Applying Mahalanobis –Tagouchi System in Detection of High Risk Customers –A case-based study in an Insurance Company



The organizations use all appropriate tools to improve their service to the customers. The detection of especial customers and the forecast of their behavior undoubtedly can play an important role in improvement of service. In this paper, a new statistical method called the Mahalanobis Taguchi system has been used for this purpose. This method is used for the analysis of real data of an insurance company and five big cities in Iran are considered. There are seven initial factors which is important in the occurrence of accidents and losses. These factors are reduced to four. Customer's behavior is analyzed case by case by the Mahalanobis–distance concept. In fact with using this new method, demand of customers case by case was analyzed and it is an important outcome in analyzing behavior of customers. Devising ways to prevent the accidents and damages will need the recognition of Customer's behavior. The neural networks method is used to recognize the high–risk customers, and the results of this method are compared with the results of Mahalanobis–Taguchi system. The results show that Mahalanobis–Taguchi system with its abnormality scale has a great capability in recognizing high-risk customer. To recognize the customer by the Mahalanobis Taguchi system is more accurate in comparison with the neural networks method.