Method for selection of motor insurance fraud management system components based on business performance

References

• Abidogun , O. A. 2005 . Data Mining, Fraud Detection and Mobile Telecommunications: Call Pattern Analysis with Unsupervised Neural Networks : Master thesis University of the Western Cape .
   Google Scholar
• Aggarwald , C. C. ; Yu , P. S. 2005 . An effective and efficient algorithm for high-dimensional outlier detection , The VLDB Journal 14 : 211 – 221 . doi: 10.1007/s00778-004-0125-5
   Google Scholar
• Artis , M. ; Ayuso , M. ; Guillen , M. 1999 . Modeling Different Types of Automobile Insurance Fraud Behavior in the Spanish Market , Insurance: Mathematics and Economics 24 : 67 – 81 . doi: 10.1016/S0167-6687(98)00038-9
   Web of Science ®Google Scholar
• Artís , M. ; Ayuso , M. ; Guillén , M. 2002 . Detection of Automobile Insurance Fraud with Discrete Choice Models and Misclassified Claims , The Journal of Risk and Insurance 63 3 : 325 – 340 .
   Google Scholar
• Barbará , D. ; Domeniconi , C. ; Rogers , J. P. 2006 . Detecting outliers using transduction and statistical testing , in Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
   Google Scholar
• Barse , E. L. ; Kvarnström , H. ; Jonsson , E. 2003 . Synthesizing Test Data for Fraud Detection Systems in , Proceedings of the 19th Annual Computer Security Applications Conference (ACSAC ‘03) , Las Vegas, Nevada, USA. IEEE Computer Society , Los Alamitos, , CA, USA , 384 – 395 .
   Google Scholar
• Becker , D. ; Kessler , D. ; McClellan , M. 2005 . Detecting Medicare abuse , Journal of Health Economics 24 : 189 – 210 . doi: 10.1016/j.jhealeco.2004.07.002
   PubMed Web of Science ®Google Scholar
• Bermudez , L. ; Perez , J. M. ; Ayuso , M. ; Gomez , E. ; Vazquez , F. J. 2007 . A Bayesian dichotomous model with asymmetric link for fraud in insurance , Insurance: Mathematics and Economics 42 2 : 779 – 786 . doi: 10.1016/j.insmatheco.2007.08.002
   Google Scholar
• Bolton , R. J. ; Hand , D. J. 2002 . Statistical Fraud Detection: A Review , Statistical Science 17 : 235 – 249 . doi: 10.1214/ss/1042727940
   Web of Science ®Google Scholar
• Bonchi , F. ; Giannotti , F. ; Mainetto , G. ; Pedreschi , I. D. 1999 . A classification-based methodology for planning audit strategies in fraud detection , in Proceedings of the fifth ACM SIGKDD international conference on knowledge discovery and data mining .
   Google Scholar
• Bond , E. W. ; Crocker , K. J. 1997 . Hardball and the soft touch: The economics of optimal insurance contracts with costly state verification and endogenous monitoring costs , Journal of Public Economics 63 : 239 – 264 . doi: 10.1016/S0047-2727(96)01594-0
   Google Scholar
• Bordoni , S. ; Facchinetti , G. 2001 . Insurance Fraud Evaluation: A Fuzzy Expert System in , FUZZ-IEEE 1491 – 1494 .
   Google Scholar
• Boukerche , A. ; Juc , K. R. L. ; Sobral , J.; B. ; Notare , M. S. M. A. 2004 . An artificial immune based intrusion detection model for computer and telecommunication systems , Parallel Computing 30 : 629 – 646 . doi: 10.1016/j.parco.2003.12.008
   Google Scholar
• Brockett , P. L. ; Derrig , R. A. ; Golden , L. L. ; Levine , A. ; Alpert , M. 2002 . Fraud Classification using Principal Component Analysis of RIDITs , The Journal of Risk and Insurance 69 3 : 341 – 371 . doi: 10.1111/1539-6975.00027
   Web of Science ®Google Scholar
• Cahill , M. H. ; Lambert , D. ; Pinheiro , J. C. ; Sun , D. X. 2002 . Detecting Fraud in Real World . Handbook of Massive Data Sets , Kluwer Academic Publishers , 913 – 930 .
   Google Scholar
• Caron , L. and Dionne , G. 1999 . “ Insurance Fraud Estimation; More Evidence From the Quebec Automobile Insurance Industry ” . In Automobile Insurance: Road Safety, Nexv Drivers, Risks, Insurance Fraud and Regulation , Edited by: Dionne , G. and Laberge-Nadeau , C. 175 – 182 . Kluwer .
   Google Scholar
• Chang , R. ; Ghoniem , M. ; Kosara , R. ; Ribarsky , W. ; Yang , J. ; Sumak , E. ; Ziemkiewicz , C. ; Kern , D. ; Sudjianto , A. 2007 . WireVis: Visualization of Categorical, Time-Varying Data From Financial Transactions , in IEEE Symposium on Visual Analytics Science and Technology 2007 . Sacramento . California, , USA , IEEE Computer Society, Los Alamitos, CA, USA , 155 – 162 .
   Google Scholar
• Cox , K. C. ; Eick , S. G. ; Wills , G. J. ; Brachman , R. J. 1997 . Visual data mining: recognizing telephone calling fraud , Data Mining and Knowledge Discovery 1 2 : 225 – 231 . doi: 10.1023/A:1009740009307
   Web of Science ®Google Scholar
• Curet , O. ; Jackson , M. ; Tarar , A. 1996 . Designing and evaluating a case-based learning and reasoning agent in unstructured decision making , in IEEE International Conference on Systems, Man, and Cybernetics, 1996 , Beijing , China . IEEE Computer Society , Los Alamitos, , CA, USA, 2487 – 2492 .
   Google Scholar
• Derrig , R. A. ; Weisberg , H. I. ; Chen , X. 1994 . Behavioral Factors and Lotteries Under No-Fault with a Monetary Threshold: A Study of Massachusetts Automotive Claims , Journal of Risk and Insurance 61 2 : 245 – 275 . doi: 10.2307/253710
   Web of Science ®Google Scholar
• Derrig , R. A. 2002 . Insurance Fraud , The Journal of Risk and Insurance 69 3 : 271 – 287 . doi: 10.1111/1539-6975.00026
   Web of Science ®Google Scholar
• Dionne , G. ; Giuliano , F. ; Picard , P. 2009 . Optimal Auditing with Scoring: Theory and Application to Insurance Fraud , Management Science 55 1 : 58 – 70 . doi: 10.1287/mnsc.1080.0905
   Google Scholar
• Eriksson , H. E. ; Penker , M. 2000 . Business Modelling with UML : Business Patterns at Work. John Wiley & Sons .
   Google Scholar
• Fawcett , T. ; Provost , F. 1997 . Adaptive Fraud Detection . Data Mining and Knowledge Discovery, Kluwer .
   Google Scholar
• Fawcett , T. ; Provost , F. 1999 . Activity monitoring: noticing interesting changes in behaviour , in Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, 1999 , San Diego . California, , USA , ACM, New York, USA 53 – 62 .
   Google Scholar
• Furlan , Š. ; Bajec , M. 2009 . Praksa slovenskih avtomobilskih zavarovalnic pri razreševanju zavarovalniških goljufij Zavarovalniški horizonti 5 3 : 29 – 41 .
   Google Scholar
• Garner , B. ; Chen , F. 1994 . Hypothesis Generation Paradigm for Fraud Detection , in Proceedings of 1994 TENCON ‘94. IEEE Region 10's Ninth Annual International Conference. Theme: Frontiers of Computer Technology , Singapore . IEEE Computer Society , Los Alamitos, , CA, USA , 197 – 201 .
   Google Scholar
• Gudas , S. 2009 . Enterprise knowledge modelling: domains and aspects , Technological and Economic Development of Economy 15 2 : 281 – 293 . doi: 10.3846/1392-8619.2009.15.281-293
   Web of Science ®Google Scholar
• Gudmundsson , S. ; Oddsdottir , G. L. ; Runarsson , T. F. ; Sigurdsson , S. ; Kristjansson , E. 2010 . Detecting fraudulent whiplash claims by support vector machines , Biomedical Signal Processing and Control 5 4 : 311 – 317 . doi: 10.1016/j.bspc.2010.05.004
   Google Scholar
• Hauser , J. R. ; Clausing , D. 1988 . The House of Quality , Harvard Business Review 3 : 63 – 73 .
   Google Scholar
• He , H. ; Wang , J. ; Graco , W. ; Hawkins , S. 1997 . Application of Neural Networks to Detection of Medical Fraud , Expert Systems with Applications 13 4 : 329 – 336 . doi: 10.1016/S0957-4174(97)00045-6
   Web of Science ®Google Scholar
• Hollien , H. ; Geison , L. ; Hicks , J. W. 1987 . Voice stress analysis and lie detection , Journal of Forensic Sciences 32 2 : 405 – 418 .
   Google Scholar
• Hyman , D. A. 2002 . HIPAA and Health Care Fraud: An Empirical Perspective , Cato Journal 22 1 : 151 – 178 .
   Google Scholar
• Jonas , J. 2006 . Threat and fraud intelligence, Las Vegas style , IEEE. Security and Privacy 4 6 : 28 – 34 . doi: 10.1109/MSP.2006.169
   Google Scholar
• Jou , S. ; Hebenton , B. 2007 . Insurance fraud in Taiwan: Reflections on regulatory effort and criminological complexity , International Journal of the Sociology of Law 35 : 127 – 142 . doi: 10.1016/j.ijsl.2007.04.002
   Google Scholar
• Kalibatiene , D. ; Vasilecas , O. 2010 . Ontology axioms for the implementation of business rules , Technological and Economic Development of Economy 16 3 : 471 – 486 . doi: 10.3846/tede.2010.29
   Web of Science ®Google Scholar
• Ko , R. K. L. 2009 . A Computer Scientist's Introductory Guide to Business Process Management (BPM) , ACM Crossroads 15 4 : 11 – 18 . doi: 10.1145/1558897.1558901
   Google Scholar
• Lavbič , D. ; Vasilecas , O. ; Rupnik , R. 2010 . Ontology based multi-agent system to support business users and management , Technological and Economic Development of Economy 16 2 : 327 – 347 . doi: 10.3846/tede.2010.21
   Web of Science ®Google Scholar
• Lundin , E. ; Kvarnström , H. ; Jonsson , E. 2002 . A Synthetic Fraud Data Generation Methodology , in Proceedings of the 4th International Conference on Information and Communications Security (ICICS ‘02) . Springer-Verlag, UK , London, , UK 265 – 277 .
   Google Scholar
• Major , J. ; Riedinger , D. R. 2002 . EFD: A Hybrid Knowledge/Statistical-Based System for the Detection of Fraud , The Journal of Risk and Insurance 69 3 : 309 – 324 . doi: 10.1111/1539-6975.00025
   Google Scholar
• Matook , S. ; Indulska , M. 2009 . Improving the quality of process reference models: A quality function deployment-based approach , Decision Support Systems 47 : 60 – 71 . doi: 10.1016/j.dss.2008.12.006
   Web of Science ®Google Scholar
• Mattison , R. 2005 . The Telco Revenue Assurance Handbook . XiT Press , Oakwood Hills, , Illinois, USA .
   Google Scholar
• Ngai , E. W. T. ; Yong , Hu ; Wong , Y. H. Yijun Chen ; Xin Sun. 2010 . The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature , Decision Support Systems . doi: 10.1016/j.dss.2010.08.006
   Google Scholar
• Nguyen , T. M. ; Schiefer , J. ; Tjoa , A. M. 2005 . Sense & Response Service Architecture (SARESA): An Approach towards a Real-time Business Intelligence Solution and its use for a Fraud Detection Application , in Proceedings of the 8th ACM international workshop on Data warehousing and OLAP (DOLAP ‘05) . New York, , USA , ACM, New York 77 – 86 .
   Google Scholar
• NHS . 2001 . Countering Fraud in the NHS [online] National Health Service (Published 2001), [accessed 28 May 2010]. Available from Internet: <http://www.nhsbsa.nhs.uk/fraud> .
   Google Scholar
• NHS . 2006a . Creating an Anti-Fraud Culture: Discovering the Public's Perception of Fraud in the NHS [online] National Health Service (Published 2006), [accessed 28 May 2010]. Available from Internet: <http://www.nhsbsa.nhs.uk/fraud> .
   Google Scholar
• NHS . 2006b . The International Fraud and Corruption Report: A study of selected countries [online] National Health Service (Published 2006), [accessed 28 May 2010]. Available from Internet: <http://www.nhsbsa.nhs.uk/fraud> .
   Google Scholar
• NHS . 2007 . Countering Fraud in the NHS: Applying Appropriate Sanctions Consistently [online] National Health Service (Published 2007), [accessed 28 May 2010]. Available from Internet: <http://www.nhsbsa.nhs.uk/fraud> .
   Google Scholar
• Peng , F. ; McCallum , A. 2006 . Information extraction from research papers using conditional random fields , Information Processing and Management 42 4 : 963 – 979 . doi: 10.1016/j.ipm.2005.09.002
   Google Scholar
• Peng , Y. ; Kou , G. ; Sabatka , A. ; Chen , Z. ; Khazanchil , D. ; Shi , Y. 2006 . Application of Clustering Methods to Health Insurance Fraud Detection , in 2006 International Conference on Service Systems and Service Management , Troyes, France. Institute of Information Science, Technology & Engineering , Nebraska University , Omaha, NE , 116 – 120 .
   Google Scholar
• Poibeau , T. ; Kosseim , L. 2001 . Proper-name Extraction from Non-Journalistic Texts , Language and Computers 37 : 144 – 157 .
   Google Scholar
• Popowich , F. 2005 . Using Text Mining and Natural Language Processing for Health Care Claims Processing , in SIGKDD Explorations 7 1 : 59 – 66 .
   Google Scholar
• Phua , C. ; Alahakoon , D. ; Lee , V. 1998 . Minority Report in Fraud Detection: Classification of Skewed Data , SIGKDD Exploration 6 1 : 50 – 59 .
   Google Scholar
• Phua , C. ; Lee , V. ; Smith , K. ; Gayler , R. 2005 . A Comprehensive Survey of Data Mining-based Fraud Detection Research , Artificial Intelligence Review .
   Google Scholar
• Radcliffe , J. G. Y. 1995 . The Insurance Industry's Use of Database to Prevent and Detect Fraud, and Improve Recoveries , in European Convention on Security and Detection , IEE , Brighton, , UK , 216 – 224 .
   Google Scholar
• Schiller , J. 2006 . The Impact of Insurance Fraud Detection Systems , Journal of Risk and Insurance 73 3 : 421 – 438 . doi: 10.1111/j.1539-6975.2006.00182.x
   Google Scholar
• Selinšek , L. 2004 . Kazenskopravni vidiki zavarovalniških goljufij – nekatera izhodišča , Goljufije v zavarovalništvu , 89 – 106 .
   Google Scholar
• Silverstone , H. ; Davia , H. R. 2005 . Fraud 101: Techniques and Strategies for Detection . 2nd ed. Wiley .
   Google Scholar
• Sokol , L. ; Garcia , B. ; West , M. ; Rodriguez , J. ; Johnson , K. 2001 . Precursory Steps to Mining HCFA Health Care Claims , in Proceedings of the 34th Hawaii International Conference on System Sciences . IEEE Computer Society, Los Alamitos, CA, USA , Hawaii, , USA , 6019 – 6029 .
   Google Scholar
• Stempel , J. W. 2002 . Recent Court Decisions , The Journal of Risk and Insurance 69 3 : 245 – 257 . doi: 10.1111/1539-6975.00006_2
   Google Scholar
• Sternberg , M. ; Reynolds , R. G. 1997 . Using Cultural Algorithms to Support Re-Engineering of Rule-Based Expert Systems in Dynamic Performance Environments: A Case Study in Fraud Detection , IEEE Transactions on Evolutionary Computation 1 4 : 225 – 243 . doi: 10.1109/4235.687883
   Google Scholar
• Stolfo , S. J. ; Fan , W. ; Lee , W. 2000 . Cost-based modeling for fraud and intrusion detection: results fromthe JAM project , in DARPA Information Survivability Conference and Exposition (DISCEX'00) . South Carolina, , USA , IEEE Computer Society, Los Alamitos, CA, USA , 130 – 144 .
   Google Scholar
• Sun , Z. ; Finnie , G. 2004 . Experience based reasoning for recognising fraud and deception , in Proceedings of the Fourth International Conference on Hybrid Intelligent Systems (HIS'04) , . Kitakyushu, , Japan IEEE Computer Society, Los Alamitos, CA, USA , 80 – 85 .
   Google Scholar
• Tennyson , S. ; Salsas-Forn , P. 2002 . Claims Auditing in Automobile Insurance: Fraud Detection and Deterrence Objectives , The Journal of Risk and Insurance 69 3 : 289 – 308 . doi: 10.1111/1539-6975.00024
   Web of Science ®Google Scholar
• Tie , R. 2010 . SAR s can Help Put Fraudsters Behind Bars 1 . Fraud Magazine .
   Google Scholar
• TMF , 2006 . Revenue Assurance Guidebook [pdf] . TeleManagement Forum Revenue Assurance Technical Team .
   Google Scholar
• Tuyls , K. 2000 . Machine Learning Techniques for Fraud Detection . Master thesis, VUB .
   Google Scholar
• Viaene , S. ; Derrig , R. A. ; Baesens , B. ; Dedene , G. 2002 . A Comparison of State-of-the-Art Classification Techniques for Expert Automobile Insurance Claim Fraud Detection , The Journal of Risk and Insurance 69 3 : 373 – 421 . doi: 10.1111/1539-6975.00023
   Web of Science ®Google Scholar
• Viaene , S. ; Derrig , R. A. ; Dedene , G. 2004 . A Case Study of Applying Boosting Naive Bayes to Claim Fraud Diagnosis , in IEEE Transactions on Knowledge and data Engineering 16 5 : 612 – 620 .
   Google Scholar
• Viaene , S. ; Dedene , G. ; Derrig , R. A. 2005 . Auto claim fraud detection using Bayesian learning neural networks , Expert Systems with Applications 29 : 653 – 666 . doi: 10.1016/j.eswa.2005.04.030
   Google Scholar
• Viaene , S. ; Ayuso , M. ; Guillen , M. ; Gheel , D. V. ; Dedene , G. 2007 . Strategies for Detecting Fraudulent Claims in the Automobile Insurance Industry , European Journal of Operational Research 176 1 : 565 – 583 . doi: 10.1016/j.ejor.2005.08.005
   Google Scholar
• Weisberg , H. I. ; Derrig , R. A. 1998 . Quantitative Methods for Detecting Fraudulent Automobile Bodily Injury Claims , Risques 35 : 75 – 101 .
   Google Scholar
• Xing , D. ; Girolami , M. 2007 . Employing Latent Dirichlet Allocation for Fraud Detection in Telecommunications , Pattern Recognition Letters 28 13 : 1818 – 1824 .
   Google Scholar
• žagar , T. 2008 . Doprinos vizualizacije pri bolju z goljufijami v telefonskih sistemih . Masters thesis , University of Ljubljana .
   Google Scholar