pom

POM is a company that started in 2014 with the goal of creating peace of mind in the invoice payment process. They initially targeted consumers with an app but later pivoted to focus on businesses. POM now provides payment tools for invoices, such as QR codes and pay links, making it easier for businesses to receive payments. They have about 2,500 customers in various sectors. POM also uses AI, specifically machine learning, to personalize the distribution and follow-up of invoices. They prioritize communication channels based on customer preferences and history. POM believes that AI should support humans rather than replace them. They stay ahead by keeping their eyes open to new technologies and evaluating opportunities, ensuring that any new releases are proven to work. Anyone interested in learning more about POM can contact them directly. Welcome to another podcast, Fintech Belgium and The Connector together. And today I've got Pom Johannes. Can you please tell me what is POM? Hi Koen, nice to be here and nice to be invited by you and nice to be in this splendid location here of Fintech Belgium. Absolutely. A few years ago, it's like in 2014, we started with the company POM and we as Tom Totte and myself, Tom Totte my co-founder, and we started with POM and POM stands for peace of mind and our big idea at that time was creating peace of mind for people in the whole invoice payment process. And what we did is actually we had a splendid idea. We wanted to make an app that allowed you to receive your invoices, pay them with one click on the moment they had to be paid, store them automatically and then share them automatically with whoever needs them, like your accountant or your wife or your husband or whatever. And although it was a splendid idea and although we worked it out also very nicely, we found out after a few years that convincing a consumer market in downloading your app at the first place and then continue using it, it costs you a lot of effort and especially a lot of money. And what made us make our big pivot and we made a big pivot in 2017. Like many of the startups do? Yeah, yeah. I think if you haven't done a pivot, you're not a startup, I would say. So we did a big pivot as well somewhere in 2017 and we thought, well, instead of going to the consumer and trying to convince the consumer to use our app and then by having a lot of app users also trying to convince the businesses, why don't we go directly to the businesses? And that's what we did. And then we told them, we can help you getting your invoices faster paid. That sounds nice. And that was nice. And it's broken interest and we were lucky to have one of our first customers, one of the biggest energy companies in Belgium and that kicked off all the rest. So yeah, what we did, what we do now is that we add payment tools on invoices and the payment tool can be a QR code, a paper invoice can be a pay link in an email or an SMS or a WhatsApp message and allows you just to pay easier. Which for the company is really nice because they get the money obviously sooner, I guess. Yeah. So they get the money sooner, they get the money always 100% correct. So the message is always correct, the amount is always correct. So there's a 100% automatic reconciliation of all the payments that come in. And they have happier customers because, okay, yeah, who doesn't like to just click and pay instead of having to type over all this boring stuff every month again. And based on that, yeah, we grew to now like we have about 2,500 customers in all kinds of sectors. We work for the government. We work for major energy companies, utility companies, lots of hospitals, lots of schools, lots of sports clubs, lots of small companies as well. So every company working actually towards customers. And so we have B2C companies as customers. Well, Johannes, thank you very much for sharing this story with us. It's a great startup story. Now, in our preparations, we said we also talk about AI. And now I'm wondering, with such a solution, do you really need AI? Well, it's an intelligent solution on itself already. But anyway, good answer, good answer. Maybe to complete the story and then you'll see the AI coming in. Actually, we became part of a group. The group was called at that time Mail2Pay back in December 2022. And Mail2Pay was actually specialized in credit management and the creation and follow-up of invoices, outgoing invoices. And it was a – we said at that time and we continue to say that it was a match made in heaven. And so we have now – we put the joint product in the market since December 22. And actually, it is in that product that we actually use AI. And well, AI that we use actually, well, it was already invented before the word AI was actually trending. And at the time, we still called it machine learning. But what we actually do is also, first of all, we give peace of mind also in the distribution of invoices and the follow-up of invoices. That's why we have this match made in heaven with Mail2Pay group. What we actually do is something that we call machine learning. So we developed a – it's like five years ago, we developed a model that actually will help you or will decide in your place about the best moment and the best channel to connect to your customer in order to get your invoice paid. And I must say, it was at that time, it was very, I would say, forward-looking and innovative. And so we created a model. We trained it with – we had like a million records. We trained it with 700,000 records and then we tested it afterwards with the other 300,000 records. And actually, the model does two things. It will, first of all, based on your age, the region where you live, will define – will try to calculate how big the chance will be that you will respond to a communication via specific – via different channels and on different times. And it will, based on that, will make a prioritization. And based on the group you're in, it will prioritize. For instance, it will say, okay, we haven't paid your invoice. You will get your next reminder via SMS because your group, your region reacts best on that. But that kind of information, we will also match them with the record of that specific person. It's like, we know your history, we've seen – we've sent you a lot of SMSs already previously and you never responded on it. So, although it is best for your group, it is probably still better to send, you know, a letter or an email. And based on that, the system decides when and through which channel the communication is sent. So, actually, you're talking about a very far-fledged personalization. Yeah. So, clearly an opportunity on AI, but I was wondering, what would you see as challenges specifically than in this whole payment sector, if I may? Challenges, I would have to say there's more opportunities. And basic thinking what we have is that AI, well, should not replace humans, should never replace humans, should support humans. And a very nice example, and it just happened to me like yesterday night. I did a transaction, it didn't went through. Today, I wanted to pay with my credit card and it was blocked. But actually, I received an SMS that said, okay, well, we found a suspicious transaction. Please call to this number and you have a code here to, well, to skip the line. And then – and actually, I was received immediately, so I phoned. Immediately I got somebody on the phone and I said, yeah, well, we think that transaction that you tried to do or that was tried to be done yesterday was suspicious. And it blocked my card and the transaction was blocked. But I had a very good conversation with that person and, okay, we could explain everything and his card was re-blocked. So, and I think this is a very big opportunity I see in AI. AI will become better and better in finding suspicious transactions, doing action on that. But it's, in the end, it's finally, it's the person that actually, it's a human person that will interpret and will finally take the decision. Yeah, so I think we're talking more about augmentation of processes then. Absolutely. I think, I definitely think about augmentation and I don't see us in the near and maybe even in the further future not really being replaced by robots or artificial intelligence. I see us being helped a lot by artificial intelligence. And I would think in this sort of way of thinking that we would be helped, augmented. Do you still believe there's ethical concerns in this whole realms of AI then? There is ethical concerns if you don't put the human in the front. And if you think that, for instance, your salesperson or your support person is going to be replaced by an AI because the AI does it faster, then you're wrong. I think AI, as good as it is now and can be and will improve, it can still not replace a human person. But it can absolutely improve a lot of the service you can give. And it can help you getting rid of the boring task, like responding to a mail. It can prepare a mail for you, but you're going to read it and you're going to finally send it through and maybe change your sentence. But it helps you with the boring stuff, eliminating the boring stuff. The second thing, it can absolutely help you enormously. Of course, it can process lots and tons and tons of data and much faster than we have. So it can help you getting the insight you need to offer a better service. But it always comes back to the human, I think. It is a supporting tool. So how does Pumps stay ahead in terms of adopting new technologies and make sure that you're in the forefront of global payment systems? First of all, of course, we keep our eyes open, but you would be not a good company and not leading. Well, your company, if you don't do it, we keep our eyes open. We have set up a team as well that is working around AI and evaluating the opportunities around AI, other than, of course, what we're already doing with machine learning. But what we want to do, we don't necessarily want to be the front runner. We process millions of transactions, even on a monthly basis, and it has to go fluently. And you can't have, you can't accept even 0.1% of errors there, because otherwise that adds up to thousands of errors in a month. So what we do, we do test, we see what's in, of course, we follow up the market. We will only release things of which we are 100% sure that it's going to work. So it almost sounds like an infrastructure. Yeah, absolutely. Again, we have our labs on the one side, but before something comes from the lab into the actual system, it really needs to be 100% proven, yeah. Makes sense. We're almost at the end of our podcast conversation. I was wondering, who should contact you and how should they contact you? I'm eager to talk to everybody who's interested in hearing more about what we're doing. So if you want to have a conversation with me, just send me a message through LinkedIn and I promise you I'll answer. And if you find an interesting topic, we'll certainly are going to sit together. So also philosophy, definitely. Thank you very much for having me here in the podcast. It was a pleasure. Thank you also for the audience for tuning in and stay tuned. More news from the financial industry. Thank you so much. Thank you very much, Koen, for having me here. You're welcome.