Credit card fraud detection and concept-drift adaptation with delayed supervised information

Pozzolo, Andrea Dal; Boracchi, Giacomo; Caelen, Olivier; Alippi, Cesare; Bontempi, Gianluca

doi:10.1109/ijcnn.2015.7280527

Cited by 78 publications

(75 citation statements)

References 36 publications

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“…This module is designed on the basis of our recent research in fraud detection [19,20] and it aims to take into account the specificity of a Fraud Detection System where automatic tools have to interact with human investigators. The role of fraud investigators is to focus on the most suspicious transactions and to contact cardholders.…”

Section: The Machine Learning Enginementioning

confidence: 99%

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SCARFF : A scalable framework for streaming credit card fraud detection with spark

et al. 2018

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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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Section: The Machine Learning Enginementioning

confidence: 99%

“…In this paper we start from the conclusions of our published works [17,19,20] and we propose a realistic and scalable implementation of a fraud detection system. SCARFF (SCAlable Real-time Fraud Finder) is an open source platform which processes and analyses streaming data in order to return reliable alerts in a nearly real-time setting.…”

Section: Introductionmentioning

confidence: 99%

SCARFF : A scalable framework for streaming credit card fraud detection with spark

et al. 2018

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“…Pozzolo [8] directly tackled verification delay in the credit card fraud detection task. The proposal assumes that, while a greater portion of the data is affected by delay, the true labels are instantly available for a much smaller portion.…”

Section: Related Workmentioning

confidence: 99%

Fast Unsupervised Online Drift Detection Using Incremental Kolmogorov-Smirnov Test

Reis

Flach

Matwin³

et al. 2016

Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

102

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Data stream research has grown rapidly over the last decade. Two major features distinguish data stream from batch learning: stream data are generated on the fly, possibly in a fast and variable rate; and the underlying data distribution can be non-stationary, leading to a phenomenon known as concept drift. Therefore, most of the research on data stream classification focuses on proposing efficient models that can adapt to concept drifts and maintain a stable performance over time. However, specifically for the classification task, the majority of such methods rely on the instantaneous availability of true labels for all already classified instances. This is a strong assumption that is rarely fulfilled in practical applications. Hence there is a clear need for efficient methods that can detect concept drifts in an unsupervised way. One possibility is the well-known Kolmogorov-Smirnov test, a statistical hypothesis test that checks whether two samples differ. This work has two main contributions. The first one is the Incremental Kolmogorov-Smirnov algorithm that allows performing the Kolmogorov-Smirnov hypothesis test instantly using two samples that change over time, where the change is an insertion and/or removal of an observation. Our algorithm employs a randomized tree and is able to perform the insertion and removal operations in O(log N ) with high probability and calculate the Kolmogorov-Smirnov test in O(1), where N is the number of sample observations. This is a significant speed-up compared to the O(N log N ) cost of the non-incremental implementation. The second contribution is the use of the Incremental Kolmogorov-Smirnov test to detect concept drifts without true labels. Classification algorithms adapted to use the test rely on a limited portion of those labels just to update the classification model after a concept drift is detected.

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“…A second challenge associated with modeling the Bitcoin Blockchain transaction network consists of capturing the complexity of the hidden structure associated with entity transactions, together with the fine-grained block-level specificities implied by the Bitcoin protocol. In particular, Bitcoin is based on an unspent transaction output (UTXO) model, which distinguishes suitable Bitcoin Blockchain models from prior studies on credit card transactions [6,18], since the proper generative structure needs to account for the underlying UTXO creation and deletion process.…”

Section: Introductionmentioning

confidence: 99%

A Probabilistic Model of the Bitcoin Blockchain

Jourdan

Blandin

Wynter

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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The Bitcoin transaction graph is a public data structure organized as transactions between addresses, each associated with a logical entity. In this work, we introduce a complete probabilistic model of the Bitcoin Blockchain. We first formulate a set of conditional dependencies induced by the Bitcoin protocol at the block level and derive a corresponding fully observed graphical model of a Bitcoin block. We then extend the model to include hidden entity attributes such as the functional category of the associated logical agent and derive asymptotic bounds on the privacy properties implied by this model. At the network level, we show evidence of complex transaction-to-transaction behavior and present a relevant discriminative model of the agent categories. Performance of both the block-based graphical model and the network-level discriminative model is evaluated on a subset of the public Bitcoin Blockchain.

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Credit card fraud detection and concept-drift adaptation with delayed supervised information

Cited by 78 publications

References 36 publications

SCARFF : A scalable framework for streaming credit card fraud detection with spark

SCARFF : A scalable framework for streaming credit card fraud detection with spark

Fast Unsupervised Online Drift Detection Using Incremental Kolmogorov-Smirnov Test

A Probabilistic Model of the Bitcoin Blockchain

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