The Detection of Credit Card Data Fraud by An Unsupervised Machine Learning Based Scheme the Detection of Credit Card Data Fraud by an Unsupervised Machine Learning Based Scheme
Keywords:
Unsupervised Learning, Anomaly Detection, Fraud Detection, Auto-Encoder, Credit Card.Abstract
Development of communication technologies and ecommerce has made the credit card as the most common technique of payment for both online and regular purchases. So, security in this system is highly expected to prevent fraud transactions. Fraud transactions in credit card data transaction are increasing each year. In this direction, researchers are also trying the novel techniques to detect and prevent such frauds. However, there is always a need of some techniques that should precisely and efficiently detect these frauds. This paper proposes a scheme for detecting frauds in credit card data which uses a Neural Network (NN) based unsupervised learning technique. Proposed method outperforms the existing approaches of Auto Encoder (AE), Local Outlier Factor (LOF), Isolation Forest (IF) and K-Means clustering. Proposed NN based fraud detection method performs with 99.87% accuracy whereas existing methods AE, IF, LOF and K Means gives 97?, 98?, 98? and 99.75? accuracy respectively.
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