Agenda. Reference scenario and strategic framework Cutomer Life Cycle Data Mining Applications. Igor Rossini

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1 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

2 Agenda Reference scenario and strategic framework Cutomer Life Cycle Data Mining Applications

3 Strategic Framework Economic Scenario Global and International Market Integration (M&A) Processes Marketing Strategy Competitive Context Customer Segmentation New Client Needs and Behaviour Product Innovation Italian Insurance Market Increased Level of Competition New Channel of Distributions and Operators Reduction market growth rate Legal Evolving Legislation ITC Improvement of Technology

4 Marketing Strategy Evolution Intensity of Competition Selling Aptitude Market-Driven Management Client Needs Product Concept Demand>Supply Demand=Supply Demand<Supply Maturity Level Market

5 Customer Life Cycle Acquisition Activation Relationship Management and Retention Prospect Responder Client Ex Client High Value Voluntary Churn Target Market New Customer Customer High Potential Low Value Forced Churn

6 Customer Life Cycle Events Prospect Responder Client Ex Client High Value Voluntary Churn Target Market New Customer Customer High Potential Low Value Forced Churn Acquisition Campaign Use Response Acquisition Campaign Info Requests Adhesion Churn Cross-Selling campaign Up-Selling campaign Anti Attrition Campaign

7 Data Mining Application on Customer Life Cycle High Value Voluntary Churn Target Market New Customer Customer High Potential Low Value Forced Churn -Predictive model for Selling -Predictive model for Risk Analysis -Descriptive model on Relevant Attributes -Descriptive model on Customer Behaviour -Predictive Model for Cross/Up- Selling campaign -Predictive model for Fraud Detection -Predictive model for Churn

8 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

9 Mining Environment ADLER

10 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

11 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

12 Predictive Model The model assigns to each customer a score meaning the purchase probability of the policy

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

14 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

15 Project Structure Data Base for Analisys Factor Analisys Clustering Profiling Distance Map - Data Base development - Customer Table definition - Different clustering algorithm applied - Trade off cluster numerosity and his level of meaning -Distance mapof each client from the centre of his segment - Factor analysis with no significative results - Cluster description - Business Intelligence

16 Customer segments of interest N AUTO FULL OPTION AUTO BASIC FUTURO E TUTELA PENSO AI MIEI AUTO E SALUTE ALL BUSINESS IN SALUTE GIOVANI PREVIDENTI POCHI MA BUONI CASA E FAMIGLIA DI TUTTO, DI PIU

17 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

18 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

19 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, mean severity 5516,84 std dev severity 11619,9 pure premium 63,753 loss ratio 0, training claims out of training points

20 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

21 Preprocessing

22 Rules discovered Example of a rule If age < = 20 And sex = male And insured_amount > = 5000 And insured_amount < = Then insurance_claimed = 1, cost = 0. (0, 15) Claim associated with each risk area (fig. 1) and high claim risk area (fig. 2) Claims Number of rules Claims Exposure Table 2 Cost Table 1

23 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

24 Neural segmentation

25 Association Rule the number of association rules obtained: S min = 1% S min = 0.5% S min = 0.25% C min = 50% 24 rules 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.

26 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

27 Cluster discovered (1) CLUSTER # CUSTOMER % Top Driver ,33% Tradizionali ,74% Donne In Carriera ,20% Mix Alto Potenziale ,98% Guidatori Inesperti ,74%

28 Cluster discovered (2)

29 Guidatori inesperti

30 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

31 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

32 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

33 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