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Using Kohonen's self-organizing feature map to uncover automobile bodily injury claims fraud

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Registro MARC
Tag12Valor
LDR  00000nab a2200000 i 4500
001  MAP20071023781
003  MAP
005  20220823211619.0
008  951228s1995 usa|||| | |00010|eng d
040  ‎$a‎MAP‎$b‎spa
084  ‎$a‎322
1001 ‎$0‎MAPA20080260019‎$a‎Brockett, Patrick L.
24510‎$a‎Using Kohonen's self-organizing feature map to uncover automobile bodily injury claims fraud‎$c‎Patrick L. Brockett, Xiaohua Xia, Richard A. Derrig
260  ‎$a‎[S.l.‎$b‎s.n.]‎$c‎1995
300  ‎$a‎31 h.‎$c‎30 cm
500  ‎$a‎Ponencia presentada en American Risk and Insurance Association Annual Meeting, Seattle, 1995
520  ‎$a‎Claims fraud is an increasingly vexing problem confronting the insurance industry. In this empirical study, the authors apply Kohonen's Self-Organizing Feature Map to the classification of automobile bodily injury (BI) claims by the degree of fraud suspicion, each claim being classified into one of four classes with different levels of suspected fraud. Feedforward neural networks and a backpropagation algorithm are used to investigate the validity of the Feature Map approach. A comparative experiment illustrates the potential usefulness of the proposed methodology. They show that this technique performs better than both an insurance adjuster's fraud assessment and an investigator's fraud assessment with respect to consistency and reliability, but that the extent of misclassification of the model remains high on the data sample
65011‎$0‎MAPA20080603779‎$a‎Seguro de automóviles
65011‎$0‎MAPA20080567118‎$a‎Reclamaciones
65011‎$0‎MAPA20080591052‎$a‎Fraude en el seguro
65011‎$0‎MAPA20080594206‎$a‎Valoración de daños
65011‎$0‎MAPA20080586331‎$a‎Métodos analíticos
65011‎$0‎MAPA20080607289‎$a‎Métodos experimentales
65011‎$0‎MAPA20080561864‎$a‎Conferencias
65011‎$0‎MAPA20080550233‎$a‎Congresos
650  ‎$0‎MAPA20080541064‎$a‎Fraude
7001 ‎$0‎MAPA20080059538‎$a‎Xia, Xiaohua
7001 ‎$0‎MAPA20080216450‎$a‎Derrig, Richard A.
7112 ‎$0‎MAPA20090031197‎$a‎American Risk and Insurance Association‎$d‎1995‎$c‎Seattle‎$q‎Annual Meeting