Credit card fraud detection using machine learning: A survey

Lucas, Yvan; Jurgovsky, Johannes

doi:10.48550/arxiv.2010.06479

Cited by 5 publications

(12 citation statements)

References 43 publications

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“…Although the problem of imbalanced data exists in many real-world data and has been extensively studied in the literature, credit card fraud data is particularly highly imbalanced. Most of the existing solutions solve the problems of moderately or and slightly unbalanced data [5,7,11,15,20,31,33,[44][45][46][47]. These solutions can be grouped under two main concepts: data augmentation and imbalanced learning.…”

Section: Related Workmentioning

confidence: 99%

“…Credit card transaction datasets are rare because they contains sensitive and private information for customers and monetary institutions. In addition, credit card transaction datasets suffer from high class-imbalance: the percentage of fraudulent transactions is lower than 0.01% in most available datasets [20,21]. This means the solutions proposed for other domains do not necessarily fit the credit card fraud dataset problem.…”

Section: Introductionmentioning

confidence: 99%

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Ensemble Synthesized Minority Oversampling-Based Generative Adversarial Networks and Random Forest Algorithm for Credit Card Fraud Detection

Ghaleb,

Saeed,

Al-Sarem

et al. 2023

IEEE Access

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The recent increase in credit card fraud is rapidly has caused huge monetary losses for individuals and financial institutions. Most credit card frauds are conducted online by illegally obtaining payment credentials through data breaches, phishing, or scamming. Many solutions have been suggested to address the credit card fraud problem for online transactions. However, the high class imbalance is the major challenge that faces the existing solutions to construct an effective detection model. Most of the existing techniques used for class imbalance overestimate the distribution of the minority class, resulting in highly overlapped or noisy and unrepresentative features, which cause either overfitting or imprecise learning. In this study, a credit card fraud detection model (CCFDM) is proposed based on ensemble learning and a generative adversarial network (GAN) assisted by Ensemble Synthesized Minority Oversampling techniques (ESMOTE-GAN). Multiple subsets were extracted using under-sampling and SMOTE was applied to generate less skewed sets to prevent the GAN from modeling the noise. These subsets were used to train diverse sets of GAN models to generate the synthesized subsets. A set of Random Forest classifiers was then trained based on the proposed ESMOTE-GAN technique. The probabilistic outputs of the trained classifiers were combined using a weighted voting scheme for decision-making. The results show that the proposed model achieved 1.9%, and 3.2% improvements in overall performance and the detection rate, respectively, with a 0% false alarm rate. Due to the massive number of transactions, even a tiny false positive rate can overwhelm the analysis team. Thus, the proposed model has improved the detection performance and reduced the cost needed for manual analysis.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ensemble Synthesized Minority Oversampling-Based Generative Adversarial Networks and Random Forest Algorithm for Credit Card Fraud Detection

Ghaleb,

Saeed,

Al-Sarem

et al. 2023

IEEE Access

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“…In this section, we review several typical surveys published recently. In [97], the majority of machine learning methods and data imbalance are discussed, but the discussion only focuses on the card defraud domain and the authors didn't consider the synergetic effects of models. Xolani Dastile, Turgay Celik et al [50] had a thorough investigation of systematic machine learning and its application in credit risk.…”

Section: Existing Survey Papersmentioning

confidence: 99%

Machine learning-driven credit risk: a systemic review

Shi

Tse

Luo

et al. 2022

Neural Comput & Applic

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Credit risk assessment is at the core of modern economies. Traditionally, it is measured by statistical methods and manual auditing. Recent advances in financial artificial intelligence stemmed from a new wave of machine learning (ML)-driven credit risk models that gained tremendous attention from both industry and academia. In this paper, we systematically review a series of major research contributions (76 papers) over the past eight years using statistical, machine learning and deep learning techniques to address the problems of credit risk. Specifically, we propose a novel classification methodology for ML-driven credit risk algorithms and their performance ranking using public datasets. We further discuss the challenges including data imbalance, dataset inconsistency, model transparency, and inadequate utilization of deep learning models. The results of our review show that: 1) most deep learning models outperform classic machine learning and statistical algorithms in credit risk estimation, and 2) ensemble methods provide higher accuracy compared with single models. Finally, we present summary tables in terms of datasets and proposed models.

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“…Credit card fraud is one of the most common financial crimes and financial institutions are trying to find accurate ways to identify suspicious transactions. As a result a large volume of research for detecting credit card fraud using machine learning and artificial intelligence methods exists ( 23,24 provide comprehensive surveys on this line of research, while Stojanovic et al 25 provide a comprehensive survey on fraud detection in general fintech applications).…”

Section: Background and Related Workmentioning

confidence: 99%

Spotting Anomalous Trades in NFT Markets: The Case of NBA Topshot

Pelechrinis¹,

Liu²,

Krishnamurthy³

et al. 2022

Preprint

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Non-Fungible Token (NFT) markets are one of the fastest growing digital markets today, with the sales during the third quarter of 2021 exceeding $10 billions! Nevertheless, these emerging markets -similar to traditional emerging marketplaces -can be seen as a great opportunity for illegal activities (e.g., money laundering, sale of illegal goods etc.). In this study we focus on a specific marketplace, namely NBA TopShot, that facilitates the purchase and (peer-to-peer) trading of sports collectibles. Our objective is to build a framework that is able to label peer-to-peer transactions on the platform as anomalous or not. To achieve our objective we begin by building a model for the profit to be made by selling a specific collectible on the platform. We then use RFCDE -a random forest model for the conditional density of the dependent variable -to model the errors from the profit models. This step allows us to estimate the probability of a transaction being anomalous. We finally label as anomalous any transaction whose aforementioned probability is less than 1%. Given the absence of ground truth for evaluating the model in terms of its classification of transactions, we analyze the trade networks formed from these anomalous transactions and compare it with the full trade network of the platform. Our results indicate that these two networks are statistically different when it comes to network metrics such as, edge density, closure, node centrality and node degree distribution. This network analysis provides additional evidence that these transactions do not follow the same patterns that the rest of the trades on the platform follow. However, we would like to emphasize here that this does not mean that these transactions are also illegal. These transactions will need to be further audited from the appropriate entities to verify whether or not they are illicit.

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Credit card fraud detection using machine learning: A survey

Cited by 5 publications

References 43 publications

Ensemble Synthesized Minority Oversampling-Based Generative Adversarial Networks and Random Forest Algorithm for Credit Card Fraud Detection

Ensemble Synthesized Minority Oversampling-Based Generative Adversarial Networks and Random Forest Algorithm for Credit Card Fraud Detection

Machine learning-driven credit risk: a systemic review

Spotting Anomalous Trades in NFT Markets: The Case of NBA Topshot

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