A Classification Based Model to Assess Customer Behavior in Banking Sector

descriptionPublicationkeyboard_double_arrow_right Article , Other literature type 19 Jun 2018Publisher:Engineering, Technology & Applied Science ResearchJournal:Engineering, Technology & Applied Science Research, volume 8, pages 2,949-2,953 (issn: 2241-4487, eissn: 1792-8036,

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Authors: Abdur Rahman; M. N. A. Khan;

doi: 10.48084/etasr.1917 , 10.60692/sryr6-t6n73 , 10.5281/zenodo.1400517 , 10.5281/zenodo.1400516 , 10.60692/p0qee-jhm59

A Classification Based Model to Assess Customer Behavior in Banking Sector

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Abstract

A customer relationship management system is used to manage company relationships with current and possible customers. Following a thorough review of contemporary literature, different data mining techniques employed in different types of business, corporate sectors and organizations are analyzed. A model that would be helpful to identify customers’ behavior in the banking sector is then proposed. Three classifiers, k-NN, decision tree and artificial neural networks are used to predict customer behavior and are assessed in order to determine which classifier performs better for predicting customer behavior in the banking sector.

Related Organizations

Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology
Pakistan

Keywords

FOS: Computer and information sciences, Artificial neural network, Organizational Behavior and Human Resource Management, Artificial intelligence, Social Sciences, Business, Management and Accounting, Customer relationship management, Customer retention, FOS: Economics and business, Customer Relationships, Behavior, and Loyalty, Data Mining Techniques and Applications, Service (business), Machine learning, Decision tree, profitability, Business, Order (exchange), Marketing, behavior, Decision Trees, data mining, prediction, Customer intelligence, Computer science, Service quality, customer, Computer Science, Physical Sciences, relationship, Classifier (UML), Customer Equity Management and Prediction, management, Finance, Information Systems

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