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.

传统上，检测欺诈保险索赔依赖于商业规则和专家判断，这使得过程耗时且昂贵。因此，研究人员一直在研究开发高效准确的分析策略来标记可疑索赔。然而，在开发欺诈检测模型时，我们面临几个挑战，如欺诈的罕见性导致高类别不平衡，只有少数索赔得到调查和标记，导致大量未标记数据，以及公开可用数据的缺乏。因此，我们设计了一个模拟机器，用于创建与实际保险欺诈数据集相似的具有网络结构和可用协变量的合成数据。此模拟引擎使研究人员和从业者可以在各种不同的设置中检验多种方法学挑战以及测试他们的保险欺诈检测模型的（开发策略），同时还可以生成大量的合成数据集以评估（高级）机器学习技术的预测性能。

模拟保险欺诈网络数据的引擎

An engine to simulate insurance fraud network data

In this paper, we introduce the Vehicle Claims dataset, consisting of
fraudulent insurance claims for automotive repairs. The data belongs to the
more broad category of Auditing data, which includes also Journals and Network
Intrusion data. Insurance claim data are distinctively different from other
auditing data (such as network intrusion data) in their high number of
categorical attributes. We tackle the common problem of missing benchmark
datasets for anomaly detection: datasets are mostly confidential, and the
public tabular datasets do not contain relevant and sufficient categorical
attributes. Therefore, a large-sized dataset is created for this purpose and
referred to as Vehicle Claims (VC) dataset. The dataset is evaluated on shallow
and deep learning methods. Due to the introduction of categorical attributes,
we encounter the challenge of encoding them for the large dataset. As One Hot
encoding of high cardinal dataset invokes the "curse of dimensionality", we
experiment with GEL encoding and embedding layer for representing categorical
attributes. Our work compares competitive learning, reconstruction-error,
density estimation and contrastive learning approaches for Label, One Hot, GEL
encoding and embedding layer to handle categorical values.

本文介绍了车辆索赔数据集，并提出用于表示分类属性的 GEL 编码和嵌入层。通过比较标签，One Hot 编码，GEL 编码和嵌入层，在标记，重建错误，密度估计和对比学习方法上，评估了该数据集的浅层和深度学习方法。