Combining Auto Encoders and One Class Support Vectors Machine for Fraudulant Credit Card Transactions Detection

Jeragh, Mohamad; AlSulaimi, Mousa

doi:10.1109/worlds4.2018.8611624

Cited by 14 publications

(2 citation statements)

References 16 publications

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“…There are many alternative ways to fight financial fraud. However, because of the dynamic and complex nature of fraudulent behavior, the development of effective FD mechanisms, leveraging available data, will always be necessary as it is impossible to completely prevent all types of fraud [12]. In other words, criminal behavior continues to evolve as criminals find new ways to carry out their fraudulent activities.…”

Section: Methodsmentioning

confidence: 99%

Active Learning in the Detection of Anomalies in Cryptocurrency Transactions

Cunha,

Brito,

Oliveira

et al. 2023

MAKE

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The cryptocurrency market has grown significantly, and this quick growth has given rise to scams. It is necessary to put fraud detection mechanisms in place. The challenge of inadequate labeling is addressed in this work, which is a barrier to the training of high-performance supervised classifiers. It aims to lessen the necessity for laborious and time-consuming manual labeling. Some unlabeled data points have labels that are more pertinent and informative for the supervised model to learn from. The viability of utilizing unsupervised anomaly detection algorithms and active learning strategies to build an iterative process of acquiring labeled transactions in a cold start scenario, where there are no initial-labeled transactions, is being investigated. Investigating anomaly detection capabilities for a subset of data that maximizes supervised models’ learning potential is the goal. The anomaly detection algorithms under performed, according to the results. The findings underscore the need that anomaly detection algorithms be reserved for situations involving cold starts. As a result, using active learning techniques would produce better outcomes and supervised machine learning model performance.

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Section: Methodsmentioning

confidence: 99%

Active Learning in the Detection of Anomalies in Cryptocurrency Transactions

Cunha,

Brito,

Oliveira

et al. 2023

MAKE

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“…Jeragh and AlSulaimi [20] evaluated the effectiveness of their proposed hybrid model of an autoencoder and One-Class SVM for OCC. They also tested other models, which included a standalone autoencoder, a standalone One-Class SVM, and a different hybrid of an autoencoder and One-Class SVM.…”

Section: Related Workmentioning

confidence: 99%

Investigating the effectiveness of one-class and binary classification for fraud detection

Leevy,

Hancock,

Khoshgoftaar

et al. 2023

J Big Data

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Research into machine learning methods for fraud detection is of paramount importance, largely due to the substantial financial implications associated with fraudulent activities. Our investigation is centered around the Credit Card Fraud Dataset and the Medicare Part D dataset, both of which are highly imbalanced. The Credit Card Fraud Detection Dataset is large data and contains actual transactional content, which makes it an ideal benchmark for credit card fraud detection. The Medicare Part D dataset is big data, providing researchers the opportunity to examine national trends and patterns related to prescription drug usage and expenditures. This paper presents a detailed comparison of One-Class Classification (OCC) and binary classification algorithms, utilizing eight distinct classifiers. OCC is a more appealing option, since collecting a second label for binary classification can be very expensive and not possible to obtain within a reasonable time frame. We evaluate our models based on two key metrics: the Area Under the Precision-Recall Curve (AUPRC)) and the Area Under the Receiver Operating Characteristic Curve (AUC). Our results show that binary classification consistently outperforms OCC in detecting fraud within both datasets. In addition, we found that CatBoost is the most performant among the classifiers tested. Moreover, we contribute novel results by being the first to publish a performance comparison of OCC and binary classification specifically for fraud detection in the Credit Card Fraud and Medicare Part D datasets.

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Comparative Analysis on Fraud Detection in Credit Card Transaction Using Different Machine Learning Algorithms

Sehrawat

Singh

2022

Lecture Notes in Networks and Systems

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Combining Auto Encoders and One Class Support Vectors Machine for Fraudulant Credit Card Transactions Detection

Cited by 14 publications

References 16 publications

Active Learning in the Detection of Anomalies in Cryptocurrency Transactions

Active Learning in the Detection of Anomalies in Cryptocurrency Transactions

Investigating the effectiveness of one-class and binary classification for fraud detection

Comparative Analysis on Fraud Detection in Credit Card Transaction Using Different Machine Learning Algorithms

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