I mean, it's, it's difficult, because you've had cost of living crisis, you know, we've had COVID, you've had these sort of huge macroeconomic conditions that have made things really tough. But the thing I always struggle with is that when we have this conversation with businesses, you know, arguably, in many ways, your software's free to them. Because ultimately, it's all about their ability to collect their ability to return. And actually their ability to, you know, bring forward working capital improve their customers position.

And the thing that always blows my mind a little bit is that what part of this doesn't make sense to a business? Because if you're a business who've lent £100 million, right, and you've got customers who are in financial difficulties, it never makes sense to write that customer off? Right? It just doesn't make sense.

So, in as much as I was a salesman who was always supposed to be pushy at all times, what I needed to also understand what was the problem that I was solving for them, right and try to solve that for them.

Now, wearing this new hat for me as a salesperson, I'd already prepared me for what it was going to look like when I moved to a company like Fair Money. And if you look at all three co founders, we actually were all at Fair Money at the time. And we moved out just to go to this. When I got into Fair Money, and then the COVID era just hits, we basically began to see one new thing, which was that the guys who were doing collections for us at the time, were still doing collections, like it was 2018 - where the customer just needed a call for them to remember to go to make the payments.

They did not need you to help them structure payments, you know, create a payment plan; they did not need you to help them with any educational of any sorts. They were not in any mess whatsoever.

We're a platform that connects people with the professional help they need to become debt free. We help people with credit cards, medical bills and student loans. So the user, they'll go on to our platform, we have what's called our Smart Debt Analyzer. It asks the user questions about their types of debt, total debt, what their main goals are, whether it's to lower their monthly payment, lower their interest rate, raise their credit score, and then based off the answers to those questions, we connect them with the relevant company, or companies, that can best assist them with their debt.

There's no one-size-fits-all solution for anyone in debt, everyone's got a different credit score, everyone's got a different income, everyone's got different financial goals. So we want to lay out their options in front of them, you know, the pros and cons of each. Whether it's credit counselling, debt settlement alone, we want to be that educational partner, inform them on all these options, because once again, a lot of people aren't sure the difference between those three, and then ultimately, they can decide for themselves what's best.

As examples, in a number of Asian countries customers will not pick up the phone if the call comes in as an unidentified number - or if they know it's the collection department number. SMS has worked very well in India. In China, for example, a lot of the interactions are via mobile phone applications, like WeChat. SMS is something antiquated, like Facebook, for many young people in Asia, because they've all got mobile phones now.

In Mexico, a personal touch is still required, so if the customer can't pay, in most cases they turn to their family to get them help and so the solutions take a bit longer. In Hong Kong time is money, so if the customer picks up the call the duration is a lot shorter, as is the resolution. And in a place like Australia, mentioning the word 'hardship' means the customer gets preferential treatment due to the customer protections in place. So that usually it comes up early in the call, if the customer is aware, which most of them are.

And therefore, you know, the situation changes and the people handling the call need to move into that mode of understanding the customer situation a lot more.

Yeah, it's definitely shifted away from what can be a very pressurised conversation where you're there, you're speaking to the agent, you're you're under pressure to share those numbers, and maybe sometimes sharing incorrect numbers because of the pressure. Some utility companies, they want evidence of certain parts of the I&E to be shared, like documentation - so our system has that.

So the patterns we're seeing are very interesting, and the journey they go through in order to share that I&E, they revisit their I&E several times before they finally share the I&E with the with the lender, you can see there might be two or three times they're going back and forth to the I&E. They're updating it on our system, doing it in their own time. And then right at the end, maybe after a day, or two or maybe a couple of hours, they then share their I&E so it's a much more fulfilled I&E to then have that conversation with the agents as I said, it's a more mature to conversation. Obviously they share that I&E then with all lenders as part of building that I&E network, which is what we're here to do.

…there had to be controls that were appropriate, but manageable in an environment where you've now got people working from home, and speaking to customers on a daily basis. The peripheral technology around the collection systems needed to be able to distribute into those people's homes... What I found really fascinating is, there had already been a shift to digital, and to using digital interaction points – we’ve seen it a lot in the acquisition space, we've seen a lot in the management space, but, historically, the collection space have been very slow to follow up. But what we're really seeing now is a more significant shift to allowing customers to interact through digital portals, and to set up payment plans, and to be able to access information about their accounts so that they can make an informed decision...

we have spent a lot of time recently looking at the infrastructure between data, applying analytics, and we're applying machine learning through our data-driven decision engine, to then differentiate how treatments are applied in the operational systems. And the real key for me is that ecosystem should be fully integrated. And it should be a continuous loop so that you continually are learning from the outcomes that you get from the actions that you apply to those customers, whichever segment they fall into whatever treatment you apply, understanding what's been successful, what hasn't been successful, introducing champion-challengers wherever you can, to test new options, but also to ensure that the quality of the data that you're pulling in to help with those decisions is at the highest standard…