Research Article
Fraud Detection in Credit Card Transaction Using ANN and SVM
@INPROCEEDINGS{10.1007/978-3-030-79276-3_14, author={Anchana Shaji and Sumitra Binu and Akhil M. Nair and Jossy George}, title={Fraud Detection in Credit Card Transaction Using ANN and SVM}, proceedings={Ubiquitous Communications and Network Computing. 4th EAI International Conference, UBICNET 2021, Virtual Event, March 2021, Proceedings}, proceedings_a={UBICNET}, year={2021}, month={7}, keywords={SMOTE Artificial neural network Support vector machine Credit card fraud detection}, doi={10.1007/978-3-030-79276-3_14} }- Anchana Shaji
Sumitra Binu
Akhil M. Nair
Jossy George
Year: 2021
Fraud Detection in Credit Card Transaction Using ANN and SVM
UBICNET
Springer
DOI: 10.1007/978-3-030-79276-3_14
Abstract
Digital Payment fraudulent cases have increased with the rapid growth of e-commerce. Masses use credit card payments for both online and day-to-day purchasing. Hence, payment fraud utilizes a billion-dollar business, and it is growing fast. The frauds use different patterns to make the transactions from the cardholder’s account, making it difficult for the organization or the users to detect fraudulent transactions. The study’s principal purpose is to develop an efficient supervised learning technique to detect credit card fraudulent transactions to minimize the customer’s and organization’s losses. The respective classification accuracy compares supervised learning techniques such as deep learning-based ANN and machine learning-based SVM models. This study’s significant outcome is to find an efficient supervised learning technique with minimum computational time and maximum accuracy to identify the fraudulent act in credit card transactions to minimize the losses incurred by the consumers and banks.
