Model behaviour for insurance risk prediction : destroying the myth
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| Tag | 1 | 2 | Value |
|---|---|---|---|
| LDR | 00000cam a22000004b 4500 | ||
| 001 | MAP20190001915 | ||
| 003 | MAP | ||
| 005 | 20190123132016.0 | ||
| 008 | 190122e20190111gbr|||| ||| ||eng d | ||
| 040 | $aMAP$bspa$dMAP | ||
| 084 | $a7 | ||
| 245 | 1 | 0 | $aModel behaviour for insurance risk prediction$b: destroying the myth$cInsurance Post |
| 260 | $aLondon$bInsurance Post$c2019 | ||
| 520 | $aIn a highly competitive market, it is important that insurers maximise their data models to create more intelligible insights. Only then, argues Alan O'Loughlin, head of analytics and statistical modelling at Lexis Nexis Risk Solutions, will they gain a strategic advantage over competitors | ||
| 650 | 4 | $0MAPA20080591182$aGerencia de riesgos | |
| 650 | 4 | $0MAPA20080563790$aPredicciones | |
| 650 | 4 | $0MAPA20080592059$aModelos predictivos | |
| 650 | 4 | $0MAPA20080590567$aEmpresas de seguros | |
| 650 | 4 | $0MAPA20080600389$aComportamiento humano | |
| 773 | 0 | $wMAP20170035732$iBlog$tInsurance post$dLondon : Infopro Digital Insurance Information , 2016-2022 |