Application of Deep Learning Algorithm to Detect Fraud in Online Transaction Networks

Abstract

Online transaction fraud is a severe problem that may cost businesses and people a lot of money. This paper suggests using deep learning algorithms to detect fraud as a remedy to this issue. These algorithms were chosen based on their ability to handle large amounts of intricate data and identify patterns that are difficult to identify using traditional techniques. Important components of this research include gathering and preprocessing transaction data, creating deep learning models, and assessing model performance. This investigation examines a variety of financial transaction types that may have involved fraud. The deep learning approach uses deep neural network designs, including Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN), to maximize detection accuracy. The study's findings demonstrate that the deep learning models created are excellent at identifying questionable transactions and can lower the false positive rate, which raises the overall effectiveness of fraud detection systems. As a result, deep learning algorithms have demonstrated a high degree of efficacy in identifying fraudulent activity inside internet-based transaction networks, so they play a vital role in fraud prevention.