  Traditionally, the detection of fraudulent insurance claims relies on
business rules and expert judgement which makes it a time-consuming and
expensive process (\'Oskarsd\'ottir et al., 2022). Consequently, researchers
have been examining ways to develop efficient and accurate analytic strategies
to flag suspicious claims. Feeding learning methods with features engineered
from the social network of parties involved in a claim is a particularly
promising strategy (see for example Van Vlasselaer et al. (2016); Tumminello et
al. (2023)). When developing a fraud detection model, however, we are
confronted with several challenges. The uncommon nature of fraud, for example,
creates a high class imbalance which complicates the development of well
performing analytic classification models. In addition, only a small number of
claims are investigated and get a label, which results in a large corpus of
unlabeled data. Yet another challenge is the lack of publicly available data.
This hinders not only the development of new methods, but also the validation
of existing techniques. We therefore design a simulation machine that is
engineered to create synthetic data with a network structure and available
covariates similar to the real life insurance fraud data set analyzed in
\'Oskarsd\'ottir et al. (2022). Further, the user has control over several
data-generating mechanisms. We can specify the total number of policyholders
and parties, the desired level of imbalance and the (effect size of the)
features in the fraud generating model. As such, the simulation engine enables
researchers and practitioners to examine several methodological challenges as
well as to test their (development strategy of) insurance fraud detection
models in a range of different settings. Moreover, large synthetic data sets
can be generated to evaluate the predictive performance of (advanced) machine
learning techniques.
