Phone scams are a growing global issue, particularly affecting vulnerable groups such as the elderly, who may be less tech-savvy. Fraudsters often impersonate trusted institutions like banks or government agencies, using increasingly sophisticated methods powered by AI. While existing solutions like call-blocking apps help by flagging known scam numbers, they struggle to catch new or evolving scams, leaving gaps in protection.

The Idea: AI-Powered Call Interception

One way to tackle this could be a real-time call-screening platform that uses AI to dynamically assess suspicious calls. Here's a simplified breakdown:

• User Setup: An app allows users to monitor incoming calls and provides localized scam alerts.
• Real-Time AI Agent: If a call seems suspicious, the AI can join the conversation and ask verification questions (e.g., "What is your bank's official contact number?").
• User Notification: The AI analyzes responses and notifies the user via the app, suggesting whether the call is likely legitimate or fraudulent.

Over time, this could expand to integrate with telecom providers for automated pre-call screening or adapt to detect newer fraud tactics like phishing.

Why This Could Work

Unlike static call-blocking services, this approach actively verifies unknown callers, making it harder for scammers to bypass. Telecom companies and banks might also find value in reducing fraud-related costs and complaints. Potential monetization paths include:

• A freemium model offering basic features for free and advanced AI interrogation for a subscription.
• Licensing the service to telecoms for broader network-level deployment.
• Providing anonymized data insights to cybersecurity firms.

Comparison With Existing Solutions

Current tools like Truecaller or Hiya rely on crowdsourced databases and caller ID, which lag behind emerging scams. This idea stands out by using AI-driven questioning in real time, offering a more dynamic defense.

While challenges like privacy concerns and scammer adaptation exist, careful design (e.g., encryption, user opt-ins) and iterative AI training could mitigate risks. Starting with a simpler MVP—such as an enhanced call metadata analyzer—could help validate the concept before introducing live AI interaction.