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In this notebook we take a look at a relevant project that is frequently encountered by insurers: Fraud Detection. For this purpose we use a car data set from a public source and will show the necessary steps to establish an automated fraud detection.

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DeutscheAktuarvereinigung/Data_Science_Challenge_2020_Betrugserkennung

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Data Science Challenge 2020: Fraud Detection

The given notebook has been created by Lukas Nolte, who won the individual competition of the Data Science Challenge 2020 of the DAV.

In this notebook we take a look at a relevant project that is frequently encountered by insurers: Fraud Detection. For this purpose we use a car data set from a public source and will show the necessary steps to establish an automated fraud detection.


The German Association of Actuaries (Deutsche Aktuarvereinigunge.V., DAV) is the professional representation of all actuaries in Germany. It was founded in 1993 and has more than 5,400 members today. More than 700 members are involved in thirteen committees and in over 60 working groups as a voluntary commitment.

The Data Science Challenge is an initiative of the Actuarial Data Science Committee of the DAV to encourage the engagement with machine learning and data science within the insurance industry

Please note that the repositories provided on GitHub are published by the DAV. The content of linked websites is the sole responsibility of their operators. The DAV is not responsible for the code and data linked to Kaggle.com and referred to in the repositories. These reflect the individual opinion of each user on Kaggle.

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In this notebook we take a look at a relevant project that is frequently encountered by insurers: Fraud Detection. For this purpose we use a car data set from a public source and will show the necessary steps to establish an automated fraud detection.

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