Titanium: AI Based Fraud Detection System

Abstract

The increasing use of digital payment systems and online financial services has led to a significant rise in fraudulent transactions. Traditional rule-based fraud detection methods often fail to identify complex and evolving fraud patterns. This paper presents an AI-powered fraud detection system that utilizes machine learning techniques to analyze transaction data and identify suspicious activities. The proposed system processes historical transaction datasets, performs data preprocessing, and applies machine learning algorithms to classify transactions as legitimate or fraudulent. Experimental results demonstrate that the system can effectively detect fraudulent transactions with improved accuracy and reduced false positives compared to traditional approaches. The proposed model enhances the security of digital financial systems and helps financial institutions minimize financial losses caused by fraud.

How to cite this paper

Pranav Mahesh Khamitkar, Anushka Shivaji Mundhe, Mohit Anil Ahir, Atharv Surendra Khadkikar. "Titanium: AI Based Fraud Detection System." PaperNova (2026). https://www.papernova.online/papers/titanium-ai-based-fraud-detection-system-0vqfb