Python or R for implementing machine learning algorithms for fraud detection
I would say that it is your call and purely depends on your comfort with (or desire to learn) the language. Both languages have extensive ecosystems of packages/libraries, including some, which could be used for fraud detection. I would consider anomaly detection as the main theme for the topic. Therefore, the following resources illustrate the variety of approaches, methods and tools for the task in each ecosystem.

Python Ecosystem


scikit-learn library: for example, see this page;
LSAnomaly, a Python module, improving OneClassSVM (a drop-in replacement): see this page;
Skyline: an open source example of implementation, see its GitHub repo;
A relevant discussion on StackOverflow;
pyculiarity, a Python port of Twitter's AnomalyDetection R Package (as mentioned in 2nd bullet of R Ecosystem below "Twitter's Anomaly Detection package").


R Ecosystem


CRAN Task Views, in particular, Time Series, Multivariate and Machine Learning;
Twitter's Anomaly Detection package;
Early Warning Signals (EWS) Toolbox, which includes earlywarnings package;
h2o machine learning platform (interfaces with R) uses deep learning for anomaly detection.


Additional General Information


A set of slides, mentioning a variety of methods for anomaly detection (AD);
A relevant discussion on Cross Validated;
ELKI Data Mining Framework, implementing a large number of AD (and other) algorithms.