Ideaz

With the growth in digital payments, online banking, and e-commerce, financial fraud has emerged as a major threat for consumers and businesses alike. Conventional fraud detection techniques use static rules, which usually do not detect newer or more complex attacks. In contrast, legitimate users occasionally get caught, leading to user frustration and mistrust.

My proposal is to create an AI-Powered Fraud Detection System that learns continuously from patterns of transactions and user behavior. Through machine learning and anomaly detection, the system would:

Monitor transactions in real-time to spot suspicious spending or access patterns.

Identify suspicious behavior immediately and warn users before money is lost.

Minimize false alarms by learning each user's normal behavior.

Learn continuously from new cases of fraud.

For instance, if a customer typically spends in Hyderabad but a sudden high-value transaction from somewhere else shows up, the system would flag it as high-risk. Likewise, multiple failed login attempts or abnormal purchases can signal alerts.

Target users are banks, online shopping websites, and normal consumers who wish to feel safe while making online transactions. This solution not only helps cut down on financial losses but also establishes confidence in digital transactions, which is crucial in the current cashless economy.

This issue is important to me because as more and more individuals switch to UPI and digital wallets, even students become vulnerable to scams and phishing. A smart fraud detection mechanism can enable individuals to make transactions fearlessly, with the assurance that AI is working behind the scenes to safeguard them.

Tags: AI, fraud detection, fintech, security, machine learning