Authors :
NagaBabu Pachhala; Mallipudi Devi Siva Sai; Palaparthi Prudhvi; Gollapudi M Naga Venkata Sai Gopi; Indla Ganeswara Naga Sai Ram; Mandadi Ram Sandeep
Volume/Issue :
Volume 8 - 2023, Issue 10 - October
Google Scholar :
https://tinyurl.com/5ny57xzs
Scribd :
https://tinyurl.com/afm8mzc2
DOI :
https://doi.org/10.5281/zenodo.10073985
Abstract :
The rise of the internet and e-commerce
appears to entail the usage of online payment
transactions. The increased usage of online payments is
leading to a rise in fraud. However, as the number of
online transactions increases, so does the number of fraud
instances. Fraud detection is an important component of
online payment systems since it serves to protect both
customers and merchants from financial damages. In this
project, we propose a fraud detection system for online
payments that uses machine learning techniques to
identify and prevent fraudulent transactions. Using
machine learning algorithms, we can find unique data
patterns or uncommon data patterns that will be useful in
detecting any fraudulent transactions. The Random
Forest Classifier will be utilized to get the best results.
Our approach strives to improve fraud detection
accuracy while reducing the amount of false positives,
resulting in a more efficient and effective method for
identifying and combating fraud.
Keywords :
Fraud Detection, Machine Learning, Random Forest Algorithm, SVM, Classification, Data Pre- Processing, Prediction.
The rise of the internet and e-commerce
appears to entail the usage of online payment
transactions. The increased usage of online payments is
leading to a rise in fraud. However, as the number of
online transactions increases, so does the number of fraud
instances. Fraud detection is an important component of
online payment systems since it serves to protect both
customers and merchants from financial damages. In this
project, we propose a fraud detection system for online
payments that uses machine learning techniques to
identify and prevent fraudulent transactions. Using
machine learning algorithms, we can find unique data
patterns or uncommon data patterns that will be useful in
detecting any fraudulent transactions. The Random
Forest Classifier will be utilized to get the best results.
Our approach strives to improve fraud detection
accuracy while reducing the amount of false positives,
resulting in a more efficient and effective method for
identifying and combating fraud.
Keywords :
Fraud Detection, Machine Learning, Random Forest Algorithm, SVM, Classification, Data Pre- Processing, Prediction.
