Abstract:
Accurate client information is important for identifying of a profitability of a client, e.g. in the insurance business. In the real situations accurate information are usually not known as there is always uncertainty in input data / information. This uncertainty of input data is based on usage vague, sparse, partially inconsistent and subjective knowledge of experts. Decision-making under these conditions is difficult and can lead to incorrect results (decisions). The aim of this paper is to present easy approach how identifying of profitability of the client in insurance business under condition of input data uncertainty. The solution to the decision-making problem is based on the decision to extend or renew an insurance contract for next period (concretely two years). The solution of this problem is based on the decision-making task, which is graphically illustrated by a decision tree. This decision problem is solved for a fictitious client, but the necessary data sets are based on real data sets. The case study is represented by a tree with three lotteries, three decisions and seven terminals. The results arising from the paper serves mainly for needs of insurance companies. The main contribution of this paper is using a decision tree to provide managers the tool to support of decision-making in evaluating whether an insurance policy selected client may be extended or not in the next period and information about expected profitability for the next period and its confidence interval.
