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Predictive Modeling Techniques in Insurance

Tuesday May 5, 2015

JF. Breton – Application Engineer

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Opening

Presenter:

JF. Breton: 13 years of experience in predictive analytics and risk management in insurance and banking

This session:

– Cover different best-practice predictive modeling techniques in insurance

– Show how these can answer practical business questions with MATLAB

Takeaways:

– Big data and new available technology are creating new opportunities in the insurance industry

– MATLAB can quickly help you get value from your data with

its predictive analytics capability

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Agenda

Predictive modeling background

Case study – Claim settlements forecasting

Conclusion

(Warning: Crowded and busy slides ahead!)

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What is predictive modeling?

Use of mathematical language to make predictions about the future

More of an art than a science (lots of trial and error)

Predictive model

Input/

Predictors

Output/

Response

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Predictive modeling applications in insurance

Price risks

Identify target clienteles

Predict fraud

Claims triage

Set assumptions and reserves

And many more reasons

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Where does it fit?

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Trends that drive the use of these techniques

Available technology and large amount of data

Increased need for customized products/services

Pressure on top and bottom lines of income statement

(ref: “Insurance Risk and Reward“, The Economist 3/18/2015)

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Historical perspective: P&C vs. Life & Annuity

P&C industry has matured much faster Life & Annuity

Credit and risk scores have been used to predict future P&C claims for over 20 years

Short duration P&C products have limited tail risk compared to most life contracts

Mortality studies can require several years of data to

analyze but Life & annuity insurers are catching up

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2013 Insurance predictive modeling survey

Impacts

– Predictive models now widely used yet still mainly in pricing and underwriting

– Benefits seen on profitability, risk reduction and operational efficiency

Challenges

– Lack of sufficient data attributes and skilled modelers

– Data prep and model deployment can often take +3 months each – Big Data is currently mainly leveraged by large insurers

(Source: Earnix)

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The future: Increased focus on these techniques?

Life Insurance Policy Issued

Health /Lifestyle feedback Provided to p/h

Predictive Model run annually on

all policies

Policyholder Chooses to incorporate feedback

Premium Adjusted / Lapse Decision

Policyholder incentive to reduce risk

Reduce tail risk with adjustable premium Identify lifestyle

based risks after policy underwritten and issued Predictive

Model Determines

UW class Application

completed

Disruptions may occur from outside the industry

From risk pooling to “Big Mother”

Social Media

Data Geospatial

Data

Positive

feedback/coaching included in contract

Disruption From Non-Traditional

Insurance Providers

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Some applications and associated techniques

Predicting loss severity

(parametric)

– Generalized Linear Model

Predicting S&P 500 for annuities

(time series)

– ARIMA modeling – GARCH modeling

Predicting customer response

(non-parametric)

– Classification learning techniques

– Measure accuracy and compare models

May-01 Feb-04 Nov-06 Aug-09 May-12

800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800

S&P 500

Realized vs Median Forecasted Path Original Data

Simulated Data

0 10 20 30 40 50 60 70 80 90 100

Percentage

Bank Marketing Campaign Misclassification Rate

Neura l Net

Lo gistic

Regre ssi on

D iscrimin

ant A nalysi

s

k-n eare st N

eighb ors

Na ive B ayes

S upport VM

Deci sion Tre es

Tre eBa gger

R educed T

B No Misclassified Yes Misclassified

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Our focus on today: Learning Techniques

Supervised Learning (The target is known)

Unsupervised Learning (The target is unknown) Parametric

(Statistical)

• Linear Regression

• Time Series

• Generalized Linear Models

• Hazard Models

• Mixed Effect Models

• Cluster Analysis (i.e. K-means)

• Principal Components Analysis

Non-parametric • Neural Networks

• CART (Classification and Regression Trees)

• Random Forests

• MARS (Multivariate Adaptive Regression Splines)

• Neural Networks

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GLM as primary method of loss cost analysis

Source: Towers Watson survey in 2014

Primary method Additional methods

13

Principal component analysis

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Flexibility of GLMs

Common GLM Applications:

Technique Link Function Distribution Application

Classical Regression (Ordinary Least Squares)

Identity: g(µ)=µ Normal General Scoring Models

Logistical Regression Logit: g(µ)= log[µ/(1-µ)] Binomial Binary Target Applications (i.e.

Retention) Frequency Modeling Log: g(µ)=log(µ) Poisson

Negative Binomial

Count Target Variable Frequency Modelnig Severity Modeling Inverse: g(µ)=(-1/µ) Gamma Size of claim modeling Severity Modeling Inverse Squared:

g(µ)=(-1/µ^2))

Inverse Gaussian Size of claim modeling

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• Refine valuation assumptions

• Understand drivers of policyholder behavior

Other popular techniques and applications

CART

(Tree models) Marketing

Product Development GLM

Underwriting Neural

Networks

Retention Clustering

Random Forests Time Series/

Survival Models

Inforce Management

• Develop product assumptions based on prior products

• Targeted marketing campaigns

• Customer segmentation

Claims

• Enhance claims forecasting

• Fraud detection

• Align customer retention with customer value

• Streamline/reduce UW requirements

• Audit UW process

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Predictive modeling workflow

Known responses

Model

Model

New Data

Predicted Responses

Use for Prediction

Measure Accuracy Select Model &

Predictors

Import Data Explore Data

Data

Prepare Data

Known data

Train the Model

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Best practice #1: Testing and performance monitoring

Train X%

Test Y%

Validation Z%

Modeling Data Iterative model building

Model Implementation Model

Validation

Ongoing Performance

Monitoring

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Best practice #2: Refining the model

Simplify your model when possible

• Remove predictors, decrease iterations

Adding synthetic variables

– Example: “is the contract owner the insured?”

Refining the results with additional models or rules – Underwriting rules

– Principle Components Analysis

– Combinations of different models

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Agenda

Predictive modeling background

Case study – Claim settlements forecasting

Conclusion

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Claim Settlements Forecasting

Goal:

– Improve reserves by producing

accurate model to predict insurance claim settlement amounts

Approach:

– Train a regression model using different techniques

– Measure accuracy and compare approaches

– Use model for prediction

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Takeaways of case study

Interactive environment

– Visual tools for exploratory data analysis – Easy to evaluate and choose best algorithm – Apps available to help you get started

(e.g,. neural network tool, curve fitting tool)

Multiple algorithms to choose from

– Clustering

– Classification

– Regression

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Agenda

Predictive modeling background

Case study – Claim settlements forecasting

Conclusion

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Summary

 Big data and new available technology are creating new opportunities in the insurance industry

 MATLAB can quickly help you get value from

your data with its predictive analytics capability

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Q&A

Questions!?!

References

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