The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
SCARFF : A scalable framework for streaming credit card fraud detection with spark
Open accessAbstract
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.
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Citations
Cites
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“In particular, the classification relies on Random Forests [42, 43], which have been shown to be particularly effective in fraud detection problems [44, 45, 46].”
Method
Research works printed but not linked: 3
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Cited by
Machine Learning Operations (MLOps): Overview, Definition, and Architecture
Kreuzberger, D., Kühl, N., & Hirschl, S. (2023) · IEEE Access · doi:10.1109/access.2023.3262138 · Cited by 766
“into the Spark-based feature engineering pipeline [54].”
Combining unsupervised and supervised learning in credit card fraud detection
Carcillo, F., Le Borgne, Y.-A., Caelen, O., Kessaci, Y., Oblé, F., & Bontempi, G. (2021) · Information Sciences · doi:10.1016/j.ins.2019.05.042 · Cited by 478
Self-citationThe state of the art and taxonomy of big data analytics: view from new big data framework
Mohamed, A., Najafabadi, M. K., Wah, Y. B., Zaman, E. A. K., & Maskat, R. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-019-09685-9 · Cited by 265
Metadata
- Authors
- Carcillo, F., Dal Pozzolo, A., Le Borgne, Y.-A., Caelen, O., Mazzer, Y., & Bontempi, G.
- Venue
- Information Fusion
- Publisher
- Elsevier BV
- Published
- 2018-05
- Volume
- 41
- Pages
- 182-194
- ISSN
- 1566-2535 (ISSN-L)
- OpenAlex
- W2751596245
- Cited by
- 267
- References
- 48
- FWCI
- 9.84
- Type
- journal article
- Language
- en
- Open access
- green
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- Funders
- INNOVIRIS
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- Carcillo, F., Dal Pozzolo, A., Le Borgne, Y.-A., Caelen, O., Mazzer, Y., & Bontempi, G. (2018). SCARFF : A scalable framework for streaming credit card fraud detection with spark. Information Fusion. https://doi.org/10.1016/j.inffus.2017.09.005
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