Credit scoring and fraud detection in retail

The story of 10 years of risk analytics at Unigro

Geert VerstraetenPython Predictions

Case Unigro10 years of risk analytics

Business Meets ITMeet the ExpertsSept 10th, 2015

appliancesfurniture

hifi & multimedia beauty

linen

home

leisure

8 EUR x

15 month

40 EUR x

20 month

4 EUR x

2 month

6 EUR x

13 month

9 EUR x

18 month7 EUR x

5 month

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

Unigro – Mission Execution

NPS

-100

-50

0

50

100

41

does unigro

increas

e life

comfort?

does unigro

increas

e hap

piness?

do you

trust

unigro?

1234567

5.8 5.8 6.0

purchase in-tentions

1

2

3

4

5

6

7 6,6

█ Since 1948█ Structure:█ Figures:

220 employees8000 products205 000 active clients300 000 orders / year

Unigro – Facts

< <

450 000 articles sold / year42 Mio EUR revenue / year40% of revenues online

project definition

to read why predictive

analytics is like making

soup

click here

datapreparation

to read why predictive

analytics is like making

soup

click here

modelbuilding

to read why predictive

analytics is like making

soup

click here

modelvalidation

to read why predictive

analytics is like making

soup

click here

modelusage

to read why predictive

analytics is like making

soup

click here

Project definitionProject

Definition

0 1 2 3 4 5 6 7 8 9 10

11

12

13

14

15

16

17

18

0%1%2%3%4%5%6%7%8%

Risk

Length of relationship (years)

– Understand Unigro (goal, business processes, data)

Data preparationProject

Definition– Construct basetable(150 variables)

• Socio-demographic• Occupation• Financial• Relationship with Unigro• Default history• Order info

Data Preparation

43%

51%

6%

Data preparationProject

Definition– Discretise variables Data Preparation

High value orders are more riskyOnly 6% of orders have a value above 900€

of ordersbelow 150€

of ordersabove 900€

of orders150 - 900€

default risk

6%9%

15%

but they are much more risky

Score 1 Score 2 Score 30.500.550.600.650.700.750.800.850.900.951.00

0.71 0.70 0.68

0.770.73 0.71

0.81 0.800.75

Model building & validation

ProjectDefinition

Data Preparation

Model Building

ModelValidation

AUC(predictive

Performance) old model

refresh

new model

– Technical quality

Model building & validation

ProjectDefinition

Data Preparation

Model Building

ModelValidation

43%

51%

6%

5%

8%

15%

14%

86%

4%

1%

credit risk

fraud risk

of ordersbelow 150€

of ordersabove 900€

of orders150 - 900€

of ordersbelow 50€

of ordersabove 50€

Credit risk is related to high-value orders

Fraud risk is related to low-value orders

– Credit vs Fraud risk

Model building & validation

ProjectDefinition

Data Preparation

Model Building

ModelValidation

Risk decrease of

6.4%

Revenue increase of

4.8%

current new0%

1%

2%

3%

4%

5%4.38%

4.10%

Risk

current new0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

2,402,852 2,519,199

Revenue

– Estimating business impact

5%

-5%

4%

1%

Model usageProject

Definition– Monitoring: distribution change Data Preparation

ModelUsage

ordersbelow 50€

ordersabove 50€

Increasing fraud risk due to increase in low-value orders

fraud risk

Model usageProject

Definition– Monitoring: overview Data Preparation

ModelUsage

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

… … …

Results

Unigro’s revenue increased with 25% Risk decreased with 0,9 percentage points

Revenue on credit orders increased with 38%

Current CooperationMarketing

Segmentation & Targeting

RiskCredit – Fraud Risk & Collections

OperationsForecasting