A mathematical programming approach to optimise insurance premium pricing within a data mining framework

A C Yeo, K A Smith (), R J Willis and M Brooks
Additional contact information
A C Yeo: Monash University
K A Smith: Monash University
R J Willis: Monash University
M Brooks: Australian Associated Motor Insurers Limited

Journal of the Operational Research Society, 2002, vol. 53, issue 11, 1197-1203

Abstract: Abstract In this paper we provide evidence of the benefits of an approach which combines data mining and mathematical programming to determining the premium to charge automobile insurance policy holders in order to arrive at an optimal portfolio. An non-linear integer programming formulation is proposed to determine optimal premiums based on the insurer's need to find a balance between profitability and market share. The non-linear integer programming approach to solving this problem is used within a data mining framework which consists of three components: classifying policy holders into homogenous risk groups and predicting the claim cost of each group using k-means clustering; determining the price sensitivity (propensity to pay) of each group using neural networks; and combining the results of the first two components to determine the optimal premium to charge. We have earlier presented the results of the first two components. In this paper we present the results of the third component. Using our approach, we have been able to increase revenue without affecting termination rates and market share.

Keywords: data mining; insurance; clustering; neural networks; integer programming; optimisation (search for similar items in EconPapers)
Date: 2002
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Citations: View citations in EconPapers (5)

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DOI: 10.1057/palgrave.jors.2601413

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