Customer segmentation in private banking sector using machine learning techniques

Abstract

Machine learning techniques have proven good performance in classification matters of all kinds: medical diagnosis, character recognition, credit default and fraud prediction, and also foreign exchange market prognosis. Customer segmentation in private banking sector is an important step for profitable business development, enabling financial institutions to address their products and services to homogeneous classes of customers. This paper approaches two of the most popular machine learning techniques, Neural Networks and Support Vector Machines, and describes how each of these perform in a segmentation process.

Keywords:

• machine learning
• neural networks
• support vector machines
• customer segmentation
• private banking

JEL Classification:

• C38
• C45
• C63

Additional information

Notes on contributors

Ion Smeureanu

Ion SMEUREANU has graduated the Faculty of Planning and Economic Cybernetics in 1980, as leader. He holds a PhD diploma in “Economic Cybernetics” from 1992 and has a remarkable didactic activity since 1984, when he joined the staff of Bucharest Academy of Economic Studies. Currently, he is a full Professor of Economic Informatics within the Department of Economic Informatics and the dean of the Faculty of Cybernetics, Statistics and Economic Informatics from the Bucharest University of Economic Studies. He is the author of more than 16 books and an impressive number of articles on economic modeling and computer applications. He was also project director or member in many important research projects. He was awarded the Nicolae Georgescu-Roegen diploma, the award for the entire research activity offered by the Romanian Statistics Society, General Romanian Economist Association Excellence Diploma and many others.

Gheorghe Ruxanda

Gheorghe RUXANDA is a PhD in Economic Cybernetics, Editor-in-chief of ISI Thompson Reuters Journal “Economic Computation and Economic Cybernetics Studies and Research” and Director of Doctoral School of Economic Cybernetics and Statistics. He is full Professor and PhD Adviser within the Department of Economic Informatics and Cybernetics, The Bucharest Academy of Economic Studies. He graduated from the Faculty of Economic Cybernetics, Statistics and Informatics, Academy of Economic Studies, Bucharest (1975) where he also earned his Doctor's Degree (1994). Had numerous research visits in Columbia University – School of Business, New York, USA (1999), Southern Methodist University (SMU), Faculty of Computer Science and Engineering, Dallas, Texas, USA (1999), Ecole Normale Superieure, Paris, France (2000), Reading University, England (2002), North Carolina University, Chapel Hill, USA (2002). He is full professor of Multidimensional Data Analysis (Doctoral School), Data Mining and Multidimensional Data Analysis (Master Studies), Modeling and Neural Calculation (Master Studies), Econometrics and Data Analysis (Undergraduate Studies). Fields of Scientific Competence: evaluation, measurement, quantification, analysis and prediction in the economic field; econometrics and statistical-mathematical modeling in the economic– financial field; multidimensional statistics and multidimensional data analysis; pattern recognition, learning machines and neural networks; risk analysis and uncertainty in economics; development of software instruments for economic-mathematical modeling. Scientific research activity: over 35 years of scientific research in both theory and practice of quantitative economy and in coordinating research projects; 50 scientific papers presented at national and international scientific sessions and symposia; 64 scientific research projects with national and international financing; 77 scientific papers published in prestigious national and international journals in the field of economic cybernetics, econometrics, multidimensional data analysis, microeconomics, scientific informatics, out of which eleven papers being published in ISI – Thompson Reuters journals; 18 manuals and university courses in the field of econometrics, multidimensional data analysis, microeconomics, scientific informatics; 31 studies of national public interest developed within the scientific research projects.

Laura Maria Badea

Laura Maria BADEA is a PhD candidate in Economic Cybernetics at the Bucharest Academy of Economic Studies, has an MA in Corporate Finance (2010) and graduated the Faculty of Finance, Insurance, Banking and Stock Exchange from Bucharest Academy of Economic Studies (2008). Fields of scientific interest: machine learning and other modeling techniques used for classification matters in economic and financial domains, with a focus on artificial neural networks. Scientific research activity: one published article in ISI Thompson Reuters Journal.