oesai comprehensive life insurance technical training
TRANSCRIPT
OESAI COMPREHENSIVE LIFE INSURANCE TECHNICAL TRAINING
Predictive UnderwritingHow insurers can use statistics models to make sales process
easier
OESAI COMPREHENSIVE LIFE INSURANCE TECHNICAL TRAINING
Ezekiel MachariaGroup Actuary - Jubilee Holdings Limited
Day 1, Wednesday 11th November, 2015
AGENDA
• Predictive Underwriting• Making a Life Insurance Sale• What are predictive models• Usage of predictive models• Sample scoring• Developing an predictive model• Conclusion
Type “statistics” on eBay and an advertisement comes related to your search – how did they know what you like or if you will click on the advertisement
Predictive Underwriting
• Using predictive models to give insights into the day-to-day underwriting processes of a life insurer
• For example, determine the profile of the client beforehand and determine which people are fast tracked and those that require a medical report
Making a Life Insurance Sale
• Ten people want to buy a life insurance policy with a sum assured of $100,000• Each requires a medical report as per the underwriting guidelines for the sum assured requested• Also required to fill in 10 page questionnaire
Before underwriting (High Sum Assured)
Making a Life Insurance Sale
• Five people give up!!• Three people are ok•One requires premium to be adjusted with exclusions•One is rejected
After underwriting (High Sum Assured)
Load OK OKDeclin
eOK
Making a Life Insurance Sale
• Sale process was unsuccessful due to the following:-
•Process is cumbersome for client but critical for insurer–Requires a third-party medical exam
•Broadcast approach – check everyone (we don’t now who is a high risk and who is a low risk)
•Blame Others: Our agents made a hard sale? Was this the right customer? The product is expensive, if the price was lower – could they have bought the product?
What are predictive models
• Example - Models that use statistics to score the risk profiles of potential clients and provide insights as to which clients require further investigation, e.g medical checkup•We can now require less people to go through the rigorous process of underwriting & verification – improving the sale process• In the example below – 5 people do not need to take medical examinations after risk scoring
What are predictive models
• The predictive models can be automated in the IT system
Possible usage of predictive models for life insurance companies
Agent SelectionShortlisting productive agents
Customer SegmentationWhich customers will buy life insurance
Cross-SellingWhich term assurance clients can buy endowment?
Sales Others
Risk SelectionRisk scoring, ordering underwriting requirements
Price OptimizationDifferent prices for different channels
FraudOver-insurance and anti-selection
PricingReflect risk more effectively
ReservingSetting the right technical provisions
Sample Scoring – Underwriting requirements
Pass Refer to underwrite
r
MedicalTest Reject
Key requirements for predictive model
•Data, data, data….•Historical data (preferable in suitable format)•Data Warehouse
•Rating Factors: Age, Gender, Smoking status, Sum Assured, Admitted family history, BMI, Negative admitted personal medical history , current findings on blood (haemoglobin), lipids (e.g fats), Liver test (GGTP), etc
•Configuration with experience (need regular updates)
Developing a Predictive Model
1. Data Mining - Establish Patterns•Collect data, clean data and assign data distribution
2. Logic & Algorithm•Develop decision trees & identify factors and predictors
3. Build Model (can be repetitive)•Build, Test & Calibrate
4. Validate5. Implement & Document6. Monitor and Recalibrate
Popular Predictive Models
1. Decision Trees 2. Regression Trees3. Cox Model 4. Generalized Linear Model5. Logistic Regression6. Regression Spline7. Neural Networks8. k-Nearest Neighbour
Disadvantage
1. The model may be wrong– If not checked/updated/calibrated regularly with
recent data– Overfitting/wrong predictors– May not make sense (common sense)
2. Black box – nobody knows what is inside it
3. May depend on modeller (biased by perceptions)
4. Requires IT infrastructure, data (lots of it) and human capital
Advantages1. Prediction– Customers are happy if the sale process is
shortened or the sale is warmer (selling to a client already looking for a particular product)
2. Some prediction models require minimal statistical knowledge – neural nets
3. Various statistical methods available for prediction models
4. Usage of already collected data to improve business process – insurers with rich history, strong data integrity can leverage – perfect for online business
Conclusion
• Predictive underwriting uses data analytics to give insights into the customer
• These insights can be used to provide competitive advantage for an insurer – this can be in sales, claims, pricing or reserving
• Prediction models can be build but require data
• Expected to grow with more adoption of big data and data mining techniques
• Perfect for online business
