  The expansion of the electronic commerce, together with an increasing
confidence of customers in electronic payments, makes of fraud detection a
critical factor. Detecting frauds in (nearly) real time setting demands the
design and the implementation of scalable learning techniques able to ingest
and analyse massive amounts of streaming data. Recent advances in analytics and
the availability of open source solutions for Big Data storage and processing
open new perspectives to the fraud detection field. In this paper we present a
SCAlable Real-time Fraud Finder (SCARFF) which integrates Big Data tools
(Kafka, Spark and Cassandra) with a machine learning approach which deals with
imbalance, nonstationarity and feedback latency. Experimental results on a
massive dataset of real credit card transactions show that this framework is
scalable, efficient and accurate over a big stream of transactions.
