From unstructured data and word vectorization to meaning: text mining in insurance

Diego Zappa, Mattia Borrelli

Risultato della ricerca: Contributo in libroChapter

Abstract

By exploiting Natural Language Processing techniques we aim at grasping latent information useful for insurance to tune policy premiums. By using a large set of police reports, we classify medical and police reports based upon the profile of the people involved and according to the relevance of their content. At a second step, we match these risks with the customer profiles of a company in order to add new and relevant risk covariates to improve the precision and the determination of policy premiums.
Lingua originaleEnglish
Titolo della pubblicazione ospiteCladag 2017, Book of Short Papers
EditorF., Mola, F., Zenga, M. Greselin
Pagine1-6
Numero di pagine6
Stato di pubblicazionePubblicato - 2017

Keywords

  • Natural language processing
  • Text mining
  • policy premiums

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