Abstract
In this paper we propose an efficient fuzzy computational framework for customer segmentation model in credit analysis. Normally segmentation methods cannot perform complex analysis so as to obtain the customer segments with high value. If the knowledge of experts in the data domain can be imparted to the generation of segments, this can bring in better results in the performance of classification models. In our approach customer attributes are selected after knowledge experts analysis and are segmented based on the limits set by them on the real numerical values. For each segment we generate the segmentation rules with definition of fuzzy basic linguistic term set. Each linguistic term set is assigned to a fuzzy membership function to generate the segmentation function. Combining the segmentation rules and generated functions the real valued numerical attributes are converted to fuzzified values in the interval [0, 1]. Both linguistic and numeric information are aggregated by a series of computations and a 2-tuple linguistic value is generated for each attribute in the database. The same term after a series of computations can be used in many decision making problems as it suffers no loss of information.
Highlights
Customer segmentation can be defined as a process by which customers can be divided among various subgroups based on their attributes or various other features such as behaviour, character, value, needs, loyalty, morale etc. [1]
Fuzzy approach in segmentation method assigns each observation to each segment with a certain degree of membership [7]
Each numerical attribute is aggregated with a linguistic attribute generated in the Basic Linguistic term set using the rules, functions and computations involving fuzzy linguistic term
Summary
Customer segmentation can be defined as a process by which customers can be divided among various subgroups based on their attributes or various other features such as behaviour, character, value, needs, loyalty, morale etc. [1]. Many important decisions have to be identified like the selection of segments, size of the segments, criteria for identifying each segment etc These input variables can be further divided into subgroups in demographic, geographic and life style [4] Credit Analysis is essential to identify the risks involved in the decision making regarding any lending situation. It assess the repayment capacity of the customer, the morale and trustworthiness shown by them during their previous interactions, the risks involved in making decision in view of the collateral and the capacity of the customers.
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