Determination of Client Profitability under Uncertainty Based on Decision Tree

Abstract

Complex decision making tasks of different natures, e.g. identifying of a profitability of a client in the insurance business, are based on vague, sparse, partially inconsistent and subjective knowledge of experts. One important problem related to realistic decision making tasks is uncertainty in input data / information. Decision-making under these conditions is difficult and can lead to incorrect results (decisions). The aim of this paper is to present an easy approach of how to identify profitability of the client in insurance business under the 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 represented by a decision tree. This decision problem is solved for a fictitious client, but the required 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 serve mainly for needs of insurance companies. The main contribution of this paper is using a decision tree to provide managers with the tool to support decision-making and information about expected client profitability for the next period and its confidence interval.Complex decision making tasks of different natures, e.g. identifying of a profitability of a client in the insurance business, are based on vague, sparse, partially inconsistent and subjective knowledge of experts. One important problem related to realistic decision making tasks is uncertainty in input data / information. Decision-making under these conditions is difficult and can lead to incorrect results (decisions). The aim of this paper is to present an easy approach of how to identify profitability of the client in insurance business under the 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 represented by a decision tree. This decision problem is solved for a fictitious client, but the required 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 serve mainly for needs of insurance companies. The main contribution of this paper is using a decision tree to provide managers with the tool to support decision-making and information about expected client profitability for the next period and its confidence interval.