Open Access
Research Article
Enhanced credit card fraud detection in online transactions using Long Short Term Memory (LSTM) and RFM analysis with ADAYSN oversampling
Bhumika Ranabhumikarana983@gmail.comDepartment of Information Technology Guru Tegh Bahadur Institute of TechnologyNew Delhi, 110064, IndiaView full profile → , Tanu Tomartomartanu647@gmail.comDepartment of Information Technology Guru Tegh Bahadur Institute of TechnologyNew Delhi, 110064, IndiaView full profile → , Amandeep Kauramandeep.gtbit@gmail.comDepartment of Information Technology Guru Tegh Bahadur Institute of TechnologyNoida, New Delhi, 110064, India0000-0002-4634-6694View full profile → , Parsan Kaurparsank6@gmail.comDepartment of Information Technology Guru Tegh Bahadur Institute of TechnologyNew Delhi, 110064, IndiaView full profile → , Simmi Singhsimmirooprai@gmail.comDepartment of Information Technology Guru Tegh Bahadur Institute of TechnologyNew Delhi, 110064, IndiaView full profile → , Puneet Singh Lambasinghs.puneet@gmail.comDepartment of Computer Science Sri Guru Tegh Bahadur Khalsa College University of DelhiDelhi, 110007, IndiaView full profile → , Achin Jainachin.mails@gmail.comDepartment of Information Technology Bharati Vidyapeeth’s College of EngineeringPaschim Vihar, New Delhi, 110063, IndiaView full profile → , *Harsh TanejaCorresponding authorharshtaneja88@gmail.comDepartment of Computer Science & Engineering Graphic Era (Deemed to be University)Dehradun, Uttrakhand, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Published Online:
- 15 Mar 2024
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-1558
- Pages:
- 413–422
Abstract
The expansion of online transactions, particularly online credit card transactions, has revolutionized the field of e-commerce and streamlined electronic payment systems. However, this growth has also given rise to a significant challenge in the form of credit card fraud. To combat this issue, banks and financial organizations have recognized the need for robust credit card fraud detection applications. Machine learning (ML) approaches have emerged as a valuable tool in this regard, as they offer the potential to accurately detect and prevent fraudulent transactions. Long Short-Term Memory (LSTM), a recurrent neural network, is used in this study’s evaluation of ML approaches for detecting credit card fraud (CCFD) in online transactions. The most effective LSTM architecture is chosen after a thorough examination based on its capacity to identify credit card fraud with high accuracy and precision. The suggested method makes use of LSTM and RFM analysis to comprehend customer behavior and ADASYN sampling to address class imbalance. The findings show that the selected LSTM architecture, in combination with RFM analysis and ADASYN, delivers great efficiency and efficacy in identifying credit card fraud, hence promoting safe online transactions.
Keywords
Subject Classifications
94-XX91-XX
References
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