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10 years of risk analytics at Unigro

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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-/

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10 years of risk analytics at Unigro

  1. 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. 2. appliancesfurniture hifi & multimedia beauty linen home leisure
  3. 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. 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. 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
  6. 6. project definition to read why predictive analytics is like making soup click here
  7. 7. data preparation to read why predictive analytics is like making soup click here
  8. 8. model building to read why predictive analytics is like making soup click here
  9. 9. model validation to read why predictive analytics is like making soup click here
  10. 10. model usage to read why predictive analytics is like making soup click here
  11. 11. 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)
  12. 12. Data preparation Project Definition– Construct basetable (150 variables) • Socio-demographic • Occupation • Financial • Relationship with Unigro • Default history • Order info Data Preparation
  13. 13. 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
  14. 14. 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
  15. 15. 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
  16. 16. 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
  17. 17. 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
  18. 18. 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 … … …
  19. 19. Results Unigro’s revenue increased with 25% Risk decreased with 0,9 percentage points Revenue on credit orders increased with 38%
  20. 20. Current Cooperation Marketing Segmentation & Targeting Risk Credit – Fraud Risk & Collections Operations Forecasting

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