This document discusses using big data scoring to improve lending decisions. It notes that 23% of the UK population and 25% of the US population are underbanked or non-prime customers. Traditional credit scoring uses limited data sources like loan applications and credit reports, but big data scoring analyzes additional sources like social media, online behavior, device info, and public records to generate more accurate risk assessments. The document presents two case studies where adding big data scoring reduced credit losses by 34.6% and 9.7% respectively for non-bank lenders while accepting the same number of loan applications.

1. BIG DATA SCORING
2015

2. OUR VISION
BRING LENDING INTO THE
DIGITAL INFORMATION AGE

3. ATTRACTING THE
NEXT GENERATION
OF AUTO BUYERS
3
LENDING TO THE
UNDERBANKED NON-
PRIME CUSTOMERS
23% of UK population,
that is 15m people
25% of USA population,
that is 83m people
…
13% of UK population,
that is 8m people
20% of USA population,
that is 64m people
…

4. 4
Credit scoring for the past 20 years…
FICO

5. Credit scoring 2.0
5
FICO

6. 6Source: Telefonica Millennial Study
People aged 18-33 spend
on average 6h a day
online

7. Studied abroad
Doesn’t use credit cards
No student loan
Fan of Apple Pay
Irregular income
No mortgage

8. Has a blog
Fan of social media
Freelance
designer
Loves his
smartphone
Has tons of friends

9. 9
Social media
Device info
Internet service
provider
Google search
results
Government
databases
Online
behavior
Employer info
Blogs

10. Social media
10

11. Freelance jobs
11

12. Behavior
12

13. Device
13

14. Government data

15. Location
15

16. Public online data
16

17. Current situation
17

18. With Big Data Scoring
18

19. Case 1 – credit loss decrease 34.6%
19
BEFORE
• Client: non-bank consumer
lender in CEE
• Current scoring: regression
based models
• Current data: (1) loan
application, (2) negative credit
bureau data, (3) positive credit
bureau data
• Credit loss: 7.5% (accepted
clients in scorebands 7-10)
AFTER ADDITION OF BIG DATA
While accepting the same amount
of loan applications, credit loss
dropped by 34.6% to 4.9%

20. Case 2 – also works with offline clients
20
BEFORE
• Client: non-bank consumer
lender in CEE
• Current scoring: regression
based models
• Current data: (1) loan
application, (2) negative credit
bureau data, (3) positive credit
bureau data
• Credit loss: 6.2% (accepted
clients in scorebands 4-10)
AFTER ADDITION OF PARTIAL
BIG DATA SCORE TO OFFLINE
CLIENTS
While accepting the same amount
of loan applications, credit loss
dropped by 9.7% to 5.6%

21. 21
Thank you!
Erki Kert
erki.kert@bigdatascoring.com
+44 7852 266613
Big Data Scoring Ltd
1 Canada Square, Canary Wharf
London, E14 5AB