The traditional credit model is often underpinned by an existing credit history. This makes sense mathematically, after all the best predictor of future delinquency is past delinquency, but it can present a barrier to entry to some customers – if you won’t give me credit today because I haven’t had credit before, then how can I ever get credit?
Consumers with thin files - or indeed no files at all - on the credit bureaus found themselves all lumped together and burden with a high-interest rate. In the big developed markets, this might be a small population and resolved by one or two lenders taking a risk. However, in developing markets many lenders don’t fall within the bureaus’ catchment areas and so even borrowers with a good history with credit may not have a bureau file that reflects that.
This is much harder to resolve. Or at least it used to be.
In today’s episode, I speak to Oscar Koster of BigDataScoring.com about the ways in which they are using alternative data to create predictive credit scores in developing markets. From social media connections to satellite photos of nighttime light production, the modern world is a rich source of data if you just know how to use it. You can reach out to Oscar by email)
You can reach out to me by email)
This is also the first cross-over with my other, temporarily paused, podcast – so if you’d like to listen to Oscar’s thoughts on mountain biking for mental health, head on over to https://www.themostfunyoucanhaveonabike.com/episodes-1/oscarkosterandthepostridebeer)
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