Predictive Modeling Using Data Mining Techniques in Support of Insurance Risk Assessment
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Date
2002-06
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Addis Ababa University
Abstract
One of the important tasks that we have to face in a real world application is the task of
classifying particular situation or events as belonging to a certain class. Risk assessment
in insurance policies is one example that can be viewed as classification problem. In
order to solve these kinds of problems we must build an accurate classifier system or
model. Data mining techniques are powerful tools in addressing such problems.
This research describes the development of predictive model, which determines the risk
exposure of motor insurance policies. Decision tree and neural network were used in
developing the model. Since rejections of policy renewal are rare at Nyala Insurance SC.
(NISCO), where the research was conducted as a case study, policies were classified into
one of the three possible groups (Low, Medium, or High risk) on the basis of annual
assessment made by NISCO. Six variables were extracted from the 25 variables used in
this study. 940 facts (90% of the working dataset) were used to build both decision tree
and neural network models. The remaining 116 (10 %) of the dataset were used to
validate the performance of the models. The decision tree model, selected based on the
meaningfulness of the rules extracted from it, correctly classified 95.69% of the
validation set, and the classification accuracy for low, medium and high risk policies are
98.15%, 94.12%, and 92.86% respectively. The neural network model correctly classified
92.24 % of the validation set, high-risk groups are correctly classified, and low and
medium-risk groups are classified with accuracy of 98.15% and 76.47% respectively.
Some possible explanations for the relatively low performance of the neural network with
medium policies are given. In addition, an interesting pattern was found between the two
models that some policies misclassified by decision tree were correctly classified by
neural networks, and vice versa. This is a good indication that the hybrid of the two
models may result in better performance
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Data Mining