Metodologie per SistemiIntelligenti

Ing. Igor RossiniLaurea in Ingegneria InformaticaPolitecnico di MilanoPolo Regionale di Como

Data Mining Applications in the Italian Insurance Market

© Igor Rossini

Agenda

• Reference scenario and strategic framework

• Cutomer Life Cycle• Data Mining Applications

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Strategic Framework

Reduction market growth rate

Italian InsuranceMarket

Global and International Market

Customer Segmentation

Improvement of Technology

New Channel of Distributions and

Operators

Product Innovation

Increased Level of Competition

New Client Needs and Behaviour

Integration (M&A) Processes

Evolving Legislation

Economic Scenario

Marketing Strategy Competitive Context

Legal ITC

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Marketing Strategy Evolution

Product Concept

Selling Aptitude

Market-Driven Management

Client NeedsIn

tens

ity o

f Com

petit

ion

Maturity Level Market

Demand>Supply Demand=Supply Demand<Supply

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Customer Life Cycle

Target Market

New Customer

High Value

Customer High Potential

Low Value

Voluntary Churn

Forced Churn

Prospect Responder Client Ex Client

Acquisition Activation Relationship Management and Retention

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Customer Life Cycle Events

Target Market

New Customer

High Value

Customer High Potential

Low Value

Voluntary Churn

Forced Churn

Prospect Responder Client Ex Client

Acquisition CampaignResponse Acquisition CampaignInfo RequestsAdhesion

Use

Cross-Selling campaignUp-Selling campaign

Anti Attrition CampaignChurn

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Data Mining Application on Customer Life Cycle

Target Market

New Customer

High Value

Customer High Potential

Low Value

Voluntary Churn

Forced Churn

-Predictive model for Selling

-Predictive Model for Cross/Up-Selling campaign

-Predictive model for Risk Analysis

-Descriptive model on “Relevant” Attributes

-Descriptive model on Customer Behaviour

-Predictive model for Fraud Detection

-Predictive model for Churn

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Swiss Life

• Title: Innovative marketing strategies using Data Mining solutions

• Challenge: support marketing initiative to preserve and extend the market share of the insurance company

• Results: better prospect selection, an efficient churn analysis, new descriptive model for client segmentation

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Mining Environment ADLER

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ADLER carachteristic

• Numerous data mining algorithms• User-friendly interface for data entry and for

setting analysis criteria

• MASY datawarehouse contains informationon:– policies– social and demographic attributes – spending level of population for geographic

areas

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Dr. Van Der Putten Case

• Title: Data Mining in an insurance company

• Challenge: expand the market for a caravan insurance product with low cost investment

• Results: improvement in the selection of individual prospects and better description of existing customers

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Predictive Model

• The model assigns to each customer a scoremeaning the purchase probability of the policy

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Descriptive Model

• The model identifies, among all the customer of a caravan policy, interesting groups for marketing initiatives

1

2

34

5

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Toro Assicurazioni

• Title: Behavioral segmentation of retail customers

• Challenge: characterize the purchasing profile of customers

• Results: more efficient marketing initiatives, target product development, Life Time Value of customer knowledge

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Project Structure

Factor Analisys

Data Basefor Analisys

Clustering ProfilingDistance

Map

- Data Base development

- Customer Table definition

- Factor analysis with no significative results

- Different clustering algorithm applied

- Trade off cluster numerosity and his level of meaning

- Cluster description

- Distance map of each client from the “centre” of his segment

- Business Intelligence

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Customer segments of interest

“DI TUTTO, DI PIU’”“N”

CASA E FAMIGLIA10

POCHI MA BUONI9

GIOVANI PREVIDENTI8

IN SALUTE7

ALL BUSINESS6

AUTO E SALUTE5

PENSO AI MIEI4

FUTURO E TUTELA3

AUTO BASIC2

AUTO FULL OPTION1

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Farmers Insurance Group

• Title: Driving profitability

• Challenge: Data Mining to the insurance industry in large-scale profitability and risk analysis

• Results: identification of “nuggets” of information “useful” for reducing frequency and severity of claims

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Project details

• Data: 4 years of historical data, 2.4 million policies, 35 million records

• Solution: Underwriting Profitability Analysis (UPA), a customer tailor package software developed for the insurance market by IBM, based on a decision tree algorithm

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Rules discovered

• 40 “nuggets” of information useful togenerate lower cost for claims of several million dollars

• Example of a rule (illustrative)Rule #22IFField “VANTILCK” “Vehicle Antilock Break Discount?”= “Antilock Brake”Field “VEHTYPE” “Type of Vehicle”= “Truck”THENclaim rate 0,0115561mean severity 5516,84std dev severity 11619,9pure premium 63,753loss ratio 0,688204608 training claims out of 53221 training points

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NRMA

• Title: Insurance risk assessment using a KDD methodology

• Challenge: Acquiring knowledge for the domain of motor vehicle insurance premium setting

• Results: interesting pattern in the data useful to better insight policy premium setting

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Preprocessing

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Rules discovered

• Example of a ruleIf age < = 20And sex = maleAnd insured_amount > = 5000And insured_amount < = 10000Then insurance_claimed = 1, cost = 0. (0, 15)

Claims Number of rules1 10902 4943 1924 825 38… …16 2

Table 1

Claims Exposure15 174516 219822 265

Cost433085021385678

Table 2

• Claim associated with each risk area (fig. 1) and high claim risk area (fig. 2)

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Australian Health Insurance Commission (HIC)• Title: Applying Data Mining Techniques to a

Health Insurance Information System

• Challenge: demonstrate the effectiveness of two data mining techniques in analyzing and retrieving unknown behavior patterns

• Results: detection of patterns in the ordering of pathology services and classification of the general practitioners into groups reflecting the nature and style of their practices

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Neural segmentation

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Association Rule

• the number of association rules obtained:

Smin= 1%

Cmin= 50% 24 rules

Smin= 0.5%

64 rules

Smin= 0.25%

135 rules

‘If Iron Studies and Thyroid Function Tests occur together then there is an 87% chance of Full Blood Examination occurring as well.This rule was found in 0.55% of transactions.’

• an example a rule:

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X- Insurance

• Title: Data Mining techniques applied to motor auto policies

• Challenge: better knowledge of customer claim profile to support marketing initiative for market share growth

• Results: policy premium setting developed according to the level of risk of the customer group discovered

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Cluster discovered (1)

“Top Driver” 398.578 34,33%

“Tradizionali” 275.742 23,74%

“Donne In Carriera” 234.537 20,20%

“Mix Alto Potenziale” 139.114 11,98%

“Guidatori Inesperti” 113.080 9,74%

CLUSTER # CUSTOMER %

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Cluster discovered (2)

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“Guidatori inesperti”

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COIL CHALLENGE 2000

• Title: predicting and explaining Caravan Policy Ownership

• Challenge: promote the application of computational intelligenge and learning technology to the real world problems

• Task:– predict which customers are potentially interested in

caravan insurance policy– describe the actual or potential customers; and

possibly explain why these customers buy a caravan policy

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RESULTS

• Prediction tasks: the winning model, based on a naive bayes approach, selected 121 policy ownerson a total of 238

• Description Task: the winning model was built using the association rule method and better explained why people were not interested in a caravan policy

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Other Applications (1)

DATA MINING MODELINSURANCE COMPANY

Descriptive and predictive model for policy rate setting

Predictive model for churn analysis

1- Predictive model to select the best customer for selling banking products2- Predictive model for a cross-selling campaign

Predictive model for policy renewals

© Igor Rossini

Other Applications (2)

DATA MINING MODELINSURANCE COMPANY

Descriptive model to discover pattern of interest among claims

Predictive model for fraudulent medical servicesdetection

Predictive model for fraud detection

Descriptive model for behaviural customer segmentation