The Optimized Anomaly Detection Models Based on an Approach of Dealing with Imbalanced Dataset for Credit Card Fraud Detection

Zhang, Yanfeng; Lü, Hongliang; Lin, Hong-Fan; Qiao, Xue-Chen; Zheng, Hao

doi:10.1155/2022/8027903

Cited by 10 publications

(3 citation statements)

References 18 publications

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“…Despite being appropriate for extensive database detecting anomalies, IForest's detection effectiveness will decline with increasing distribution of data difficulty. When detecting anomalies in data with extremely high dimensions, the method is highly volatile [37].…”

Section: Isolation Forest-based Fraud Detectionmentioning

confidence: 99%

Digital banking fortification: a real-time isolation forest architecture for detecting online transaction fraud

Abbassi,

E L Mendili,

Gahi

2024

Eng. Res. Express

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Since the use of the Internet has increased exponentially, numerous organizations, including the financial industry, offer services online. As a result, financial scams are expanding in quantity and complexity worldwide, resulting in massive revenue losses and making digital fraudulent transactions a severe issue. Abnormal attempts and illegal access are instances of these dangers that fraudulent activity detection systems must identify. Machine learning and data mining approaches have been extensively used to address this issue in recent years.However, these approaches must be enhanced regarding real-time detection speed, tackling enormous amounts of data, and finding undiscovered attack patterns. Consequently, the present study provides a real-time architecture for averting and identifying digital transaction fraud, which relies on the Isolation Forest (IForest) approach and big data analytic tools, including Spark Streaming, sparkling water, Kafka, and PostgreSQL. This architecture seeks to improve present detection strategies by increasing accuracy for detection when considering enormous amounts of data. Two real datasets of online transactional fraud are used to assess the proposed architecture, and the findings are compared to relevant studies. The investigation results showed that IForest performed flawlessly, achieving an accuracy of 0.99 in two datasets.

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Section: Isolation Forest-based Fraud Detectionmentioning

confidence: 99%

Digital banking fortification: a real-time isolation forest architecture for detecting online transaction fraud

Abbassi,

E L Mendili,

Gahi

2024

Eng. Res. Express

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“…It employs a similar technique as the (RF) Random Forest algorithm and is based on the notion of decision trees. Rather than using the typical properties of data points, the isolation forest algorithm's basic idea and approach are to detect abnormalitiesfor example, fraudulent transactions [24] [25].…”

Section: Isolation Forestmentioning

confidence: 99%

End-to-End Real-time Architecture for Fraud Detection in Online Digital Transactions

Hanae,

Abdellah,

Saida

et al. 2023

IJACSA

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The banking sector is witnessing a fierce concurrence characterized by changing business models, new entrants such as FinTechs, and new customer behaviors. Financial institutions try to adapt to this trend and invent new ways and channels to reach and interact with their customers. While banks are opening their services to avoid missing this shift, they become naturally exposed to fraud attempts through their digital banking platforms. Therefore, fraud prevention and detection are considered must-have capabilities. Detecting fraud at an optimal time requires developing and deploying scalable learning systems capable of ingesting and analyzing vast volumes of streaming records. Current improvements in data analytics algorithms and the advent of open-source technologies for big data processing and storage bring up novel avenues for fraud identification. In this article, we provide a real-time architecture for detecting transactional fraud via behavioral analysis that incorporates big data analysis techniques such as Spark, Kafka, and h2o with an unsupervised machine learning (ML) algorithm named Isolation Forest. The results of experiments on a significant dataset of digital transactions indicate that this architecture is robust, effective, and reliable across a large set of transactions yielding 99% of accuracy, and a precision of 87%.

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“…After that, an adaptive density based clustering (ADBC) strategy is presented for the purpose of identifying the speci ed characteristics while the clustering process is being carried out. Anomaly identi cation is performed on unbalanced data by Zhang et al (2022), who also use Isolation Forest (IForest) in conjunction with kernel principal component analysis. The use of a one-class support vector machine (OCSVM) with AdaBoost as two models to identify anomalies results in a substantial improvement in both the precision and effectiveness of the identi cation process.…”

Section: Related Workmentioning

confidence: 99%

Flower pollination optimization algorithm with stacked temporal convolution network-based classification for financial anomaly fraud detection

Govindarajan

Rao³

2023

Soft Comput

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As internet use increases dramatically, an increasing number of industries, including the nancial one, are operationalizing their services online. Due to the signi cant nancial losses they cause, nancial fraud is becoming a big issue as it spreads globally and grows in both volume and variety. Financial fraud detection systems should be used to identify dangers like unauthorized access and erratic assaults.During the last several years, this problem has been extensively addressed using machine learning and data mining approaches. These methods still need to be developed in order to cope with massive data, compute quickly, and spot new attack patterns. As a result, in this research, a deep learning-based approach based on the stacked temporal convolution network technique is provided for the identi cation of nancial fraud. This model aims to improve both the e ciency and accuracy of existing detection methods in the context of large data. In addition, a ower pollination optimization process is incorporated for feature selection, which addresses any side effects that may result from choosing the best features.An actual dataset of credit card, loan, and insurance frauds is used to assess the proposed model, and the results are contrasted with those of current deep learning methods. The experimental results show that the suggested FPO_QCNN achieves 99.95%, 99.95%, 99.95% of accuracy for credit card, insurance and Mortgage Data set.

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The Optimized Anomaly Detection Models Based on an Approach of Dealing with Imbalanced Dataset for Credit Card Fraud Detection

Cited by 10 publications

References 18 publications

Digital banking fortification: a real-time isolation forest architecture for detecting online transaction fraud

Digital banking fortification: a real-time isolation forest architecture for detecting online transaction fraud

End-to-End Real-time Architecture for Fraud Detection in Online Digital Transactions

Flower pollination optimization algorithm with stacked temporal convolution network-based classification for financial anomaly fraud detection

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