Surveillance Technologies and Early Warning Systems: Data Mining Applications for Risk Detection PDF
Preview Surveillance Technologies and Early Warning Systems: Data Mining Applications for Risk Detection
Surveillance Technologies and Early Warning Systems: Data Mining Applications for Risk Detection Ali Serhan Koyuncugil Capital Markets Board of Turkey, Turkey Nermin Ozgulbas Baskent University, Turkey InformatIon scIence reference Hershey • New York Director of Editorial Content: Kristin Klinger Director of Book Publications: Julia Mosemann Acquisitions Editor: Lindsay Johnston Development Editor: Joel Gamon Publishing Assistant: Keith Glazewski Typesetter: Keith Glazewski Production Editor: Jamie Snavely Cover Design: Lisa Tosheff Published in the United States of America by Information Science Reference (an imprint of IGI Global) 701 E. Chocolate Avenue Hershey PA 17033 Tel: 717-533-8845 Fax: 717-533-8661 E-mail: [email protected] Web site: http://www.igi-global.com Copyright © 2011 by IGI Global. All rights reserved. No part of this publication may be reproduced, stored or distributed in any form or by any means, electronic or mechanical, including photocopying, without written permission from the publisher. Product or company names used in this set are for identiication purposes only. Inclusion of the names of the products or com- panies does not indicate a claim of ownership by IGI Global of the trademark or registered trademark. Library of Congress Cataloging-in-Publication Data Surveillance technologies and early warning systems : data mining applications for risk detection / Ali Serhan Koyuncugil and Nermin Ozgulbas, editors. p. cm. Includes bibliographical references and index. Summary: "This book presents an alternative to conventional surveillance and risk assessment offering a multidisciplinary excursion comprised of data mining, early warning systems, information technologies and risk management and explores the intersection of these components in problematic domains"--Provided by publisher. ISBN 978-1-61692-865-0 (hardcover) -- ISBN 978-1-61692-867-4 (ebook) 1. Electronic surveillance. I. Koyuncugil, Ali Serhan, 1973- II. Ozgulbas, Nermin, 1968- TK7882.E2S87 2011 658'.056312--dc22 2010016312 British Cataloguing in Publication Data A Cataloguing in Publication record for this book is available from the British Library. All work contributed to this book is new, previously-unpublished material. The views expressed in this book are those of the authors, but not necessarily of the publisher. Editorial Advisory Board K. Ibrahim Akman, Atilim University, Turkey Jan Dhaene, Katholic Leuven University, Belgium Omer L. Gebizlioglu, Ankara University, Turkey Orhan Guvenen, Bilkent University, Turkey Boris Kovalerchuk, Central Washington University, USA Senthil Kumar, CMS College of Science and Commerce, India Colleen McCue, MC2 Solutions, LLC, USA Hakikur Rahman, SchoolNet Foundation, Bangladesh Neven Vrcek, University of Zagreb, Croatia Chris Westphal, Visual Analytics Inc., USA List of Reviewers Boris Kovalerchuk, Central Washington University, USA Ali Serhan Koyuncugil, Capital Markets Board of Turkey, Turkey Senthil Kumar, CMS College of Science and Commerce, India Colleen McCue, MC2 Solutions, LLC, USA Nermin Ozgulbas, Baskent University, Turkey Evgenii Vityaev, Russian Academy of Science, Russia Neven Vrcek, University of Zagreb, Croatia Chris Westphal, Visual Analytics Inc., USA Table of Contents Foreword ............................................................................................................................................xiii Preface .................................................................................................................................................xv Acknowledgment .................................................................................................................................xx Section 1 Theoretical and Conceptual Approach to Early Warning Systems Chapter 1 Overview of Knowledge Discovery in Databases Process and Data Mining for Surveillance Technologies and EWS ...........................................................................................................................1 Inci Batmaz, Middle East Technical University, Turkey Guser Koksal, Middle East Technical University, Turkey Chapter 2 Data Mining and Privacy Protection .....................................................................................................31 Armand Faganel, University of Primorska, Slovenia Danijel Bratina, University of Primorska, Slovenia Chapter 3 On the Nature and Scales of Statistical Estimations Divergence and its Linkage with Statistical Learning ................................................................................................................................................52 Vassiliy Simchera, Research Institute of Statistics (Rosstat), Russia Ali Serhan Koyuncugil, Capital Markets Board of Turkey, Turkey Chapter 4 Black-Necked Swans and Active Risk Management ............................................................................64 Tze Leung Lai, Stanford University, USA Bo Shen, Stanford University, USA Section 2 Early Warning Systems for Finance Chapter 5 Financial Early Warning System for Risk Detection and Prevention from Financial Crisis ................76 Nermin Ozgulbas, Baskent University, Turkey Ali Serhan Koyuncugil, Capital Markets Board of Turkey, Turkey Chapter 6 Designing an Early Warning System for Stock Market Crashes by Using ANFIS ............................109 Murat Acar, ISE Settlement and Custody Bank Inc., Turkey Dilek Karahoca, Bahcesehir University, Turkey Adem Karahoca, Bahcesehir University, Turkey Chapter 7 Bankruptcy Prediction by Supervised Machine Learning Techniques: A Comparative Study ...........128 Chih-Fong Tsai, National Central University, Taiwan Yu-Hsin Lu, National Chung Cheng University, Taiwan Yu-Feng Hsu, National Sun Yat-Sen University, Taiwan Chapter 8 Data Mining Used for Analyzing the Bankruptcy Risk of the Romanian SMEs ................................144 Laura Giurca Vasilescu, University of Craiova, Romania Marian Siminica, University of Craiova, Romania Cerasela Pirvu, University of Craiova, Romania Costel Ionascu, University of Craiova, Romania Anca Mehedintu, University of Craiova, Romania Section 3 Early Warning Systems for Detection and Prevention of Fraud, Crime, Money Laundering and Terrorist Financing Chapter 9 Social Aid Fraud Detection System and Poverty Map Model Suggestion Based on Data Mining for Social Risk Mitigation ......................................................................................................173 Ali Serhan Koyuncugil, Capital Markets Board of Turkey, Turkey Nermin Ozgulbas, Baskent University, Turkey Chapter 10 Collaborative Video Surveillance for Distributed Visual Data Mining of Potential Risk and Crime Detection ..................................................................................................................................194 Chia-Hui Wang, Ming Chuan University, Taiwan Ray-I Chang, National Taiwan University, Taiwan Jan-Ming Ho, Academia Sinica, Taiwan Chapter 11 Data Mining and Economic Crime Risk Management .......................................................................205 Mieke Jans, Hasselt University, Belgium Nadine Lybaert, Hasselt University, Belgium Koen Vanhoof, Hasselt University, Belgium Chapter 12 Data Mining in the Investigation of Money Laundering and Terrorist Financing ..............................228 Ibrahim George, Macquarie University, Australia Manolya Kavakli, Macquarie University, Australia Section 4 Early Warning Systems for Customer Services and Marketing Chapter 13 Data Mining and Explorative Multivariate Data Analysis for Customer Satisfaction Study .............243 Rosaria Lombardo, Second University of Naples, Italy Chapter 14 Using POS Data for Price Promotions Evaluation: An Empirical Example from a Slovenian Grocery Chain .....................................................................................................................................267 Danijel Bratina, University of Primorska, Slovenia Armand Faganel, University of Primorska, Slovenia Compilation of References ..............................................................................................................286 About the Contributors ...................................................................................................................322 Index ...................................................................................................................................................329 Detailed Table of Contents Foreword ............................................................................................................................................xiii Preface .................................................................................................................................................xv Acknowledgment .................................................................................................................................xx Section 1 Theoretical and Conceptual Approach to Early Warning Systems This section introduces basic principals of data mining, early warning systems, risk evaluation and detection in multi dimensional structure. Chapter 1 Overview of Knowledge Discovery in Databases Process and Data Mining for Surveillance Technologies and EWS ...........................................................................................................................1 Inci Batmaz, Middle East Technical University, Turkey Guser Koksal, Middle East Technical University, Turkey This chapter presents a formal deinition of knowledge discovery in databases (KDD) process and DM, their functions and methods, used or likely to be used in early warning systems. It also presents a brief survey of overview and application papers and software in the early warning system literature. Chapter 2 Data Mining and Privacy Protection .....................................................................................................31 Armand Faganel, University of Primorska, Slovenia Danijel Bratina, University of Primorska, Slovenia This chapter introduces the comparison of laws on data privacy protection. In this chapter, the compari- son of EU comprehensive laws model and US sectoral laws model that arise from different cultural and historical background have been presented. Chapter 3 On the Nature and Scales of Statistical Estimations Divergence and its Linkage with Statistical Learning ................................................................................................................................................52 Vassiliy Simchera, Research Institute of Statistics (Rosstat), Russia Ali Serhan Koyuncugil, Capital Markets Board of Turkey, Turkey This chapter deals with the divergence in statistical estimations from statistical learning point of view. In this chapter some of the approaches presented which open possibilities for the reduction of the huge gaps in modern statistical estimations of the same phenomena and its linkage with statistical learning. In addition, a solution has been given for create a single number of standards of economical information and economical indicators based on total conventional decisions via data warehouse and data mining logic for clean, comparable and standardized deinitions instead of directed ones for acceptable estima- tions and reliable conclusions. Chapter 4 Black-Necked Swans and Active Risk Management ............................................................................64 Tze Leung Lai, Stanford University, USA Bo Shen, Stanford University, USA This chapter gives a review of recent developments in sequential surveillance and modeling of default probabilities of corporate and retail loans, and relates them to the development of early warning or quick detection systems for managing the risk associated with the so-called “black swans” or their close relatives, the black-necked swans. Section 2 Early Warning Systems for Finance This section introduces early warning systems for detection and prevention of inancial crisis, stock market crashes and bankruptcies. Chapter 5 Financial Early Warning System for Risk Detection and Prevention from Financial Crisis ................76 Nermin Ozgulbas, Baskent University, Turkey Ali Serhan Koyuncugil, Capital Markets Board of Turkey, Turkey This chapter introduces a inancial early warning system that all enterprises in need which detects signs to warn against risks and prevent from inancial crisis. For this purpose, data of SMEs listed in Istanbul Stock Exchange (ISE) is processed with Chi-Square Automatic Interaction Detector (CHAID) Decision Tree Algorithm. By using this EWS, the risk proiles and risk signals have been determined for risk detection and road maps have been developed for risk prevention from inancial crisis. Chapter 6 Designing an Early Warning System for Stock Market Crashes by Using ANFIS ............................109 Murat Acar, ISE Settlement and Custody Bank Inc., Turkey Dilek Karahoca, Bahcesehir University, Turkey Adem Karahoca, Bahcesehir University, Turkey This chapter focuses on building a inancial early warning system (EWS) to predict stock market crash- es by using stock market volatility and rising stock prices. The relation of stock market volatility with stock market crashes is analyzed empirically. Also, Istanbul Stock Exchange (ISE) national 100 index data used to achieve better results from the view point of modeling purpose. Adaptive neuro fuzzy in- ference system (ANFIS) model was proposed to forecast stock market crashes eficiently. Also, ANFIS was explained in detail as a training tool for the EWS. Chapter 7 Bankruptcy Prediction by Supervised Machine Learning Techniques: A Comparative Study ...........128 Chih-Fong Tsai, National Central University, Taiwan Yu-Hsin Lu, National Chung Cheng University, Taiwan Yu-Feng Hsu, National Sun Yat-Sen University, Taiwan This chapter introduces a comparison of bankruptcy prediction performances of new and advanced machine learning and statistical techniques. The aim of this chapter is to compare two different machine learning techniques, one statistical approach, two types of classiier ensembles, and three stacked gen- eralization classiiers over three related datasets. Chapter 8 Data Mining Used for Analyzing the Bankruptcy Risk of the Romanian SMEs ................................144 Laura Giurca Vasilescu, University of Craiova, Romania Marian Siminica, University of Craiova, Romania Cerasela Pirvu, University of Craiova, Romania Costel Ionascu University of Craiova, Romania Anca Mehedintu, University of Craiova, Romania This chapter introduces a surveillance system for bankruptcy risk of Romanian SMEs. In this context, starting from the necessity to design an early warning system, authors elaborated a new model for analysis of bankruptcy risk for the Romanian SMEs that combine two main categories of indicators: inancial ratios and non-inancial indicators. Analysis based on data mining techniques (CHAID) in order to identify the irms’ categories accordingly to the bankruptcy risk levels. Through the proposed analysis model authors tried to offer a real surveillance system for the Romanian SMEs which can al- low an early signal regarding the bankruptcy risk.
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