Metodologie per Sistemi
Intelligenti
Data Mining Applications in the
Italian Insurance Market
Ing. Igor Rossini
Laurea in Ingegneria Informatica
Politecnico di Milano
Polo Regionale di Como
Agenda
• Reference scenario and strategic
framework
• Cutomer Life Cycle
• Data Mining Applications
© Igor Rossini
Strategic Framework
Economic Scenario
Global and
International Market
Integration (M&A)
Processes
Marketing Strategy
Competitive Context
Customer
Segmentation
Increased Level of
Competition
Italian
Insurance
Market
New Client Needs
and Behaviour
New Channel of
Distributions and
Operators
Reduction market
growth rate
Product Innovation
Legal
ITC
Evolving Legislation
Improvement of
Technology
© Igor Rossini
Intensity of Competition
Marketing Strategy Evolution
Client Needs
Market-Driven
Management
Selling Aptitude
Product Concept
Demand>Supply
Demand=Supply
Demand<Supply
Maturity Level Market
© Igor Rossini
Customer Life Cycle
Acquisition
Activation
Prospect
Responder
Relationship Management and Retention
Client
Ex Client
High Value
Target
Market
New
Customer
Customer
Voluntary
Churn
High
Potential
Low Value
Forced
Churn
© Igor Rossini
Customer Life Cycle Events
Prospect
Responder
Client
Ex Client
High Value
Target
Market
New
Customer
Customer
Voluntary
Churn
High
Potential
Low Value
Forced
Churn
Acquisition Campaign
Use
Anti Attrition Campaign
Response Acquisition Campaign
Churn
Info Requests
Cross-Selling campaign
Adhesion
Up-Selling campaign
© Igor Rossini
Data Mining Application on
Customer Life Cycle
High Value
Target
Market
New
Customer
Customer
High
Potential
Low Value
-Predictive model for Selling
-Predictive model for Risk
Analysis
-Descriptive model on
“Relevant” Attributes
Voluntary
Churn
-Descriptive model on
Customer Behaviour
Forced
Churn
-Predictive model
for Churn
-Predictive Model for Cross/UpSelling campaign
-Predictive model for Fraud
Detection
© Igor Rossini
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
© Igor Rossini
Mining Environment ADLER
© Igor Rossini
ADLER carachteristic
• Numerous data mining algorithms
• User-friendly interface for data entry and for
setting analysis criteria
• MASY datawarehouse contains information
on:
– policies
– social and demographic attributes
– spending level of population for geographic
areas
© Igor Rossini
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
© Igor Rossini
Predictive Model
• The model assigns to each customer a score
meaning the purchase probability of the policy
© Igor Rossini
Descriptive Model
• The model identifies, among all the customer of a
caravan policy, interesting groups for marketing
initiatives
3
4
2
5
1
© Igor Rossini
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
© Igor Rossini
Project Structure
Data Base
for Analisys
Factor
Analisys
Clustering
- Different clustering
algorithm applied
- Data Base
development
- Customer Table
definition
- Trade off cluster
numerosity and his
level of meaning
- Factor analysis with
no significative results
Profiling
Distance
Map
- Distance map of
each client from the
“centre” of his
segment
- Cluster description
- Business Intelligence
© Igor Rossini
Customer segments of interest
1
AUTO FULL OPTION
2
AUTO BASIC
3
FUTURO E TUTELA
4
PENSO AI MIEI
5
AUTO E SALUTE
6
ALL BUSINESS
7
IN SALUTE
8
GIOVANI PREVIDENTI
9
POCHI MA BUONI
10 CASA E FAMIGLIA
“N “DI TUTTO, DI PIU’”
”
© Igor Rossini
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
© Igor Rossini
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
© Igor Rossini
Rules discovered
• 40 “nuggets” of information useful to
generate lower cost for claims of several
million dollars
• Example of a rule (illustrative)
Rule #22
IF
Field “VANTILCK” “Vehicle Antilock Break Discount?”
= “Antilock Brake”
Field “VEHTYPE” “Type of Vehicle”
= “Truck”
THEN
claim rate
0,0115561
mean severity
5516,84
std dev severity
11619,9
pure premium
63,753
loss ratio 0,688204
608 training claims out of 53221 training points
© Igor Rossini
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
© Igor Rossini
Preprocessing
© Igor Rossini
Rules discovered
• Example of a rule
If
And
And
And
Then
age < = 20
sex = male
insured_amount > = 5000
insured_amount < = 10000
insurance_claimed = 1, cost = 0. (0, 15)
• Claim associated with each risk area (fig. 1)
and high claim risk area (fig. 2)
Claims
1
2
3
4
5
…
16
Table 1
Number of rules
1090
494
192
82
38
…
2
Claims
15
16
22
Exposure
1745
2198
265
Cost
43308
50213
85678
Table 2
© Igor Rossini
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
© Igor Rossini
Neural segmentation
© Igor Rossini
Association Rule
• the number of association rules obtained:
Smin= 1%
Cmin= 50%
24 rules
Smin= 0.5% Smin= 0.25%
64 rules
135 rules
• an example a rule:
‘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.’
© Igor Rossini
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
© Igor Rossini
Cluster discovered (1)
CLUSTER
# CUSTOMER
%
“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%
© Igor Rossini
Cluster discovered (2)
© Igor Rossini
“Guidatori inesperti”
© Igor Rossini
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
© Igor Rossini
policy
RESULTS
• Prediction tasks: the winning model, based on a
naive bayes approach, selected 121 policy owners
on 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
© Igor Rossini
Other Applications (1)
INSURANCE
COMPANY
DATA MINING MODEL
Predictive model for policy renewals
1- Predictive model to select the best customer for
selling banking products
2- Predictive model for a cross-selling campaign
Predictive model for churn analysis
Descriptive and predictive model for policy rate setting
© Igor Rossini
Other Applications (2)
INSURANCE
COMPANY
DATA MINING MODEL
Descriptive model for behaviural customer
segmentation
Predictive model for fraud detection
Predictive model for fraudulent medical services
detection
Descriptive model to discover pattern of interest
among claims
© Igor Rossini