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    Allianz: Improving P&L through machine learning

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    Author
    Heyvaert, Carl-Erik
    Darmawan, Viola
    Stouthuysen, Kristof
    Verdonck, Tim
    Grumiau, Christopher
    Thoppan Mohanchandralal, Sudaman
    Publication Year
    2022
    Publication Number of pages
    6
    
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    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.
    Keyword
    Machine Learning
    Knowledge Domain/Industry
    Accounting & Finance
    URI
    http://hdl.handle.net/20.500.12127/7193
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