Allianz: Improving P&L through machine learning
Heyvaert, Carl-Erik Darmawan, Viola Stouthuysen, Kristof Verdonck, Tim Grumiau, Christopher Thoppan Mohanchandralal, Sudaman
Heyvaert, Carl-Erik
Darmawan, Viola
Stouthuysen, Kristof
Verdonck, Tim
Grumiau, Christopher
Thoppan Mohanchandralal, Sudaman
Citations
Altmetric:
Publication Type
Editor
Supervisor
Publication Year
2022
Journal
Book
Publication Volume
Publication Issue
Publication Begin page
Publication End page
Publication NUmber of pages
6
Collections
Abstract
During an Allianz Benelux SA (Allianz) board meeting held in early 2019, Allianz’s chief financier officer (CFO) had a profound discussion with Allianz’s chief data and analytics officer (CDAO) on improving the company’s profit and loss (P&L) statement by targeting problematic cases among disability claims related to Allianz’s life insurance product. It appeared that certain claims had very long durations, leading to recurrent payouts surpassing the total amount of premiums. Consequently, there were too many claims that could translate into future losses. If this phenomenon persisted, Allianz could lose millions of dollars in revenues. Therefore, the CFO contacted the CDAO and his data office and requested that the team identify the client segments in which the most problematic cases of disability claims occurred. Additionally, the CFO wanted the data office to build a predictive model that could estimate the duration of a claim, to adapt the premium coverage to specific customer segments.
Research Projects
Organizational Units
Journal Issue
Keywords
Machine Learning