Constructing predictive model using Data mining techniques in support of Motor insurance policy risk Assessment: the case of Ethiopian Insurance Corporation (EIC)
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Date
2015-06
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A.A.U
Abstract
In recent years data mining has attracted a great deal of attention in
information industry due to the wide availability of huge amounts of data and
the need to change them into useful information and knowledge for broad
applications including market analysis, business management, decision
support, risk assessment and fraud detection. This research applies data
mining techniques in support of motor insurance risk assessment at the time
of underwriting.
The research is implemented using the six-step suggested by Cisos et. al.(2000)
DM process model. The data collection process has been done in two phases.
Records about vehicles are collected from INSIS database where as records
about drivers’ are collected manually. The collected dataset is preprocessed
using weka DM tools and Microsoft Excel in order to select attributes, derive
new attributes, handle missing values and remove outliers.
In this study an attempt was made to apply data mining clustering and
classification algorithms. K-means clustering algorithm is implemented to come
up with the natural group of the claim records as low risk, medium risk, high
risk and very high risk. The researcher implemented two classification
algorithms, J48 decision tree classification algorithms and multiperceptron
ANN. Using j48 decision tree classification algorithms different
experimentations are conducted. The first experimentation with default
parameter values and 10 fold cross validation test options has registered
94.63% accuracy. ANN experimentations have also been conducted.
Accordingly the experimentation with default parameter values with 10-fold
cross-validation test option registered 99.58% accuracy. The study also registered an accuracy of 93.74% with percentage split test option by splitting
the dataset into 80% to training set and 20% to test set.
The result of this study indicates that applying data mining to classify
insurance customers to predict the risk level is very promising. The above
prediction accuracy also indicates that data mining is a powerful tool to
measure the uncertainty of losses in EIC. Hence, the research identified future
research direction in order to implement applicable system in risk assessment
process.
Description
A Thesis Submitted to the School of Graduate Studies of Addis Ababa University in Partial Fulfillment of
The Requirements for the Degree of Master
Of Science in Information Science
Keywords
Data Mining Techniques, Insurance policy