Analysis and Comparison of Credit Card Fraud Detection Using Machine Learning

Saloni,; Rout, Minakhi

doi:10.1007/978-981-15-8752-8_4

Cited by 6 publications

(1 citation statement)

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“…Therefore, the detection of fraudulent transactions has paramount importance in online platforms. To overcome this issue, various machine learning and data mining techniques have been applied for the detection of such fraudulent transactions [4,5] but due to the lack of data availability and confidentiality issues, this area is still not fully researched. However, current researches rely heavily on Artificial Intelligent to build more robust intelligent models which might provide high accuracy during analyzing massive datasets.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Credit Card Fraud Transaction Detection Using Deep Reinforcement Learning Scheme

Qayoom

Khuhro

Kumar

et al. 2023

Preprint

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Online transactions are the backbone of the financial industry globally. Millions of people are using credit cards daily, which has led to an exponential rise in their daily life that has led to an exponential rise in credit card frauds. With the passage of time, many variations and schemes of fraudulent transactions have been occurring. Real-time credit card fraud detection is still a challenging task. Every person has a unique transaction pattern. This pattern may vary over a period of time. Sometimes, credit card frauds have no constant pattern. This paper aims to 1) understand how deep reinforcement learning can play a vital role in detecting credit card fraud and 2) design solution architecture for real-time detection. Our proposed model uses the DeepQ network. A deep Q network has been utilized for real time detection. The online available Kaggle dataset hasbeen used for model training and testing, and 97% validation performance has been achieved via our proposeddeep learning component. The reinforcement learningcomponent has an 80% learning rate. Our proposedmodel was able to learn patterns itself based on previous history and adopted the pattern changes over timeand accommodate them without any further manualtraining.

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Section: Introductionmentioning

confidence: 99%