Marquee investors like Warren Buffet, Jack Ma, Masayoshi Son are aggressively investing in FinTech ecosystem and fintech startup.

And the reason it has happened is just one: 10 years ago, the dream of every person after completing the Master's in finance, was to get a job and private equity or hedge fund or investment banking. Now, it is not the case, every person who graduates or comes out of masters wants to get into startup ecosystem, or want to start a new startup.

Custom Credit was really set up with three mission statements in mind - we always are asking ourselves, does this move us closer to this or further away - and that is (1) to become the most customer centric FinTech in the UK, (2) is to ensure our colleagues better reflect our customers, and (3) to improve financial literacy, both in terms of our customers and the broader community.

I think the product itself is tailored and custom. But to achieve that, the way you score and assess risk needs to be tailored and custom, that's often where the problem is with these other kind of flexible payment lenders, most lenders will make a decision on on affordability based purely on averages to estimate your your expenditure. They will use a combination of the information you've provided to them, and an indicator from the credit reference agencies.

People with very different spending profiles and very different income profiles effectively could be judged to having the same level of affordability. So we've actually taken a different approach in that, initially, all of our customers will have to provide open banking data.

If I'm looking like 10 or 20 years back, when we were basically as a FinTech organisation the majority of our decisions were relying on the application form. So credit application form. And nowadays, it's leaning to the direction where you ask very few questions from customers. So from a UX point of view, from a customer experience point of view, it's much smoother.

And it can be super automatic - meaning by that you're able to work with a very lean organisation to support 10s of 1000s of customers. It all comes down to what kind of data sources you're able to use. So coming back to this credit application, you might have had 50 or 20 questions on a credit application 10 years ago, today, it might be that you are asking just for identification, which in countries like Finland, Sweden, Estonia, it's automatic. So basically, you're using bank IDs to do the automization.

And once the automizationis is done, then you're able to start calling different data banks, credit bureaus, all kinds of third party data banks, bank transaction history that you're also able to get through the same identification method, although it's a different call. But anyway, you are able to get so much information to support your decision making and you're able to automate the size from the beginning until the end, meaning by that in a previous life, when we were making the risk assessment, we were using application scorecards.

High level, I would say there are four elements of the lending process, and we started with one: which was how risky is it to lend Brendan a certain money? Like what's the likelihood of repayment or default, and then really allowing our lenders to specify, are they comfortable with that? How do they want to price it?

Increasingly, I think to the point you're making, we are also applying it to the second area we really focused on, how do I reduce friction in the process, right? We didn't start with this insight. This is kind of one of those you learn in the market. When we started, every borrower that our lenders were onboarding, we did a phone call, we asked for an ID to be uploaded to verify identity, that standard kind of KYC stuff that you do. And we had this insight, like, for the small loans, it costs too much money to get on the phone with people, maybe we could just use automated signals to do fraud prevention and not get on the phone, just for small loans, just for a few, to see what happens.

And so we tried it. And we saw this 2x to 3x increase in pull through and actually equal or positive credit performance. We went, 'oh, that's interesting, if I can take a certain amount of demand and turn it into twice as many loans, that's really valuable'. So we started the process of saying, can we use machine learning to get to a place where we're comfortable with more loans of larger sizes of longer durations that we can approve without that human intervention, because it both lowers the cost, but it reduces the friction.

And it turns out, consumers are not only rate sensitive on the loan side, they're also friction sensitive, they don't like putting in a lot of effort. So we are now at a place where our lenders see 70% of loans coming through the platform, having no touch origination - with ID verification, income verification done in automated ways, with very high NPS and very low cost and high conversions as a result of that.