OESAI COMPREHENSIVE LIFE INSURANCE TECHNICAL TRAINING

Predictive Underwriting How insurers can use statistics models to make sales process easier OESAI COMPREHENSIVE LIFE INSURANCE TECHNICAL TRAINING Ezekiel Macharia Group 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

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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) LoadOK Decline OK

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 Selection Shortlisting productive agents Customer Segmentation Which customers will buy life insurance Cross-Selling Which term assurance clients can buy endowment? SalesOthers Risk Selection Risk scoring, ordering underwriting requirements Price Optimization Different prices for different channels Fraud Over-insurance and anti-selection Pricing Reflect risk more effectively Reserving Setting the right technical provisions

Sample Scoring – Underwriting requirements Pass Refer to underwriter Medical Test 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.Validate 5.Implement & Document 6.Monitor and Recalibrate

Popular Predictive Models 1.Decision Trees 2.Regression Trees 3.Cox Model 4.Generalized Linear Model 5.Logistic Regression 6.Regression Spline 7.Neural Networks 8.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

Advantages 1.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

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