Adam Parks (00:07)
Hello everybody, Adam Parks here with another episode of receivables podcast today very excited to have Ohad joining me, the founder of TrueAccord who has been in the space and was one of those first movers when it came to leveraging RPA and all different types of technology. So really excited to finally have the opportunity to chat with you today.
Ohad (00:30)
Thanks for having me. Excited to do this.
Adam Parks (00:32)
Absolutely. For anyone who doesn't know your backstory, could you tell everyone a little about yourself and how you get to the seat that you're in today?
Ohad (00:39)
Yeah. The pre-TrueML to Accord, I've been in tech for 20 years now, a little bit more than 20 years, always in machine learning for financial services. Started at a company called Fraud Sciences. We did fraud prevention for e-commerce with machine learning back in 2005, 2008. We got acquired by PayPal basically to become part of their risk organization. Spent a few years there, moved to the Bay Area through there.
And then I left and started, actually started working on two companies that both became companies. One is called Signified, still around, kind of a Fraud Sciences 2.0, part of that generation, doing very well, thanks to my founders there. And another called Analyzd, they did consumer credit underwriting. And Analyzd got acquired by Klarna, where I was a Chief Risk Officer.
And at Klarna, underwriting a few billions of dollars per year at the time. I learned about the importance of servicing and importance of collections and how that area specifically did not get a lot of attention from technologists. I actually got some coaching about collections and a very senior person from a bank told me every quarter, you hire people, you fire the bottom performing agents, the 15 % bottom performing.
And it's an art. You don't exactly know why people collect, why people don't collect. And it kind of, I like to say, tingled my, my spidey sense. It felt like, well, you know, I know that, I know that we can replace a lot of human judgment in these areas with machine learning if we do it the right way. And so we kind of manage two, one, two, two, two, three issues. And we bring the most complicated stuff to humans where they can actually have this judgment.
And that's what we did. We built an engine, patented engine called Heartbeat that replaces the human to human interaction in collections to human to machine interaction. we'll talk a little bit of technology underlying it and so on. But the point is that we started selling it first and foremost as a
fully automated machine learning native collection agency, which is TrueAccord competing with call center based solutions. And over the years have grown, have proven the model, have proven the model at scale. We're able to be part of the process to change regulation F, to bring regulation F, change the FTCPA and bring digital to the industry at large. And down to today with a few steps on the way that we can expand on.
Adam Parks (03:20)
Okay, and so talk to me a little bit about TrueAccord, TrueML and what that journey has been like you were one of the first movers to start leveraging this technology in the space and you've kind of built the organization around it versus bolting it on to say traditional collections What's that journey been like as an organization?
Ohad (03:40)
Wow. So complicated question in that I can answer it in many ways. It's been fascinating. know, first and foremost, why did we start TrueAccord versus a technology vendor? Because when we were raising money for the first time, we're a venture funded company. We raised almost $150 million. Investors throughout our life always said, just build a SaaS solution. Just sell to collection agencies, just sell technology. And I told them, guys, we are educating a market.
This is a market where people tell me I would get phone calls. People telling me that what we do is illegal. What we do is, we'll fail. Frankly, not in a mean way. Nobody was mean against me. It was just a deeply entrenched belief. And so we needed to prove that this thing works. And the way you prove that this thing works is you create an incumbent and then you show that it works. You don't go and you try and sell and convince people that actually the technology is going to work. So that's where we started with TrueAccord.
Now, as we scaled and as we've proven the model and grown to our size, step one was lenders coming to us and saying, Hey, we're using TrueAccord for third party collections post charge off. Can we use the same technology white label in our own operations for the length of debt? Uh, and that was a turning point, a major turning point for us where we said, yes, absolutely. We're big enough so that we can say, Hey, trust us this technology is going to work.
Trust us that it is not just about penetration. It's not about just emailing people every day. It is about the personalization. It is about giving people what they need at the time that they need through the challenge they need. So we could speak with authority based on data that now when we sell technology, people would actually listen. Same thing now, having acquired ERC in 22, having acquired Sentry Credit last year, that we are expanding into additional areas under the TrueML umbrella.
Because again, if we can speak with authority about, actually this is how you negotiate with debt settlement agencies at scale. This is how you actually scale it in an automated fashion. This is how you do credit reporting in a way that consumers react to and that auditors and regulators don't react to in a negative way. Interesting. And now we're starting to expand into legal collections as well.
Part of it is that lenders and creditors want one vendor to do everything if they can. The other thing is exactly what I said. As we have demonstrated that things work, we can speak with authority and say, hey, by the way, here's a new area where our method and our technology can be applied. How about you try that? And it's been incredibly successful, but we need to build into it.
Adam Parks (06:17)
Well, that decision engine I would think that you're using to drive the consumer down the right channel at the right time with the right path would be really similar to the styling of technology necessary to drive that litigation side of the business as well. I mean, I realize that the processes are quite different, but that decision engine that's pointing each account down the right path at the right time, I would think is a big competitive advantage.
Ohad (06:45)
You're absolutely right. And you've been always on the forefront of technology. But if we look at the industry in general, when we started, we had to deal with, you send emails, we send emails also, you know, or you guys are, you guys are the nice solution or like a misunderstanding or just not being, not being very open to what machine learning is and what technology can do for them.
I think the tune has changed in the last two to three years, incredibly gratifying, amazing to see it, great to kind of feel the camaraderie around technology and what we can do. But it has not been for the first seven, eight years of the life of the company. It was not very clear that the industry at large was agreeing, was adopting it. Now it's accelerating and that's incredibly good. And we can ride that wave and continue to expand our services.
Adam Parks (07:29)
Well, early on, think a lot of organizations were so concerned with laying down the train tracks of technology that they weren't really deeply considering how they were using it. that, for example, email, with a focus on email deliverability, create a competitive advantage with a focus on sending an email, you're commoditized, you're doing the same things as everybody else.
But that decision engine that allows you to kind of control where those accounts are going and then understanding the deliverability of those emails and those types of things, I think is one of those first mover differentiators that kind of puts you guys on the path that you're on now.
Ohad (08:15)
100% and also doing it at scale. You know, we're pushing 100 million content items a month. I can tell you, I have all the scars to show, know, discovering on a Friday night that Gmail had turned us off and we were not reaching any Gmail consumers. I'm talking, you know, 12 years ago, but still I remember it because we had, yeah, we had to learn all of these things and we had to build a very complex routing algorithm to be able to hit deliverability which sounds kind of obvious, but it's not so yeah absolutely each one of these things is not only needs to be adjusted to collections and how we do things but also needs to be adjusted again and again when you hit scale and things break when at times in any ways that you did not you could not anticipate.
Adam Parks (08:46)
Such a true statement. And when you start hitting that scale and you have to be able to ingest the information or results in order to improve creates this cyclone of data that requires constant refinement. There's no end to that ever. I don't know that there's ever a final state.
Ohad (09:18)
Yeah, one of our proudest moments, and I agree with you completely, one of our proudest moments was from a technology perspective, was when we had acquired ERC. And then by the end of that year was the time to flip the switch and move everyone from the ERC entity into the TrueAccord entity. And over three weeks, we moved between six and eight million accounts into our system.
When I say moved, I mean moved and started servicing and the system didn't break. That was big. That was something that, you know, we had experienced cases where we moved stuff and things did break. And to experience that and experience that scale and not breaking, really being able to dig deeper into the file, reach consumers more effectively, kind of surprise the clients in the short, on a short time frame. That was pretty cool.
Adam Parks (10:15)
There's a lot of elasticity to an organization that can ingest that many accounts without the system overloading, especially when you consider the algorithms behind email deliverability and some of those decision engines.
And I keep going back to the decision engines because that seems to, well, when I did a session with Naama Bloom recently and we were talking a lot about skills and decisioning, which I thought was quite interesting, but it sounds like, as a first mover, you've been looking at these things for a while and looking at the not just the the rails in which things are happening, but that ability to control what's happening and when you can throttle those emails, when you can throttle those text messages and customize or personalize those communications, that seems to be the path, which leads me to the topic that we kind of set off to talk here about tonight, but I was really interested in learning more about some of the things in that, the TrueML, TrueAccord story.
Talk to me about the heuristic revolution and what we're seeing right now with consumers becoming more open to technology, they're becoming more open to subscription plans, like the consumer has changed significantly since you initially started the company. How have you been able to keep your finger on the pulse of that changing consumer in order to continue meeting them where they want to be?
Ohad (11:36)
That's an excellent question. You know, when I started as a founder, I like to say we sometimes have tense confusion. We talk about things in the future as if they exist right now. And I used to talk about choose your own adventure. I'm in my late 40s, so I still remember the books where you had to choose to do this, go to this page, to do that, go to that page, the experience that we wanted for consumers.
Our assumption was not that just be nice to people and they'll pay. And of course not, hey, be start with people at scale with technology so you reach more people and they'll pay. No, it was about giving the right person the right treatment at the right time. And furthermore, it was not about looking at Joe Smith and saying he owes a firm $500 and this is his score and this is going to be his treatment plan. No, it's about with every interaction deciding what is the next step.
This is why we call the system Heartbeat. It beats, it looks at every consumer, it looks at the history of the consumer, it looks at in a multidimensional space, who are the consumers that are similar to this consumer? What has worked for them in the past? what channel, what communication, what approach, what payment offer and so on. And through a combination of these behavioral cues, through a combination of the historical data that we have, it decides what the next step should be.
Now we can inject heuristics into the process. We can teach the system, this is what we call feature engineering. We can teach the system to look at, actually look at when their payday is, look at how many communications they've had with us, look at what is the sentiment of their text when they they reply to us and so on. This is an important distinction of a system like ours from AI systems that are black box, but you don't even know what it's doing. We teach the system what to look at, much like you would teach a very sophisticated agent.
And then it takes all of the data that we have from all of the history of communication with tens of millions of consumers and decides how to communicate with a consumer. And the thing is that the scaling with data is almost infinite. What do I mean with that? As you get more data into your system, there's almost, we have not found in a lot of frontier labs have not found an end to how much better the model can become if you show it more cases, because it still learns differently than the human.
It can look at everything, every case, all cases at the same time, all historical data. And the more cases you have, the more edge cases it looks at, the more irregularities it can learn from. And that adds to our ability, to the system's ability to treat everyone exactly the way they need to be treated.
And on top of that, another topic that's been hotly debated lately, reinforcement learner. That's a system where the system itself sees what the result was of its attempts. And it can say, you know, I'm kind of simplifying, but you know, it can say, Hey, this worked. Let's do this more. This did not work. Let's do this less.
And we can actually see when we look at, for example, we started a new client, we look at cohort one, cohort two, cohort three, cohort four, we start seeing the curve of repayments sloping up over time as the system learns that specific, correct, it learns from the vantage is like, let's bring up the things that work for this type of consumer earlier into the cycle while they are extremely engaged. And that really changes kind of what the vantage looks like.
So that has been, I think something I have not seen that implemented across many solutions at scale. I'm sure there are, but have not seen many.
Adam Parks (15:33)
It's not easy to do that at scale. That's a that's a big lift to have the system continually learning from its own actions. And what does that start to look like? Because you're still keeping that human in the loop and you're still looking at the larger picture to make sure that you're staying on track. But I think it's interesting.
Are there any specific signals that you found that are more intuitive of a consumer who's going to have a higher repayment? Like are there any specific data signals that you've started looking at and found that these are more important than others?
Ohad (16:09)
So the interesting thing is that I would say two things. First and foremost, we don't use demographic data. We use it to a very limited extent. And early on, we made a decision to not use credit score data. Now we use it in some areas, but we decided not to because it was expensive and because we wanted our inference to behave differently than the rest of the industry. Because at the time, most of the industry was using credit scores.
Um, and so number one is that a lot of our signals are behavioral based. So it's not about determining in advance who is going to engage with us more. It is about because the marginal cost of sending an email is close to zero. Right. It is about engaging with them and then seeing how they react. Did they open the email? Did they open it a few times? Did they open it to different times from different devices?
Did they click through the link on the email or the text? Did they browse on the website? Did they play with the widgets? All these things add back into the system and the system on its own decides how to optimize basically. And this is a very important thing. We didn't need to teach the system which indicators are better. We just need to teach it where to look and then it optimizes on its own.
So that's one very important piece that's very hard to do when you don't have the scale of data that we have. The other element here is that counter-intuitively, I think for many of us in the collections industry, TrueAccord is a brand. It is a recognized brand by consumers that they can research online, they can see recommendations, they can see kind of how people interact with us and so on. As a result, there's a higher level of trust when we contact consumers.
And that means that we can ask consumers to tell us how they're doing and they will tell us. They will actually react. And so, for example, we don't need to guess when their payday is, we can make approximations of when they get paid or we can just ask them. So when we set up a payment plan, you can ask them, when do you get paid? And then we can tailor the recurring payments and the retries if something fails and so on around their payment dates.
And so that increases success rates of payment plans. On the contrast, if we get, we have more data about their cashflow and how much work they've done and so on, we can detect whether they have more money in their account or whether they're in a position to make a larger payment. And then we can trigger that and we can encourage them to pay in advance for their plan. So as a result, we see double digits reduction in plan breakage because it's about adjusting to what the consumer does.
Adam Parks (18:53)
Interesting. The behavioral signal aspect of it makes a lot of sense because it is hard to predict but based on the actions and behaviors that they take, it's probably a little easier to categorize and optimize that that journey. As you've looked at these behaviors and you've watched the behaviors change over 14 years and consumer behavior has dramatically changed in the last 14 years.
What do you think the next five years starts to look like from a consumer engagement perspective. Do you think we see new channels? Do we see deeper channels like RCS and text? Any any insights into what the next five years holds?
Ohad (19:35)
So I think there's, I want to separate the technology that is used for delivery of communications from consumer behavior. Regarding technology, I'm not 100 % sure. It could be RCS, that is definitely where the industry is going. We may find, you know, some day the consumers adopt WhatsApp, I don't know, not likely in the US, but you know, channels aside, I think the question is, What are consumers used to?
When we started, consumers were extremely, extremely suspicious of collection communication delivered by email or by text. Now they're used to it. When we started, consumers were, they didn't know if they were talking to a human or a bot and they cared about that very much. Now they don't care. They actually communicate with a bot in a way that's equivalent to how they communicate with a human.
I think if anything, we are going to see a lot more communication that's aided by LLMs and maybe a lot more structured. I think all of us see that in the pro se plaintiff wave that's clogging up the courts. I think, from a consumer perspective, I think it's beautiful that people who are unsure what to say and when can use a tool that kind of gets them going.
On the flip side, it kind of feels like the, you know, the last place where people still believe in magic. They think that if they say the right words in the right order, then things are going to change in the world. And that introduces interesting challenges in terms of how you communicate to people and them thinking that they said a certain word and you have to do it just in a different way and so on.
So I think there's going to be weirdly more structure to our communication with consumers in the baseline because they're going to use LLMs more. And on our end, we've implemented with tremendous success, LLMs and RPA into our bank office to allow us to respond to consumer concerns that were historically unstructured in a more structured way. So you can imagine how on our side, we use agents, on their side, they use agents. At what point do the agents speak to one another without humans at all.
That is something that we're thinking about and that we're fascinated by and kind of we're running some experiments around and kind of we'll see where that takes us.
Adam Parks (21:54)
I'm very interested to see where that starts to pan out because I know the debt settlement companies are already working on bots. I'm sure that the consumer attorneys are going to be working on bots with the intent within which is a totally new attack vector for them. They're going to love that opportunity. And then you've got the ones that will be operating for consumers. And what does that mean in terms of third party disclosure?
I think there's a really interesting dynamic in that bot-to-bot situation. And I think being able to identify on our side whether or not we're talking to a bot is going to be important. My understanding to this point is that most of those incoming bots have trouble validating and authenticating as the person and they get knocked out from that perspective.
But just like our technology continues to get better, I expect their technology will continue to get better and hopefully the bot war, you know, won't start too soon, but I feel like it's something that's already started and it's just gonna ramp up over the coming, let's call 12 to 18 months. We'll expect to see more of that.
Ohad (22:55)
Yes, I will just say in addition to that, hopefully we will, we're always going to have the confrontational aspect of lawfare and kind of contextual warfare. In addition, we can hope that there's going to be a segment, maybe a growing segment is going to basically say, hey, look, just talk to my agents.
Here's my permissions. Here's what the agent knows, talk to my agent, figure out what needs to be figured out. And we think that we don't know if this is an imminent thing. It seems a little bit, a little bit further out. But we want to believe that there's a world where we basically say via API, via NCP, whatever, you know, whatever the agents can, can understand. can say, Hey, consumer, this is what's available to you. Let's let, let's let the agents talk. You don't need to, don't stress about it.
We'll figure it out with your agent. Maybe there's a world like that, and we think it's going to be a pretty cool world. And again, if you can do that at scale, if you have the data to understand what to say to whom, I think you'll be very successful in that world.
Adam Parks (24:02)
I think it dramatically changes the debt settlement engagements.
Ohad (24:06)
Absolutely. We're already seeing a lot more structure in our communication with that side. This is why we can structure and kind of automate those conversations at scale. Yeah, we will see where that leads us.
Adam Parks (24:16)
I think it'll be interesting. Again, I know that they're working on the bots as well, and I think everybody's trying to come to their piece of the puzzle. I'm curious to see how it evolves over the coming years. Look, this has been a great conversation. I'm glad we finally got an opportunity to connect. Is there anything that you wanted to cover today that we haven't covered yet?
Ohad (24:35)
No, thank you for having me. I mean, again, I am so happy that we're talking about technology in the debt collection industry. I think that's been long overdue. I encourage everyone who wants to geek out about technology to reach out. I'm always happy to have these conversations. I think that more than anything, an exchange of ideas, especially the ones that are several years in the future, doesn't hurt anyone. Just helps us all understand the world that we're operating in and what we're seeing.
And I'm excited for what the future brings. I think that the industry, is a slow moving industry because of compliance reasons, because of regulatory reasons. but it's, finally where we wanted it to be. And it feels like the change is accelerating. And I think that really everyone's positively excited about it, myself included. So very cool to talk about.
Adam Parks (25:23)
Well, you've built a great team and I think that helps to develop some great technology. really a big fan of just so many people in your organization. So I was very excited for an opportunity to have a chat with you today, Ohad, and thank you so much for joining me. For those of you that are watching, if you have additional questions you'd to ask Ohad or myself, you can leave those on LinkedIn and YouTube and we'll get responses out to those.
Or if you have additional topics you'd like to see us discuss, you can leave those in the comments below as well. And hopefully I can get Ohad back here at least one more time to help me continue to create great content for a great industry. But until next time Ohad, thank you so much for your time. I really do appreciate it. And thank you everybody for watching. We appreciate your time and attention. We'll see y'all again soon. Bye everyone.
Ohad (26:06)
Cheers. Thank you.
