Detection of Automobile Insurance Fraud With Discrete Choice Models and Misclassified Claims

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Applied Predictive Modeling (2013, background): “, Artis et al. 2002). Basically, instead of having the model deal with the imbalance, we can attempt to balance the class frequencies. Taking this approach eliminates the fundamental imbalance issue that plagues model training. However, if the training set is sampled to be balanced, the test set sho”
Data mining for credit card fraud: A comparative study (2011, background): “Prior work in related areas has estimated logit models of fraudulent claims in insurance, food stamp programs, and so forth [2] [7] [23] [41].”
Fraud detection system: A survey (2016, background): “Artís et al. (2002) and Dionne (1997) proposed a logit model for fraud detection, taking into account misclassi ﬁ cation of the type of claim.”