Als internetverzekeraar in een markt waarin de kleinste aanpassingen in pricing, product of dienstverlening het verschil kunnen maken moet je sneller en slimmer zijn dan je concurrenten. Ik laat concrete Data Science toepassingen zien die bewijzen dat je in een verzadigde markt gezonde groei kunt realiseren.

1. Zelfverzekerd met data
September 2018

2. Presenter:
Arjuna Bosch
Product owner Intrasurance

3. On the agenda today.
Customer lifetime value with individual loyalty model.
Risk scan.
SURE model.
Insurtech market and company introduction.

4. Some Insurtech trends
The digital insurer trends 2018:
InsurTech Trend #1 – Automation will replace human effort across the entire insurance
value chain
InsurTech Trend #2 – Insurance premiums will become highly personalised based on
greater tech enabled insight on customers and their individual risk
InsurTech Trend #6 – The All-in-One insurance policy is here to stay

5. What’s on the insurers mind?

6. Empowering consumers to be self-assured
4 maart 2002
Mission

7. Our companies

8. 1.Build the ultimate customer experience
2.Leveraging data by using smart blocks in co-operation
3.Risk and pricing in harmony
Why data science:

9. Consumer centered

10. Data processing
Data
warehouse
Model estimation
External data
sources
Cleaned data
(anonymized)
Validated
models
Prediction
Calculation
environment
Combine &
Optimize
Monitoring
environment
Actionable
output
New
customers
Data Calculations Outputs
Optimized
premium

11. Cases in action

12. Case: Customer Lifetime Value
CLV = Expected individual lifetime * commission -/- direct and overhead cost

13. Case: Customer Lifetime Value
CLV = Expected individual lifetime * commission -/- direct and overhead cost
Based on individual lifetime model (ILM)

14. Example 1

15. Example 2

16. Contract length customer can be predicted

17. Use cases
Segmentation based on CLV leads to more effective marketing spend
through online channels.
Focus on valuable existing customers with higher service (EG leniency
with claim handling).
Focus cross sell on customers who are likely to buy more / focus
retention campaigns on customers with high probability of churning.

18. Case 2: Risk scan

19. Risk scan
Focus on customer needs instead of products.
Take risk appetite into account.

20. In action

21. Risk scan results
+- 1.200 users a month.
2,2% Conversion rate of (non optimized for selling).

22. Next up
Create digital advisor who pro-actively keeps the offer up to date.
Offer flexible micro insurances which are used to assemble the right
product.
Enrich data instead of asking to customer.

23. Case 3: SURE

24. SURE
Premium vs. claims
Premium Claims

25. Case: SURE

26. SURE results
Higher differentiation in pricing so anti selection is prevented and the “right”
customers are attracted with the right premium for the risk.
Sure combines market pricing with risk levels to make a decision aggregating both
worlds.
Sure resulted in significantly lower combined ratio’s for our car insurance.

27. To wrap up
Start with the customer needs.
Start small. Choose one block. Integrate later.
Hire specialists. Data sciencists and data translators.