Van der Schraelen, LennertWillems, EmmaStouthuysen, KristofVerdonck, TimGrumiau, ChristopherThoppan Mohanchandralal, Sudaman2023-03-032023-03-032022http://hdl.handle.net/20.500.12127/7194The case is suitable for participants who want to enhance their machine learning and data science skills by solving a real-world application in the insurance industry. By working through the case and assignment questions, students will have the opportunity to do the following: Learn about direct debit and see how it can improve cash flow. Gain preliminary insights using exploratory data analysis. Train and interpret supervised learning algorithms in a binary classification problem. Apply unsupervised techniques such as clustering and gain insights into the data. Obtain relevant business insights from the data and determine how they can be used to drive intelligent decision-making.In January 2021, the chief data and analytics officer (CDAO) at Allianz Benelux SA (Allianz) spotted a possible opportunity to optimize cash flow with direct debit. Direct debit was a pre-authorized financial transaction between two parties where the amount due was directly and automatically collected from the payer’s bank account. Direct debit would allow Allianz to shorten payment processes, reduce risks by anticipating payments, and improve customer loyalty. Despite the clear advantages of direct debit for both clients and insurers, only a few of Allianz’s clients were currently making use of direct debit. It was not clear what drove Allianz’s customers or brokers to implement direct debit. This was where the CDAO and his data office team came in. The data office possessed a large amount of data on Allianz’s property and casualty insurance contracts and customers. Now the team needed to investigate how this data could be leveraged to determine the value drivers and develop a strategy to convert more clients to direct debit payments.enDebit PaymentsW27310286357286362119751220053