  Evaluating new techniques on realistic datasets plays a crucial role in the
development of ML research and its broader adoption by practitioners. In recent
years, there has been a significant increase of publicly available unstructured
data resources for computer vision and NLP tasks. However, tabular data --
which is prevalent in many high-stakes domains -- has been lagging behind. To
bridge this gap, we present Bank Account Fraud (BAF), the first publicly
available privacy-preserving, large-scale, realistic suite of tabular datasets.
The suite was generated by applying state-of-the-art tabular data generation
techniques on an anonymized,real-world bank account opening fraud detection
dataset. This setting carries a set of challenges that are commonplace in
real-world applications, including temporal dynamics and significant class
imbalance. Additionally, to allow practitioners to stress test both performance
and fairness of ML methods, each dataset variant of BAF contains specific types
of data bias. With this resource, we aim to provide the research community with
a more realistic, complete, and robust test bed to evaluate novel and existing
methods.
