Download Data mining as a tool for Customer Selection

Data mining as a tool for customer selection
Nils F. Haavardsson & André Hønsvall
Gjensidige Forsikring
Agenda:
1) About Gjensidige Forsikring
2) The Business Problem at Gjensidige Skade
3) The Data warehouse for Non-Life Insurance
4) The use of SAS Enterprise Miner on the problem
4.1) Sampling
4.2) Exploring
4.3) Model
4.4) Modify
4.5) Assessment
5) Conclusion
SEUGI 1998 Prague
Data mining as a tool for customer selection
Nils F. Haavardsson & André Hønsvall
Gjensidige Forsikring
1) About Gjensidige Forsikring
* Founded by Ole J. Broch (the meter) in 1847
* Norwegian Mutual consisting of Gjensidige Skadeforsikring (1922)
Gjensidige Livsforsikring (1847) and Gjensidige Bank(1993).
* Total Assets ca. 20 Bill DEM.
* ca. 4500 employees.
* Market share in Non Life : 29.0 percent.
* Market share in Life
: 17.7 percent.
* Market share in Bank
: 2.3 percent.
* Gjensidige covers both The Commercial and The Private Market.
* Gjensidige has six regions with full business responsibility.
In addition, the decentralised organisation consists of 38
mutual fire companies and regional units. These undertake
fire insurance for own account and represent Gjensidige for
all other insurance lines. Most also represent Gjensidige Bank.
Data mining as a tool for customer selection
Nils F. Haavardsson & André Hønsvall
Gjensidige Forsikring
2) The Business Problem at Gjensidige Skade
* The motor insurance portfolio holds 38.2 percent of the total
premium income.
* Claims ratio for Non Life in 1997 : 84,7
Cost ratio for Non Life in 1997 : 25,8
This yields a combined ratio of 110,5.
Due to financial income…still good results...
* In 1996 Gjensidige had a net increase of almost 16000 private cars.
* Gjensidige`s agreement with the Norwegian Automobile
Association has undoubtedly contributed to this growth.
* The last year Gjensidige has seen an increase in the frequency
and in the claim amounts. Is this a general trend or are there
some other explanations ?
SEUGI 1998 Prague
Data mining as a tool for customer selection
Nils F. Haavardsson & André Hønsvall
Gjensidige Forsikring
3) The Data warehouse for Non-Life Insurance
* End-user interface
* User defined and controlled
* Produced for user access and analysis
* Flexible
* Can be distributed
* Data Warehouse
* Modelled and normalised
* Often centralised
* Controlled by IS Department
* Historical data
* Business operational systems
* Different platforms
* Existing platforms and new ones
* Continuously updated
Data Mining as a tool for customer selection
Business information warehouse
Derived
data
=
User
BIWs
User defined
DM tables
Reconciled
data
Real-time
data
=
Business data warehouse
=
Operational systems
Repository
Metadata
Data warehouse
Data mining as a tool for customer selection
Nils F. Haavardsson & André Hønsvall
Gjensidige Forsikring
4) The use of Enterprise Miner on the problem
* Constructing a data mining table :
* Draw randomly 50,000 customers from the private
market.
* Exclude customers with more than 3 vehicles.
* Create one record pr vehicle. For every vehicle
* Compute Earned Premium for 1997
* Policy information :
* geographical data
* bonus arrangement
* mileage
* sex and age of the policy holder
* policy status
* inception date
Data mining as a tool for customer selection
Nils F. Haavardsson & André Hønsvall
Gjensidige Forsikring
* agreement information
* make of car
* In case of loss :
* Compute Aggregated Amount of
Loss for 1997
* Driver data. (Sex, age, etc.)
* Number of losses
* For every vehicle we define the variable NP which represents
net profit and is given by
NP = Earned Premium for 1997
- Aggregated Amount of Loss for 1997
* We want to
* study the behaviour of NP
* learn about its distribution
* assess its most important explanatory variables
Data mining as a tool for customer selection
Nils F. Haavardsson & André Hønsvall
Gjensidige Forsikring
SEUGI 1998 Prague
Data mining as a tool for customer selection
Nils F. Haavardsson & André Hønsvall
Gjensidige Forsikring
5) Conclusion
* SAS Enterprise Miner enables advanced statistical analysis.
* The analyst must use the tool critically :
* Make a point of method understanding and thorough
interpretations of results.
* Check carefully whether the various model assumptions
are realistic and valid.
* SAS Insight is a powerful tool, useful in the exploring phase.
* Data organising is crucial.
* Some models are vulnerable to changes in the original data set.
SEUGI 1998 Prague