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OPTIMAL RATEMAKING WITH SAS4TH OCTOBER 2016

Company Confidential - For Internal Use OnlyCopyright © 2015, SAS Institute Inc. All r ights reserved.

OPTIMAL RATEMAKING WITH SAS

Overview

Data

Modelling• Model Prototyping (Visualization)• Actuarial Analytics• Portfolio Optimization

Deployment• Automated Processes• Real Time Pricing

Benefits

AGENDA

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OPTIMAL RATEMAKING WITH SAS (OVERVIEW)

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OPTIMAL RATEMAKING FRAMEWORK IN ACTION

Data

DeploymentModellingTo get VALUE out of any analytics endeavor, 3 key components have to be considered: • DATA; • MODELLING; and • DEPLOYMENT.

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DATA IS THE FOUNDATION

Data Access

Data Quality

Data Transformation

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WHAT WE DO WITH DATA: MODELLING

Model Prototyping

Pricing & Reserving Models

Other Models (e.g. Churn)

Portfolio (pricing) Optimization

Modelling

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HOW WE OPERATIONALIZE THE RESULTS: DEPLOYMENT

Real Time Pricing

Automated (batch)processes

Regulatory Compliance

Dashboards

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OPTIMAL RATEMAKING SAS INSURANCE RISK MANAGEMENT

Risk Analytics• Stress Testing and 

Scenario Analysis

• Reserving

• ALM

• Model Risk

Risk Analytics• Stress Testing and 

Scenario Analysis

• Reserving

• ALM

• Model Risk

IRM SuiteIRM Suite

IoT• Usage Based 

Insurance

• Ratemaking

IoT• Usage Based 

Insurance

• Ratemaking

Regulatory Risk• Solvency 

Frameworks

• Economic Capital

• ORSA

Regulatory Risk• Solvency 

Frameworks

• Economic Capital

• ORSA

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DATA

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DATA ENSURE THE QUALITY OF THE MODELS AND CALCULATIONS

Data Exploration

Rules Definition

Process Deployment

Data Quality KPIs

Analysis & Remediation

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MODEL PROTOTYPING (VISUALIZATION)MODELLING

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MODELLING VARIABLE EXPLORATION AND TRANSFORMATION

• Explore variables:• Understanding variable distribution curves• Understanding correlation among variables• Detecting outliers

• Transform Variables:• Normalization of variables• Logarithmic transformation• Peak limiting• Manual discretization of continuous variables• Clustering or Segmentation• Automatic discretization of continuous variables

using Classification trees

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MODELLING MODEL PROTOTYPING

• Create GLM, Tree, Logistic, Linear Regression and Cluster models with full interactivity

• Create models using transformed or segmented variables

• Create multiple models at any granular level including clusters or segments

• Understanding variable significance and interaction of variables

• Model comparison to understand the best techniques and variables to select for the final model

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ACTUARIAL ANALYTICSMODELLING

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MODELLING ACTUARIAL ANALYTICS

• Use a variety of methodologies from a Programming or Visual modelling environment

• Easily create and compare more models leveraging the high performance environment

• Integration of tasks to extract and transform data with analytical processes and data mining.

• Pricing models:• Use traditional GLM methodologies• Test other alternative methodologies

• Claims reserves:• Deterministic methods like chain ladder, link

ratio• Stochastic methods like Mack Bootstraping• Ad-hoc methodologies

• Create models for other business objectives from a unique interface:

• Pricing based churn

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MODELLING ACTUARIAL ANALYTICS

Web-based Modelling

Automate the modelling process Explore model performance and results

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PORTFOLIO OPTIMIZATIONMODELLING

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PORTFOLIO OPTIMIZATION DEFINING THE BUSINESS PROBLEM

• Insurance companies renew their insurance policy portfolio periodically

• Must inform their customers, in advance, that they will change the premium price• If the increase of the premium is less than optimal, the next year Profit will be lower

• If the premium increase exceeds the optimal, the odds of quitting increase and ofpotentially having the customer leave the company

• It is necessary to optimize the profit function considering that each policy has acertain margin and that we must compensate the potential abandonment of certainsegments with rising premiums.

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PORTFOLIO OPTIMIZATION

MAXIMIZE THE PORTFOLIO RETURN MEETING THE BUSINESS CONSTRAINTS

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PORTFOLIO OPTIMIZATION

MAXIMIZE THE PORTFOLIO RETURN MEETING THE BUSINESS CONSTRAINTS

Business Global Parameters (Filters)

• They can act as excluding possible selections

• For a given customer profile:• Tipology

• With accidents• Platinum Segments• With Civil Liability Bonus>=

60%• The limitations will be:

• Minimum Increase 0%• Maximum Increase +20%

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PORTFOLIO OPTIMIZATION

MAXIMIZE THE PORTFOLIO RETURN MEETING THE BUSINESS CONSTRAINTS

Business Global Parameters (Constraints)

• They can set goals for portfolio or sub portfolio

• For the entire portfolio• Average Revenue Increase

4% • For a customer profile

• Typology• Without Claims• Platinum Segment• Civil Liability Bonus >=

60%• Average increase of -4%

Portfolio

Segment

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PORTFOLIO OPTIMIZATION SCENARIO’S RESULTS EXPLORATION

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AUTOMATED PROCESSESDEPLOYMENT

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DEPLOYMENT AUTOMATED PROCESSES

New Data

CentralizedDQ KPIs

Automated Process

History Tracking

Exploration of theactuarial calculations

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DEPLOYMENT DETAILED INFORMATION OF THE PROCESS

List of existingprocesses

Processtraceability

Resultsexploration

Data explorationManual input overrideProcess re-run

Documentation

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REAL-TIME PRICINGDEPLOYMENT

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REAL TIME PRICINGDEPLOYMENT

• Business user oriented tool• Flexible decision logic• Simulation & Test capabilities• Segregation of roles and responsibilities

• Low involvement of IT resources• No need to recode for production• Short deployment time• Integration with corporate front-ends using web-services

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BENEFITS

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BENEFITS

• Single (modular) platform extendable to cover other business topics• Complete automation of the business processes: data validation,

calculations and reporting• Improvement of the analytical capabilities to include new modelling

techniques (non standard)• Continuous improvement• Reduction of the operational risk• Regulatory compliance• Peace of mind

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THANK YOU