Neural Network and Classification Approach in Identifying Customer Behavior in the Banking Sector: a Case Study of an International Bank

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Date

2015

Authors

Ogwueleka, Francisca Nonyelum
Misra, Sanjay
Colomo-Palacios, Ricardo
Fernandez, Luis

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Publisher

Wiley

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Organizational Unit
Computer Engineering
(1998)
The Atılım University Department of Computer Engineering was founded in 1998. The department curriculum is prepared in a way that meets the demands for knowledge and skills after graduation, and is subject to periodical reviews and updates in line with international standards. Our Department offers education in many fields of expertise, such as software development, hardware systems, data structures, computer networks, artificial intelligence, machine learning, image processing, natural language processing, object based design, information security, and cloud computing. The education offered by our department is based on practical approaches, with modern laboratories, projects and internship programs. The undergraduate program at our department was accredited in 2014 by the Association of Evaluation and Accreditation of Engineering Programs (MÜDEK) and was granted the label EUR-ACE, valid through Europe. In addition to the undergraduate program, our department offers thesis or non-thesis graduate degree programs (MS).

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Abstract

The customer relationship focus for banks is in development of main competencies and strategies of building strong profitable customer relationships through considering and managing the customer impression, influence on the culture of the bank, satisfactory treatment, and assessment of valued relationship building. Artificial neural networks (ANNs) are used after data segmentation and classification, where the designed model register records into two class sets, that is, the training and testing sets. ANN predicts new customer behavior from previously observed customer behavior after executing the process of learning from existing data. This article proposes an ANN model, which is developed using a six-step procedure. The back-propagation algorithm is used to train the ANN by adjusting its weights to minimize the difference between the current ANN output and the desired output. An evaluation process is conducted to determine whether the ANN has learned how to perform. The training process is halted periodically, and its performance is tested until an acceptable result is obtained. The principles underlying detection software are grounded in classical statistical decision theory.

Description

Misra, Sanjay/0000-0002-3556-9331; Colomo-Palacios, Ricardo/0000-0002-1555-9726; Fernandez Sanz, Luis/0000-0003-0778-0073

Keywords

Customer relationship management (CRM), Artificial neural network (ANN), Classification, Banks, Back-propagation algorithm

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Citation

26

WoS Q

Scopus Q

Q2

Source

Volume

25

Issue

1

Start Page

28

End Page

42

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