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

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.