FrauDetect: Deep Learning Based Credit Card Fraudulency Detection System

FrauDetect: Deep Learning Based Credit Card Fraudulency Detection System

Shanmukha Priya Sreenidhi Appalabatla1, Abothu Sharath Kumar2, Adidamu Pramod Sai3, Avula Sanjana4, Dr. S. Satyanarayana5

1,2,3,4 Student, Dept. of Computer Science and Engineering (AIML), Malla Reddy University, Hyderabad, India

5 Professor, Dept. of Computer Science  and Engineering  (AIML), Malla Reddy University, Hyderabad, India

---------------------------------------------------------------------***---------------------------------------------------------------------

Abstract - Huge increase in the internet usage has been observed since last decade. It led to the emergence of services like e-commerce, tap and pay systems, online bill payment systems, etc. have proliferated and become more widely used. Due to various online payment options introduced by e-commerce and other numerous websites, the possibility of online fraud has risen drastically. Thus, due to an increase in fraud rates, research on analyzing and detecting fraud in online transactions has begun utilizing various machine learning techniques. The Deep Learning techniques viz., Convolutional Neural Network Architecture is used to detect credit card frauds in the proposed model. The Principal Component Analysis transformation gives a set of numerical input variables as output which are taken as the features to be considered. Due to confidentiality concerns, some of the original characteristics and background information about the data are not entirely disclosed. Features of credit card frauds must be chosen carefully as they play important role when deep learning techniques are used for credit card fraud detection. The TensorFlow and Keras are used for the development of the current proposed model. The proposed model aims to predict credit card frauds with 91.9% accuracy.

Key Words: Credit card, Fraudulent, Deep learning, Principal Component Analysis, TensorFlow, Keras