• Arjun Singh Pundir
• Arun G K
• Arya N D
• Bharatkumar N M

Fraud is an increasingly pervasive issue in today’s interconnected digital world, impacting various domains such as financial transactions, insurance claims, and content creation. Traditional fraud detection systems often lack the sophistication and scalability to address the complexity and diversity of modern fraud schemes. To tackle these challenges, this paper introduces the Multi-Domain Fraud Detection System, a unified platform designed to detect fraudulent activities across three key areas: GPT-Generated Text, Credit Card Transactions, and Automobile Insurance Claims. The system integrates advanced technologies, including Artificial Neural Networks (ANN), perplexity analysis, and supervised machine learning models, to ensure high accuracy and reliability. Enhanced with secure OTP-based authentication through the Telegram API and a user-friendly interface built on Streamlit, the platform offers robust fraud detection capabilities while prioritizing user experience. This paper explores the system’s design, implementation, and performance, highlighting its potential to transform how fraud is detected and mitigated across industries.

Fraud Detection, Machine Learning, Artificial Intelligence, GPT Analysis, Credit Card Security, Insurance Fraud Prevention, Streamlit, Telegram API, Secure Authentication.

"Fraud Fortify: Multi-Domain Fraud Detection System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 12, page no.f507-f515, December-2024, Available :http://www.jetir.org/papers/JETIR2412559.pdf

"Fraud Fortify: Multi-Domain Fraud Detection System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 12, page no. ppf507-f515, December-2024, Available at : http://www.jetir.org/papers/JETIR2412559.pdf