downside? Now what we do with online web presence is, you know, injecting that into the overall equation.
Matt Alder [00:21:42]:
So as a final question to you, where does this go next? What does the future look like?
Ben Mones [00:21:46]:
I mean, I think you’re going to see a continued sort of adoption of companies turning to the online record.
Ben Mones [00:21:53]:
Right.
Ben Mones [00:21:53]:
And I don’t think it’s. It’s just for risk management either. I don’t think you’re just going to see folks saying, oh, I need to manage risk, manage my downside. Artificial intelligence every day, especially generative AI, is unlocking new capabilities at a scale that, you know, frankly, Matt, we just haven’t. Haven’t seen before. So what that means is that we can now begin looking at the language that people are creating online, the words that people are writing, the videos that they’re appearing in. Right. And actually begin running psychometric and professional competency analysis on that language. In the same way that folks might fill out a Gallup strengths finder assessment or answer templated questions as part of an overall talent screening assessment, we can begin looking at that language that people are posting online and begin, you know, generating actual conclusions about the professional competencies and psychometric network, psychometric cap, excuse me, intricacies of that person based simply on the words they’re creating online. So there’s so much power in unpacking the meaning within text, you know, especially text that’s created by a human in a primary format online that there’s a lot we can do with it from assessing quality of hiring, even things like talent acquisition. So that’s probably another podcast.
Matt Alder [00:23:10]:
More to More, I’ve got so many questions about that. I think it’s going to have to be Ben, thank you very much for joining me.
Ben Mones [00:23:16]:
My pleasure, Matt. Thank you so much for having me and really appreciate the time.
Matt Alder [00:23:21]:
My thanks to Ben. You can subscribe to this podcast in Apple Podcasts on Spotify or via your podcasting app of choice. Please also follow the show on Instagram. You can find us by searching for Recruiting Future. You can search all the past episodes@recruitingfuture.com on that site. You can also subscribe to our monthly newsletter Recruiting Future Feast and get the inside track about everything that’s coming up on the show. Thanks very much for listening. I’ll be back next time and I hope you’ll join me.
