ScamStop

ScamStop protects from scams by using AI to detect malicious callers in real time. ScamStop analyzes messages, conversations and behavior to identify scams as they happen!

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ScamStop (scamstop.app)

Inspiration

Phone scams and spam calls have evolved into a digital plague. Most current solutions are stuck in the past, relying on static databases of known spam numbers. The problem? Scammers are experts at rotating numbers, spoofing identities, and shifting tactics faster than a database can update. Traditional blocking apps are essentially bringing a knife to a laser fight.

We wanted to see if modern AI could play defense more intelligently. Instead of just asking, "Who is calling?" we wanted to ask, "What are they actually saying?" The Amazon Nova AI Hackathon provided the perfect sandbox. Using Amazon Nova foundation models, we set out to build ScamStop—a system that doesn't just check Caller ID, but actually understands the anatomy of a scam in real time. Our goal was to create a protective layer that lets users answer their phones without that "is this a trap?" feeling.

What it does

ScamStop is an AI-powered system designed to intercept and neutralize phone fraud as it happens.

Rather than filtering by a simple "black list," ScamStop (available at scamstop.app) analyzes the linguistic and behavioral signals of an incoming call. It looks for the "red flags" that humans often miss under pressure—urgency, inconsistent stories, and specific predatory patterns.

When a threat is detected, ScamStop can:

  • Alert the user with a high-confidence scam warning.
  • Screen or block calls that match known malicious behaviors.
  • Explain the "Why": It provides specific insights into why a call was flagged.
  • Evolve: It learns from new tactics, ensuring it stays ahead of the curve.

By moving from number-based filtering to intent-based detection, we’ve built a system that stops scammers even when they use a brand-new "clean" number.

How we built it

We designed ScamStop as a real-time, AI-assisted analysis engine. Leveraging the power of Amazon Nova, we focused on the nuances of communication—identifying urgency tactics, financial requests, and impersonation patterns.

The Architecture

  • Call Monitoring Layer: Incoming calls are processed through a screening gateway, allowing the AI to analyze the interaction before the user fully engages.
  • AI Analysis Engine: Powered by Amazon Nova models, this engine parses conversational intent. It looks for behavioral cues rather than just digits.
  • Risk Evaluation System: The system assigns a real-time risk score. If the score crosses a specific threshold, the "stop" in ScamStop kicks in.
  • User Alert System: A clean interface warns the user, giving them the context they need to decide whether to continue the conversation.

Challenges we ran into

The hardest part? Nuance. A legitimate call from a bank teller can occasionally sound like a scam on the surface. Minimizing "false positives" while maintaining a high catch rate required significant thought regarding the logic of suspicious behavior.

Then there’s the real-time hurdle. In a live conversation, every millisecond counts. We had to ensure ScamStop could process and flag signals fast enough to provide meaningful warnings during a live interaction, rather than just providing a post-call summary.

Accomplishments that we’re proud of

We’re incredibly proud to have moved the needle from reactive to proactive protection. Demonstrating that AI can successfully interpret "scammer intent" and call behavior in a live prototype is a huge win for user safety.

By building ScamStop, we’ve created more than just an app; we’ve created a proof of concept for a world where you don't have to ignore every "Unknown Number" call. We’re tackling a multi-billion dollar fraud problem with a scalable, intelligent solution.

What we learned

The biggest takeaway: Context is king. A phone number is just a label, but intent is a fingerprint. We learned that for an AI safety system to be effective, it must be fast, transparent, and—most importantly—trustworthy. Users need clear explanations and reliable signals to feel confident using the system.

What’s next for ScamStop

While the current version of ScamStop is a powerful prototype, we’re just getting started. The roadmap for scamstop.app includes:

  • Deep OS Integration: Working closer with mobile operating systems for a seamless "silent guardian" experience.
  • Smarter Screening Agents: Developing AI agents that can automatically interact with suspicious callers.
  • Pattern Learning: Aggregating data to learn and adapt to new scam types across large datasets.
  • Real-time Explanations: Providing even more granular feedback on exactly why a call was flagged.

Ultimately, our vision is to make ScamStop the definitive standard for voice security, turning the phone back into a tool of connection rather than a source of anxiety.

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