Published Paper Details:

With the introduction of digital payments and the likes of QR transactions and web-based services, individuals become increasingly vulnerable to falling prey to cyber fraud. This can happen if one receives emails from unidentified sources, visits harmful links, or incorrectly uses QR codes and UPI transactions. As per some research papers, it has been found that despite being able to control frauds in some areas, we still cannot align ourselves well enough. In this literature review, I will discuss Sotrian, an online platform using computer algorithms such as logistic regression, random forest, and XGBoost for predicting fraudulent behavior in email and URL links, QR code scans, and UPI transactions. The developers behind Sotrian utilized technologies like Next.js and Python to create this web app and include a dashboard for viewing analytics of detected fraudulent activity. In this context, Sotrian plays a vital role in bridging the existing gap in anti-fraud measures. Specifically, it combines unique aspects of the discussed frameworks and implements them in a unified approach for monitoring and analyzing online behavior, making it more efficient and user-friendly. In summary, Sotrian represents a web platform focused on fraud detection within digital payments and QR transactions, web-based services, and email communications.