Big data scoring for thin file and new-to-credit customers, with Oscar Koster (Big Data Scoring)
How to Lend Money to Strangers
Brendan le Grange
4.9 • 43 Ratings
🗓️ 9 September 2021
⏱️ 32 minutes
🧾️ Download transcript
Summary
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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Transcript
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| 0:00.0 | There's a whole bunch of people out there where the traditional model doesn't work. |
| 0:04.6 | There simply isn't enough information on these people to make a reasonable credit call. |
| 0:08.7 | Welcome back to How to Lend Money to Strangers. In today's episode I speak to Oscar |
| 0:13.5 | Custer from BigDataScoreing.com. Oscar Custer is a Dutch mining engineer turns |
| 0:18.4 | HabafganBanker turned in the National Entrepreneur. I met him at the midpoint of that progression when |
| 0:24.0 | he was my boss. Most famous for organizing a team bonding day where he took us to his previous |
| 0:30.2 | employer, the explosive factory. But I haven't made a podcast about how to efficiently break and |
| 0:35.6 | move a rock face using a series of carefully placed and perfectly timed charges. |
| 0:41.0 | How to Lend Money to Strangers is a podcast about lending strategies around the world |
| 0:45.6 | and across the credit life cycle. Sometimes those strategies are underpinned by traditional |
| 0:50.5 | data and tools and sometimes like today there's something a bit newer on the market. |
| 1:01.6 | Your entrance into banking was an unusual path but in terms of your experience within lending |
| 1:22.8 | was working with credit cards and working with debt collection and traditional scorecard models |
| 1:28.4 | and a market with multiple credit bureaus, solid data and you can kind of work a traditional |
| 1:34.2 | scorecard approach. But now what's the approach you're taking? I'm focusing on an Africa |
| 1:41.9 | about BigDataScoreing.com.com is largely focused on South America. It's really big for them. |
| 1:49.2 | The management is based in Chile. But in general the developing world is seen as the market |
| 1:55.1 | we're going after a couple of reasons. It's traditionally the people who have been excluded |
| 2:00.0 | from the credit cycle. There's so much data available on say Western Europe typically |
| 2:05.7 | that you don't really need to resort to alternative data to get an image of what someone is like. |
| 2:11.4 | There's a lot of stuff available. We found within the credit bureau world that often the fact |
| 2:17.4 | that we could tell somebody was definitely, thin file definitely new to credit was the most |
... |
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