SCAMGUARD is an AI-powered web app that uses NLP, ML, and Speech Recognition to detect and block scam calls in real time, helping users stay safe from fraud and unwanted threats.
Inspiration : The rise in scam calls and fraudulent schemes inspired us to create SCAMGUARD. We wanted to develop a smart solution that could protect users in real time by understanding and intercepting scam attempts using AI.
What it does : SCAMGUARD analyzes live phone conversations using NLP, ML, and Speech Recognition to detect suspicious behavior or scam patterns. It alerts users instantly, helping them avoid potential fraud and stay safe.
How we built it : We used speech-to-text APIs to convert call audio into text, applied NLP models to analyze intent and sentiment, and trained a machine learning model on real scam call datasets. The frontend was built with a user-friendly web interface for accessibility.
Challenges we ran into : We faced challenges in obtaining quality scam call data, ensuring real-time processing speed, and balancing detection accuracy with false positives. Integrating multiple AI components smoothly also required careful system design.
Accomplishments that we're proud of : We successfully developed a working prototype capable of analyzing calls in real time and flagging scams with high accuracy. Creating a seamless pipeline from voice input to actionable feedback was a major achievement.
What we learned : We learned how to integrate complex AI technologies in real-time systems, the importance of clean and labeled datasets for model training, and how to design user-centric security features with a practical impact.
What's next for SCAMGAURD : We plan to expand SCAMGUARD into mobile platforms, enhance our ML models with larger datasets, and add support for multilingual scam detection. We also aim to partner with telecom providers for wider adoption and impact.
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