Financial fraud in digital payment systems and online banking platforms has become a major technological and security challenge due to the rapid growth of online transactions and electronic financial services. Although many existing fraud detection systems provide transaction monitoring and alert mechanisms, most of them rely on predefined rules and manual verification processes, which are often unable to identify fraudulent activities in real time or analyze complex transaction behavior intelligently. This paper presents the AI-Based Fraud Detection System, an intelligent fraud prevention platform that combines real-time transaction monitoring, machine learning-based risk prediction, and automated fraud alert generation.

The proposed system evaluates multiple transaction-related parameters, including transaction amount, transaction frequency, login location, device information, IP address, user behavior patterns, and unusual account activity to estimate the probability of fraudulent transactions. Based on this analysis, the system classifies transactions into Low Risk, Medium Risk, or High Risk categories. When a high-risk transaction is detected, the application automatically generates a fraud alert and notifies administrators for immediate action and transaction verification.

The application is developed using React and TypeScript for the user interface, Python and FastAPI for backend services, SQLite/MySQL for data management, and JWT authentication for secure user access and authorization. Experimental testing confirms that the system provides accurate fraud detection, efficient risk classification, secure authentication, and reliable real-time monitoring of transactions.

The AI-Based Fraud Detection System transforms traditional fraud monitoring approaches into an intelligent and proactive financial security platform capable of reducing financial losses, improving transaction safety, and enhancing real-time fraud prevention.