A Review on: Credit Card Fraud Detection Techniques Using ML

A Review on: Credit Card Fraud Detection Techniques Using ML

Aditya V. Belsare ¹, Dr. R. R. Keole², Dr. A.B.Raut³

¹ PG Students, Dept. of Computer Science and Engineering, HVPM COET, Amravati, Maharashtra, India

² HOD, Dept. of Information and Technology, HVPM COET, Amravati, Maharashtra, India

³ HOD, Dept. of Computer Science and Engineering, HVPM COET, Amravati, Maharashtra, India

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Abstract-Now a day’s credit card frauds are easy and friendly targets. E-commerce and many other online sites have increased the online payment modes, increasing the risk for online frauds. Increase in fraud rates, researchers started using different machine learning methods to detect and analyses frauds in online transactions. Frauds are known to be dynamic and have no patterns, hence they are not easy to identify. Fraudsters use recent technological advancements to their advantage. They somehow bypass security checks, leading to the loss of millions of dollars. Analyzing and detecting unusual activities using data mining techniques is one way of tracing fraudulent transactions. In this paper the different proposed system developed by different researcher used in Credit Card Risk Detection Techniques Using Machine Learning is stated.

Key Words: credit card, fraud detection, machine learning, deep learning, random forest, k nearest neighbor, support vector machine, auto encoder, restricted Boltzmann machine, deep belief networks, convolutional neural networks