Online Payment Fraud Detection using Random Forest Algorithm

Author(s)	Vulugundam Anitha, Chamakura Siri, Gandepelly Akanksha, Mathe Joshna
Country	India
Abstract	In today's digital world, online transactions have become a part of everyday life, offering convenience, speed, and ease of use. However, they also come with risks like fraud, phishing, and data breaches. To tackle these challenges, we propose a machine learning-based fraud detection model that leverages feature engineering. By analyzing large volumes of data, the model learns, adapts, and improves over time, enhancing bothstability and accuracy in identifying fraudulent activities. These techniques play a crucial role in detecting online transaction fraud. By analyzing a dataset of online transactions, machine learning algorithms can spot unusual patterns that indicate fraudulent activity. Among these, the Random Forest Classifier has proven to be the most effective, achieving an impressive accuracy of 94.94%, outperforming other models in identifying suspicious transactions.
Field	Engineering
Published In	Volume 16, Issue 2, April-June 2025
Published On	2025-04-07
Cite This	Online Payment Fraud Detection using Random Forest Algorithm - Vulugundam Anitha, Chamakura Siri, Gandepelly Akanksha, Mathe Joshna - IJSAT Volume 16, Issue 2, April-June 2025. DOI 10.71097/IJSAT.v16.i2.3057
DOI	https://doi.org/10.71097/IJSAT.v16.i2.3057
Short DOI	https://doi.org/g9dpgb

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