Naïve Bayes Based Classifier for Credit Card Fraud Discovery

Ogundokun, Roseline Oluwaseun; Misra, Sanjay; Fatigun, Olufunmilayo Joyce; Adeniyi, Jide Kehinde

doi:10.1007/978-3-030-95947-0_37

Cited by 2 publications

(2 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In line with the findings in the present article and the findings in current state-of-the-art systems for detecting fraud this study provides a high digital transaction fraud detection accuracy (0.99) using relevant big data analysis tools to speed up model analysis and training and also to detect suspects' transactions as soon as they arise. in contrast to the research published in [32], [33], [34], and [35].…”

Section: Discussioncontrasting

confidence: 59%

See 1 more Smart Citation

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

Hanae,

Abdellah,

Saida

et al. 2023

IJACSA

View full text Add to dashboard Cite

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%.

show abstract

Section: Discussioncontrasting

confidence: 59%

“…Experimental evaluation shown around 0.98 of accuracy. Also, [35] have utilized the Naïve Bayes Based classifier for transaction fraud detection on a credit card dataset. They have compared the proposed model with the state-of-the-art ML methods.…”

Section: Discussionmentioning

confidence: 99%