Machine Learning in Insurance

ISBN 10: 3039364472 / ISBN 13: 9783039364473

Published by MDPI AG, 2020

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - Machine learning is a relatively new field, without a unanimous definition. In many ways, actuaries have been machine learners. In both pricing and reserving, but also more recently in capital modelling, actuaries have combined statistical methodology with a deep understanding of the problem at hand and how any solution may affect the company and its customers. One aspect that has, perhaps, not been so well developed among actuaries is validation. Discussions among actuaries' 'preferred methods' were often without solid scientific arguments, including validation of the case at hand. Through this collection, we aim to promote a good practice of machine learning in insurance, considering the following three key issues: a) who is the client, or sponsor, or otherwise interested real-life target of the study b) The reason for working with a particular data set and a clarification of the available extra knowledge, that we also call prior knowledge, besides the data set alone. c) A mathematical statistical argument for the validation procedure.

Seller Inventory # 9783039364473

Bibliographic Details

• Title: Machine Learning in Insurance
• Publisher: MDPI AG
• Publication Date: 2020
• Language: English
• ISBN 10: 3039364472
• ISBN 13: 9783039364473
• Binding: Buch
• Condition: Neu
• Dimensions: 250W x 175H x 21D millimeters 250 millimeters width by 175 millimeters height by 21 millimeters depth
• Weight: 850 grams

About this title

Synopsis

Machine learning is a relatively new field, without a unanimous definition. In many ways, actuaries have been machine learners. In both pricing and reserving, but also more recently in capital modelling, actuaries have combined statistical methodology with a deep understanding of the problem at hand and how any solution may affect the company and its customers. One aspect that has, perhaps, not been so well developed among actuaries is validation. Discussions among actuaries' "preferred methods" were often without solid scientific arguments, including validation of the case at hand. Through this collection, we aim to promote a good practice of machine learning in insurance, considering the following three key issues: a) who is the client, or sponsor, or otherwise interested real-life target of the study? b) The reason for working with a particular data set and a clarification of the available extra knowledge, that we also call prior knowledge, besides the data set alone. c) A mathematical statistical argument for the validation procedure.