  With the increase of credit card usage, the volume of credit card misuse also
has significantly increased. As a result, financial organizations are working
hard on developing and deploying credit card fraud detection methods, in order
to adapt to ever-evolving, increasingly sophisticated defrauding strategies and
identifying illicit transactions as quickly as possible to protect themselves
and their customers. Compounding on the complex nature of such adverse
strategies, credit card fraudulent activities are rare events compared to the
number of legitimate transactions. Hence, the challenge to develop fraud
detection that are accurate and efficient is substantially intensified and, as
a consequence, credit card fraud detection has lately become a very active area
of research. In this work, we provide a survey of current techniques most
relevant to the problem of credit card fraud detection. We carry out our survey
in two main parts. In the first part,we focus on studies utilizing classical
machine learning models, which mostly employ traditional transnational features
to make fraud predictions. These models typically rely on some static physical
characteristics, such as what the user knows (knowledge-based method), or what
he/she has access to (object-based method). In the second part of our survey,
we review more advanced techniques of user authentication, which use behavioral
biometrics to identify an individual based on his/her unique behavior while
he/she is interacting with his/her electronic devices. These approaches rely on
how people behave (instead of what they do), which cannot be easily forged. By
providing an overview of current approaches and the results reported in the
literature, this survey aims to drive the future research agenda for the
community in order to develop more accurate, reliable and scalable models of
credit card fraud detection.
