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.
Carcillo et al. REF also introduced the SCAlable Real-time Fraud Finder (SCARFF), which incorporates big-data techniques (Cassandra, Kafka and Spark) in a machine learning method to address nonstationarity, imbalance, and feedback latency.
SCARFF: a Scalable Framework for Streaming Credit Card Fraud Detection with Spark
Information Fusion 41C (2018) pp. 182-194
Computer Science