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Allianz: Improving P&L through Machine Learning

Carl-Erik Heyvaert, Viola Darmawan, Kristof Stouthuysen, Tim Verdonck, Christopher Grumiau, Thoppan Mohanchandralal Sudaman

商品編號:W27373
出版日期:2022/12/21
再版日期:
商品來源:
商品主題:Accounting
商品類型:Case (Field)
涵蓋議題:Disability claim;Machine Learning;P&L Optimization
難易度:5 - MBA/Postgraduate
內容長度:6 頁
地域:Belgium
產業:Finance and Insurance
事件年度:2019

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.

教學手冊:Allianz: Improving P&L through Machine Learning - Teaching Note
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