Big Data Scoring – the story so far

Tell me about your company – where did the idea come from?

Prior to founding Big Data Scoring I was a management board member as well as a member of credit committee of the fastest growing Estonian bank. That’s when I saw that the way credit scoring is done at most banks is ignoring all the technical innovation the world has seen over the past 30 years. Shortly after I quit the banking job and we started looking for ways how modern technology can make credit scoring better.

How would you describe your business’ ideology?

Our goal is to help bring lending to the digital information age and that’s what makes the team get up early each morning. At the same time, by helping lenders we are actually helping consumers – we helping people who can afford credit get access to the financing they deserve. But also we’re helping banks make sure money doesn’t end up in the hands of people who cannot afford credit.

Could you tell me a bit about your product/services range?

Most of our solutions are aimed at banks and non-bank lenders, both established players and new companies. At the same time, we also work with e-commerce, insurance and telecom providers.

Without going into too much detail, our solutions predict human behaviour using all the publicly available information. Based on the predictive models, lenders can better assess people’s creditworthiness, insurance companies can assess car accident likelihood and telecom provider can reduce churn. Whenever there’s a need for consumer behaviour prediction, our solutions can help.

Who are your biggest competitors?

On one hand, we are to some extent competing with all the traditional credit data providers (e.g. Equifax, Experian, etc). At the same time, there are also new start-ups that are looking into innovative data sources for credit behaviour predictions.

In a nutshell, our main strength is the amount of data that we are able to collect about consumers that can be used in the predictive models. As a general rule, the more valuable data you’re able to collect, the more accurate the predictive models. That’s essentially what clients need – accurate predictions.