SCARFF: a Scalable Framework for Streaming Credit Card Fraud Detection with Spark
arXiv:1709.08920 · doi:10.1016/j.inffus.2017.09.005
Abstract
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.
References in corpus (1)
Cited by in corpus (5)
- Streaming Active Learning Strategies for Real-Life Credit Card Fraud Detection: Assessment and Visualization
- A Survey on Spark Ecosystem for Big Data Processing
- Approaches to Fraud Detection on Credit Card Transactions Using Artificial Intelligence Methods
- Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data
- Towards Credit-Fraud Detection via Sparsely Varying Gaussian Approximations