Inspiration

UPI has made digital payments fast and easy, but it has also led to an increase in fraud cases such as fake payment requests, phishing links, and unauthorized transactions. Seeing people around me lose money due to these scams inspired me to create a system that can identify suspicious UPI transactions early and help users stay safe using the power of AI.

What it does

The Smart UPI Fraud Detection System analyzes UPI transaction data in real time and detects potentially fraudulent activities. It studies transaction patterns such as amount, frequency, timing, and user behavior, and then classifies each transaction as legitimate or suspicious. If fraud is suspected, the system generates an alert so that preventive action can be taken immediately.

How we built it

We built the system by collecting sample transaction data and preprocessing it to remove inconsistencies. Machine learning techniques were used to train a classification model that learns common fraud patterns. The trained model is then integrated into a simple application that monitors transactions and displays alerts and risk status to the user.

Challenges we ran into

One of the biggest challenges was handling imbalanced data, since fraudulent transactions are much fewer than normal ones. Reducing false positives while still catching real frauds was also difficult. Additionally, understanding real-world UPI fraud patterns and designing a system that is both accurate and user-friendly required careful planning.

Accomplishments that we're proud of

We successfully built a working prototype that can detect suspicious transactions in real time. Achieving reliable predictions with limited data and creating a clear alert system for users are accomplishments we are proud of. The project also helped bridge the gap between theoretical AI concepts and practical financial applications.

What we learned

This project helped us gain a strong understanding of UPI systems, fraud detection techniques, and machine learning workflows. We learned the importance of data quality, feature selection, and evaluation metrics. Most importantly, we understood how AI can be responsibly used to solve real-world problems and improve digital payment security.