Fuzzy Prediction of Insolvent Customers in Mobile Telecommunication

Walid Moudani, Grace Zaarour and Félix Mora-Camino
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Walid Moudani: Department of Business Information System, Lebanese University, Beirut, Lebanon
Grace Zaarour: Department of Business Information System, Lebanese University, Beirut, Lebanon
Félix Mora-Camino: MAIAA (Mathématiques Appliquées, Informatique et Automatique pour l'Aérien) - Ecole Nationale de l'Aviation Civile/DGAC, Paris, France

International Journal of Strategic Information Technology and Applications (IJSITA), 2014, vol. 5, issue 3, 1-23

Abstract: This paper presents a predictive model to handle customer insolvency in advance for large mobile telecommunication companies for the purpose of minimizing their losses. However, another goal is of the highest interest for large mobile telecommunication companies is based on maintaining an overall satisfaction of the customers which may have important consequences on the quality and on the consume return of the operations. In this paper, a new mathematical formulation taking into consideration a set of business rules and the satisfaction of the customers is proposed. However, the customer insolvency is defined to be a classification problem since our main purpose is to categorize the customer in one of the two classes: potentially insolvent or potentially solvent. Therefore, a model with precise business prediction using the knowledge discovery and Data Mining techniques on an enormous heterogeneous and noisy data is proposed. Moreover, a fuzzy approach to evaluate and analyze the customer behavior leading to segment them into groups that provide better understanding of customers is developed. These groups with many other significant variables feed into a classification algorithm based on Rough Set technique to classify the customers. A real case study is considered here, followed by analysis and comparison of the results for the reason to select the best classification model that maximizes the accuracy for insolvent customers and minimizes the error rate in the misclassification of solvent customers.

Date: 2014
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