Interviewed By The Asian Banker Live
Loubna Bazine, CEO of alternative credit score finetch, Friendly Score, explains its business model and growth plan to Emmanuel Daniel, chairman of the Asian Banker
Here is the transcript:
Emmanuel Daniel (ED): I'm very pleased to speak with Loubna Bazine, the CEO of Friendly Score. Tell us a little bit about the Friendly Score proposition and how that's different from traditional credit score and – and – and, you know, who you're working with.
Loubna Bazine (LB) : Okay, we are an alternative credit scoring solution and we are looking at alternative data in order to assess credit worthiness. So we are different than traditional credit scoring in a way that we are pulling out the big data and digital footprint of individuals in order to assess credit worthiness. So we are looking at things like social media, like we are looking at cell phone data, mobile data, and very big area of data in order to decide about the score of individuals.
ED: Where is that technology right now in terms of the use of big data and social data, right? Social media data. The – the early iterations of the peer-to-peer players said that – was saying that the social media data was not really usable, you know, and that it gave a limited time specific profile that could be used for credit, but where is it now?
LB: So I would agree with that actually because that's why I said it's big data and digital footprint. Actually the social media aspect in our algorithm is getting lower and lower, and what we are looking at, we are looking at more at the data that – we look at your financial health, so we are looking at transaction data that we can pull, for example, like, through your email or, like, through your – your SMSs or looking at – really like, a lot of sources that will tell us about your – your health – like financial health, this is what is important for us rather than the behaviour of data that we can pull out of social media, so actually social media for us as of now is less than 30%.
ED: Okay. So this is a permission based...
LB: It's all consent based. It's all consent based. The individual has to really select one by one what he has to give us and he has to include his user name, passwords for everything. We are all GDPR so we are not doing any reseller of data, or we are not taking any data which is not given permission by the end user. And we are also – like, FCA licensed, so this is the financial conduct authority here in the UK.
ED: Okay. How many clients have you got right now, and who are – who do they tend to be?
LB: So we are – we have – actually we have now a global presence. We have about 30 – we are in about 30 countries and we have about 70 clients worldwide. And our biggest region obviously is in Asia, and also like, in – in Africa and Latin America, so it's really where the end bank and the end bank and the corporation is –
LB: – is a
ED: If – if you say Asia and Africa are – you're actually dealing with unbanked population, and also where the personal profiles are, in the first place, not even verifiable, so – so you actually have to create data almost from scratch.
LB: Yes. Most of the time because there is no data at all, so this is
LB: – we work.
LB: So when there is no – no real data this is where we come in, yeah.
ED: Right. So, and when you build that data, do you then – or you don't – you actually do that for client specific needs, so it's like, are your clients generally financial institutions, are they also like , or, you know...
LB: So it's a financial institutions, auto makers, it could be like HR department. It could be like landlords. It's really, like, any business that needs to do a credit check.
ED: Okay. Give us an example of emerging country where, you know, you've scaled very quickly and –
LB: India, it's like –
ED: So do you work with a bank or with a finance company, or...
LB: We – we – we work with financial institutions and mostly we are working with motor makers.
ED: Okay, got it.
LB: Yeah, there's two – two major motor makers in India.
ED: And is – is this alternative to credit score? Do – do you find yourself in competition or up against, you know, traditional...
LB: We are not in competition, we are a complimentary to the traditional credit score because where Friendly Score comes in, it comes in when there is no traditional data. This is where we come in, so we are not competing with them, we are complimentary with them. We are actually working with them.
ED: It's – it's that – is your –
LB: It's a top off.
LB: It's a top off to the traditional credit score.
ED: Right. And in terms of data points, you know, like. the clients who use you, do they choose the number of data points that they want, or...
LB: They are choosing – like, the client – you mean the clients as the bank –
ED: The bank, financial institutions.
LB: – the financial institutions, yes, we are deciding also this with them, yes.
ED: Now, in – in that regard, you know, what about countries like China? Is this a model that might work well – well there?
LB: So in – in China, like, they have their own model. Like, they have this for example, is it – it is like –
LB: – like a credit scoring, which is –
LB: – like very well developed in China. For us, like, Friendly Score, we would look at – also like China, but we are not there yet.
ED: Yeah, but I know, very interesting you mentioned credit scores to me because there's also a game element in there which is the more transactions you do the more, you know, they're – they're able to profile you better. Do you get into that kind of a mode where – where you actually work with your partner to – to use it as a marketing platform in addition to just a risk profile?
LB: No. For now we are just using it for risk profile and, as I said before, we are all GDPR and FCA, the data that we are taking, it's really just for, like, credit scoring, so it's really for one purpose.
LB: One area.
ED: So – so your largest client, what's your, you know, population base and your smallest client, what's your population base, what's...
LB: Let's say the largest client will be like a client that has more than one million score per – per month, and it goes to a client that does less than 50 score per month.
ED: Okay, so –
ED: – you're able to –
LB: It's – it's –
ED: – support a small –
ED: – player?
LB: Also, yes, of course, yeah.
ED: Okay. And is this a software as a service model, or...
LB: It's a software as a service, yes.
ED: Okay, good.
LB: It's just us, yes.
ED: Yeah, now, where do you think this is going in terms of the use of such data? Is it becoming more and more definitive? If you take mobile data and also supply chain, right, like if a – if a – if a customer has got a stable relationship with a – you know, financial institution, you know, do you find that you're able to make more substantive decisions – like mortgage decisions for example, or automobile loan decisions.
LB: Well, it is very important because the – the Friendly Score does allow to integrate data that is not usually available to banks and financial institutions and the – the – the – the credit profilers, so we bring in another data, which is now becoming more and more torrent, like the digital footprint is really like getting – the – the internet penetration and also the kind of information through your digit footprint that we can get is really like, it's – it's huge, and it's very important and as a financial institution it is all beneficial for them to include this data also on the top of their data, so it's just like our positive program, yeah.
ED: Thank you, very much.
LB: Thank you very much for your time, thank you.