Machine Learning for Fraud Detection

Detailed version: https://lab.civicrm.org/community-team/gsoc2018/issues/2

Synopsis

The project aims to build a new open-source fraud detection system for WMF. The 2 major steps involved are:

* experimenting with various anomaly detection techniques to figure out which one provides a required balance of precision (% of detected frauds which are actually fraudulent) and recall (% of all frauds detected);
* providing the technique as an independent web service to WMF (like ORES) which can entertain requests to ascertain the authenticity of transactions.

Stretch Goals

* The web service uses the feedback from its decisions (new correct detection/wrong detection corrected by a human) to train the underlying model, improving its accuracy in the future.
* Use something like LIME to provide a justification as to why our classifier chose to mark a transaction as fraud.
* CiviCRM extension to interface directly with the web service.