More customers can be approved for loans using Big Data Scoring's alternative credit scoring model. Their model analyzes more sources of personal information than traditional credit scores, including government data, device and location information, and public online data. This additional data allows them to better assess risk for underbanked individuals. In trials, Big Data Scoring's model improved loan approval quality and increased the number of approved loans by 26% compared to traditional credit scoring.

1. MORE CUSTOMERS, LESS RISK
Credit Scoring 2.0

2. 2

3. 3
Banked,
risk well
managed
Under-
banked
AMOUNT OF PERSONAL
INFORMATION AVAILABLE

4. In reality… there’s plenty of information
4
Under-
banked

5. Device
5

6. Government data

7. Location
7

8. Location
8

9. Public online data
9

10. Behavior
10

11. Effect of Big Data ScoreTM
11
Improved
quality
+26%
more
loans

12. 12
Banked,
risk well managed
AMOUNT OF PERSONAL
INFORMATION AVAILABLE
Under-
banked

13. 13
Thank you!
Kersten Eero
kersten.eero@bigdatascoring.com
+44 7535476647
+372 56682684
Big Data Scoring Ltd
1 Canada Square, Canary Wharf
London, E14 5AB