Claim Models: Granular Forms and Machine Learning Forms

• Autore principale: Taylor, Greg (auth)
• Natura: Elettronico Capitolo di libro
• Lingua: inglese
• Pubblicazione: MDPI - Multidisciplinary Digital Publishing Institute 2020
• Soggetti: Pharmaceutical technology; n/a; granular models; neural networks; actuarial; payments per claim incurred; risk pricing; machine learning; claim watching; loss reserving; gradient boosting; predictive modeling; classification and regression trees; individual models; individual claims reserving
• Accesso online: DOAB: download the publication; DOAB: description of the publication

This collection of articles addresses the most modern forms of loss reserving methodology: granular models and machine learning models. New methodologies come with questions about their applicability. These questions are discussed in one article, which focuses on the relative merits of granular and...