What is FriendlyScore about?
FriendlyScore is about allowing borrowers to use their online footprint as a way of increasing the amount of information a lender has about them. This can be useful for borrowers who lack credit history to get access to products they deserve, and also for allowing borrowers with some history to get better products by making lenders more comfortable with their risk profile.
How can your company help p2p lending marketplaces? Can you please share some references?
We help lending marketplaces make better credit decisions by enabling them to get way more data on their customers. By incorporating FriendlyScore into the platform’s decision engine, we can help prevent outright fraud; validate user identity and personal information; and most importantly, gain propensity insights from the users behaviour. This allows the platform to approve more borrowers (or lenders), reject more fraudsters and bad borrowers, as well as price their risk more accurately.
A borrower your software identifies as creditworthy has been previously ruled out by the scoring mechanism of the marketplaces. How would the marketplace deal with this loan when showing a credit grade/score class for this loan to investors? Assign a new class?
Our most common use case in the p2p space is for FriendlyScore to be offered as an optional way for borrowers to bump up to a higher internal credit rating if they get a high FriendlyScore. In other words, it is an opportunity for borderline declines to get bumped up to higher-riskaccepted, and for the accepted applications to get a better risk grade and hence a lower interest rate from the lending community. This allows the marketplace to increase approvals and hence conversion and also to more competitively price good borrowers. As our algorithm develops, we expect to be able to function as a standalone credit score.
How do you price your service for p2p lending marketplaces?
Our standard pricing is on our website at https://friendlyscore.com/page/pricing. We charge a subscription price to make the decision simple and easy for marketplaces. We are open to alternative, variable pricing models where they make more sense for the customer on a case-by-case basis.
Do you think your service will be more beneficial for marketplaces in developed countries or in developing markets or what factors indicate in which markets you could add most value?
We can service marketplaces in any market because, at the core, we are simply a data enrichment and machine learning platform for improved decisioning. We are however seeing steadily increasing interest from emerging market lenders which makes sense based on the following two macro factors that drives demand of our product:
1) Shortages of credit bureau data (much more prevalent in emerging markets). 2) High internet and social media penetration (much higher in developed markets but converging quickly). We will always be able to help developed market lenders access non-traditional borrowers (students, young professionals and foreign nationals). However, in developing markets where vast portions of the population lack financial history, and will soon be using the internet as much as anywhere else, we have a chance at bridging an accessibility gap in finance that unfairly applies to a large portion of the normal population.
Is data privacy a main concern for platforms and or borrowers? How many potential borrowers abandon the process at the step where they are asked to consent in sharing the data you require?
Data privacy is always a consideration. Many people would not, and do not, share their private profiles to improve their score. Most of these people are lucky enough to have ample transaction history to have a good credit score, and therefore don’t need to. This is not the demographic who needs our product. Unfortunately, there are many good and trustworthy people who are not able to access finance simply because they have not had the opportunity to build financial history. Many of those people, particularly ones with little to hide about themselves, don’t mind sharing some of their private online data to show themselves as a worthwhile credit risk. We offer people who deserve affordable financial products an alternative way, and a better chance, to access them by sharing more about themselves very quickly and easily if they want to.
Please tell us a bit about your team, the challenges you faced when developing the service and how your company is funded.
FriendlyScore was born out of an online lending platform run by co-founder, Maciej Dolinski, trying to solve the problem of very low approval rates among young borrowers because of a lack of credit data at the bureau. He started using facebook data to get more information on his customers in an attempt to lend to more of them. Good results led to the use of more online data sources and eventually selling the lending business to focus entirely on building a product to solve the same problem for other lending businesses, FriendlyScore. Emilian Siemsia developed the product and became the technical co-founder. Gideon joined the founding team right after they moved to London for the StartupBootCamp fintech accelerator to access the global market. Today we are building a product that can analyse over 50 data sources and map the online footprint of any Internet user (only with their consent of course).
One of the largest challenges we have faced is aggregating the data collection across different markets, both geographical and product-type.
FriendlyScore is funded since early 2015 by a UK based EIS fund, Mercia Technologies, and is currently raising a round from early stage VC’s and angels.
P2P-Banking.com thanks Gideon Valkin for the interview.