Correspondence: Maria Mavri, Quantitative Methods Laboratory, Department of Business Administration, University of the Aegean, Michalon 8, Chios 82100, Greece. Tel: +30 2270 35184; Fax: +30 22710 35189; e-mail: m.mavri@ba.aegean.gr

¹is Lecturer of Quantitative Methods in the School of Business, Department of Business Administration, University of the Aegean, Chios, Greece. She holds a first degree in Mathematics from the University of Athens, an MSc degree in Decision Sciences from Athens University of Business and Economics and a PhD in Operational Research from the same university. Her scientific interests apply in the field of Operational Research; Data Analysis, Linear and nonlinear Programming, Optimisation, Survival Analysis, Banking Management and Customer Relationship Management. She has presented her research work in international conferences and she has published research papers in scientific journals such as Omega, International Transactions in Operational Research and International Journal of Consumer Studies.

²is Professor of Quantitative Methods in the School of Business, Department of Business Administration, University of the Aegean, Chios, Greece. He holds a first degree in Mathematics from the University of Athens, an MSc degree in Industrial Mathematics and Statistics from the University of Aston in Birmingham and a PhD in Control Theory from the University of Warwick. He teaches graduate and undergraduate courses in Quantitative Methods. His research areas of interest include Sustainable Regional Development, Location Analysis, Business Process Management, Performance and Customer Satisfaction in the Services Sector. He has published extensively in journals such as Studies in Location Analysis, The Journal of Management Science and Regional Development, Studies in Urban and Regional Planning, International Journal of Applied Systemic Studies, Business Process Management and The Journal of Financial Services Marketing.

³is an Associate Professor at the Athens University of Economics and Business, the Acting Director of the International MBA Program, and Head of the Operations Management & ERP Systems Center. He received his diploma in Mechanical Engineering from NTUA (Athens), his MSc/DIC in Industrial Robotics from Imperial College and his PhD in Mechanical Engineering from the University of Maryland. His research concentrates on business systems, merging operations research with information technology. He is the recipient of the Microsoft Excellence in Education Award, chairs the Research and Development Committee of the Technical Chamber of Greece and is an Editorial Board member of Production Planning & Control.

⁴is a member of Special Scientific and Teaching Staff in the School of Business, Department of Business Administration, University of the Aegean, Chios, Greece. She has earned her PhD in Economic and Regional Analysis from the University of the Aegean. She teaches graduate and undergraduate courses in Quantitative Methods. Her research areas of interest include Transport/ICT and Regional Development, Isolated Regions' Development. She has presented several papers in international conferences and has published papers in Studies in Urban and Regional Planning.

⁵is adjunct Instructor in the School of Business, University of the Aegean, Chios, Greece. He has earned an MSc in Tourism Management, Policy and Planning from the University of the Aegean and his PhD in ICT and Innovative Organisations from the same university. He teaches graduate courses in ICT. His research areas of interest include Information and Communication Technologies and Regional Development, Innovative and Network Organisations, Destination Management and use of ICT in Tourism. He has presented several papers in international conferences.

Received 17 February 2008; Revised 17 February 2008.

Abstract

As credit card usage has expanded rapidly worldwide, credit scoring has become a very important task for banks, which can benefit from reducing possible risks of default. Credit scoring models help decision makers to decide whether to issue a credit card to a new applicant on the basis of both financial and nonfinancial criteria. The scope of the current study is to develop a dynamic scoring model that (a) estimates the credit performance of an applicant using generalised linear models and (b) accommodates the changes of a borrower's characteristics after the issuance of the credit card and forecasts the time of default using survival analysis.