Presentation of our journey with Unigro in credit scoring and fraud detection at Business Meets IT: Meet the Experts in Antwerp, Belgium. Unigro (part of the Otto Group) is a Belgian distant-seller with a large product assortment. The company has a strategic focus on consumer (installment) credit, and has used Predictive Analytics with SAS for more than 10 years to predict and monitor creditworthiness. Using these techniques, Unigro has realized a revenue growth of 25 percent whilst reducing credit risk, despite the financial and economic crises and the increase of riskier internet orders. Find additional details about the event here: http://www.businessmeetsit.be/events/162887/seminar-meet-the-experts-de-beste-ict-projecten-on-stage-10-09-2015-/

1. Credit scoring and
fraud detection in retail
The story of 10 years of
risk analytics at Unigro
Geert Verstraeten
Python Predictions
Case Unigro
10 years of risk analytics
Business Meets IT
Meet the Experts
Sept 10th, 2015

2. appliancesfurniture
hifi & multimedia beauty
linen
home
leisure

3. Unigro – Mission
The brand contributes to making
the lives of its customers more comfortable
by facilitating access to
a large number of products and services,
offering purchases on credit,
granted responsibly

4. Unigro – Mission Execution
41
-100
-50
0
50
100
NPS
5.8 5.8 6.0
1
2
3
4
5
6
7
does unigro
increase life
comfort?
does unigro
increase
happiness?
do you
trust unigro?
6,6
1
2
3
4
5
6
7
purchase
intentions

5. █ Since 1948
█ Structure:
█ Figures:
220 employees
8000 products
205 000 active clients
300 000 orders / year
Unigro – Facts
< <
450 000 articles sold / year
42 Mio EUR revenue / year
40% of revenues online

7. project
definition
to read
why predictive
analytics is
like making
soup
click here

8. data
preparation
to read
why predictive
analytics is
like making
soup
click here

9. model
building
to read
why predictive
analytics is
like making
soup
click here

10. model
validation
to read
why predictive
analytics is
like making
soup
click here

11. model
usage
to read
why predictive
analytics is
like making
soup
click here

12. Project definition
Project
Definition
0%
1%
2%
3%
4%
5%
6%
7%
8%
0 1 2 3 4 5 6 7 8 9 101112131415161718
Risk
Length of relationship (years)
– Understand Unigro
(goal, business processes, data)

13. Data preparation
Project
Definition– Construct basetable
(150 variables)
• Socio-demographic
• Occupation
• Financial
• Relationship with Unigro
• Default history
• Order info
Data
Preparation

14. 43%
51%
6%
Data preparation
Project
Definition– Discretise variables
Data
Preparation
High value orders are more risky
Only 6% of orders have a value above 900€
of orders
below 150€
of orders
above 900€
of orders
150 - 900€
default risk
6%
9%
15%
but they are much more risky

15. 0.71 0.70
0.68
0.77
0.73 0.71
0.81 0.80
0.75
0.50
0.55
0.60
0.65
0.70
0.75
0.80
0.85
0.90
0.95
1.00
Score 1 Score 2 Score 3
Model building &
validation
Project
Definition
Data
Preparation
Model
Building
Model
Validation
AUC
(predictive
Performance) old model
refresh
new model
– Technical quality

16. Model building &
validation
Project
Definition
Data
Preparation
Model
Building
Model
Validation
43%
51%
6%
5%
8%
15%
14%
86%
4%
1%
credit risk
fraud risk
of orders
below 150€
of orders
above 900€
of orders
150 - 900€
of orders
below 50€
of orders
above 50€
Credit risk is related to
high-value orders
Fraud risk is related to
low-value orders
– Credit vs Fraud risk

17. Model building &
validation
Project
Definition
Data
Preparation
Model
Building
Model
Validation
Risk
decrease of
6.4%
Revenue
increase of
4.8%
4.38%
4.10%
0%
1%
2%
3%
4%
5%
current new
Risk
2,402,852
2,519,199
0
500,000
1,000,000
1,500,000
2,000,000
2,500,000
3,000,000
current new
Revenue
– Estimating business impact

18. 5%
-5%
4%
1%
Model usage
Project
Definition– Monitoring: distribution change
Data
Preparation
Model
Usage
orders
below 50€
orders
above 50€
Increasing fraud risk due to
increase in low-value orders
fraud risk

19. Model usage
Project
Definition– Monitoring: overview
Data
Preparation
Model
Usage
Variable % Change in
Risk
Risk Evolution
Score 5.6% Higher
Predictor 1 -0.3% Stable
Predictor 2 -15.1% Lower
Predictor 3 -2.3% Stable
Predictor 4 1.2% Stable
… … …

20. Results
Unigro’s revenue increased with 25%
Risk decreased with 0,9 percentage points
Revenue on credit orders increased with 38%

21. Current Cooperation
Marketing
Segmentation & Targeting
Risk
Credit – Fraud Risk & Collections
Operations
Forecasting