  Credit card fraud has emerged as major problem in the electronic payment
sector. In this survey, we study data-driven credit card fraud detection
particularities and several machine learning methods to address each of its
intricate challenges with the goal to identify fraudulent transactions that
have been issued illegitimately on behalf of the rightful card owner. In
particular, we first characterize a typical credit card detection task: the
dataset and its attributes, the metric choice along with some methods to handle
such unbalanced datasets. These questions are the entry point of every credit
card fraud detection problem. Then we focus on dataset shift (sometimes called
concept drift), which refers to the fact that the underlying distribution
generating the dataset evolves over times: For example, card holders may change
their buying habits over seasons and fraudsters may adapt their strategies.
This phenomenon may hinder the usage of machine learning methods for real world
datasets such as credit card transactions datasets. Afterwards we highlights
different approaches used in order to capture the sequential properties of
credit card transactions. These approaches range from feature engineering
techniques (transactions aggregations for example) to proper sequence modeling
methods such as recurrent neural networks (LSTM) or graphical models (hidden
markov models).
