Title: Analytical CRM in banking and finance using SVM: a modified active learning-based rule extraction approach

Authors: M.A.H. Farquad; V. Ravi; S. Bapi Raju

Addresses: Institute for Development and Research in Banking Technology, Castle Hills Road #1, Masab Tank, Hyderabad – 500 057 (AP), India; Department of Computer and Information Sciences, University of Hyderabad, Hyderabad – 500 046 (AP), India; School of Business, The University of Hong Kong, Room 729(3), Meng Wah Complex, Pokfulam Road, Hong Kong ' Institute for Development and Research in Banking Technology, Castle Hills Road #1, Masab Tank, Hyderabad – 500 057 (AP), India. ' Department of Computer and Information Sciences, University of Hyderabad, Hyderabad – 500 046 (AP), India

Abstract: This paper presents advancement to modified active learning-based approach in an eclectic framework for extracting if-then rules from support vector machine (SVM) for customer relationship management (CRM) purposes. The proposed approach comprises of three major phases: 1) feature selection using SVM-RFE (recursive feature elimination); 2) active learning for synthetic data generation; 3) rule generation using decision tree (DT) and Naive Bayes tree (NBTree). Finance problems solved in this study are churn prediction in bank credit cards customers and fraud detection in insurance. Based on sensitivity measure, the empirical results suggest that the proposed modified active learning-based rule extraction approach yielded best sensitivity and length and number of rules is reduced resulting in improved comprehensibility. Feature selection leads to the most important attributes of the customers and extracted rules serves as early warning system to the management to enforce better CRM practices and detect/avoid possible frauds.

Keywords: support vector machines; SVM; rule extraction; modified active learning; insurance fraud; customer relationship management; analytical CRM; normal distribution function; logistic distribution function; customer churn; feature selection; banking industry; finance industry; churn prediction; bank credit cards.

DOI: 10.1504/IJECRM.2012.046470

International Journal of Electronic Customer Relationship Management, 2012 Vol.6 No.1, pp.48 - 73

Received: 08 May 2021
Accepted: 12 May 2021

Published online: 09 Apr 2012 *

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