A recommendation system for car insurance
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| Tag | 1 | 2 | Valor |
|---|---|---|---|
| LDR | 00000cab a2200000 4500 | ||
| 001 | MAP20220013222 | ||
| 003 | MAP | ||
| 005 | 20220504104152.0 | ||
| 008 | 220504e20201207esp|||p |0|||b|spa d | ||
| 040 | $aMAP$bspa$dMAP | ||
| 084 | $a322 | ||
| 245 | 0 | 0 | $aA recommendation system for car insurance$cLaurent Lesage...[et.al] |
| 520 | $aWe construct a recommendation system for car insurance, to allow agents to optimize up-selling performances, by selecting customers who are most likely to subscribe an additional cover. The originality of our recommendation system is to be suited for the insurance context. While traditional recommendation systems, designed for online platforms (e.g. e-commerce, videos), are constructed on huge datasets and aim to suggest the next best offer, insurance products have specific properties which imply that we must adopt a different approach. Our recommendation system combines the XGBoost algorithm and the Apriori algorithm to choose which customer should be recommended and which cover to recommend, respectively. It has been tested in a pilot phase of around 150 recommendations, which shows that the approach outperforms standard results for similar up-selling campaigns. | ||
| 650 | 4 | $0MAPA20080603779$aSeguro de automóviles | |
| 650 | 4 | $0MAPA20080573577$aRecomendaciones | |
| 773 | 0 | $wMAP20220007085$g07/12/2020 Volúmen 10 - Número 2 - diciembre 2020 , p. 377-398$tEuropean Actuarial Journal$dCham, Switzerland : Springer Nature Switzerland AG, 2021-2022 |