Neural Network and Classification Approach in Identifying Customer Behavior in the Banking Sector: A Case Study of an International Bank Francisca Nonyelum Ogwueleka, 1 Sanjay Misra, 2 Ricardo Colomo-Palacios, 3 and Luis Fernandez 4 1 Department of Computer Science, Federal University of Technology, Minna, Nigeria 2 Department of Computer Engineering, Atilim University, Ankara, Turkey 3 Universidad Carlos III de Madrid, Spain 4 Universidad de Alcal ´ a, Depto. de Ciencias de la Computaci ´ on, Alcal ´ a de Henares, 28400 Madrid, Spain 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 classi- fication, 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 eval- uation 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. C  2012 Wiley Periodicals, Inc. Keywords: Customer relationship management (CRM); Artificial neural network (ANN); Classifi- cation; Banks; Back-propagation algorithm 1. INTRODUCTION A bank generates profits from transaction fees on finan- cial services and from the interest it charges for lend- ing. Many banks offer ancillary financial services, such as selling insurance and investment products or stock brokering, to make additional profits. Banks have vast Correspondence to: Dr (Mrs) Francisca Nonyelum Ogwueleka, Computer Science Department, School of Information & Information Technology, Federal University of Technology, P. M. B 65, Minna, Niger State, Nigeria. Phone: (+234) 07035653127; e-mail: nonnyraymond@yahoo.co.uk Received: 3 December 2010; revised 11 August 2011; accepted 11 August 2011 View this article online at wileyonlinelibrary.com/journal/hfm DOI: 10.1002/hfm.20398 databases, and important business information can be extracted from these data stores for decision making concerning customer transaction behavior patterns. Banks are facing increased competition for different reasons, including the entrance of financial and in- surance firms into the traditional banking market and the wide range of offered products and services to the public. As a consequence, the banking industry strives to succeed by putting the topic of rapid and changing customer needs on their agenda (Krishnan et al., 1999). Customer relationship management (CRM) first, seeks how to get closer to the customer by using the hidden data in broad databases, then transform the company into customer-centric organizations with a greater focus on customer profitability as compared to line profitability. CRM helps banks to improve the Human Factors and Ergonomics in Manufacturing & Service Industries 00 (0) 1–15 (2012) c  2012 Wiley Periodicals, Inc. 1