Apate sends AI agents undercover to get scammed so they can gather fraud intelligence
- ID
- 35442
- Status
- summarized
- Published
- 15 Oct 2026, 2:00 AM
- Fetched
- 15 Oct 2026, 2:16 AM
- Provider
- TechCrunch
- Category
- technology
- Original URL
- https://techcrunch.com/2026/10/14/apate-sends-ai-agents-undercover-to-get-scammed-so-they-can-gather-fraud-intelligence/
- Source URL
- https://techcrunch.com/feed/
Summary
- Score
- 5.5
- Created
- 15 Oct 2026, 2:16 AM
- Tags
- Audience
- developersai_ml_learnersai_agent_userssaas_startup_founders
What happened
Apate.ai, a startup founded in 2024 out of research at Macquarie University in Sydney, runs thousands of AI agents that pose as scam targets — answering scam calls, replying to shady messages, clicking phishing links and joining social groups — to keep fraudsters talking. The company says the fleet has burned 17 years of scammers' time across 2.5 million conversations, and the payoff is intel that usually only surfaces after a victim is hit: scammers' bank accounts, crypto wallets, phone numbers, malicious sites, and the brands being impersonated. It sells that feed to banks, telcos, government agencies and utilities, and is a Startup Battlefield 200 finalist pitching at TechCrunch Disrupt in San Francisco through October 15.
Why it matters
The output categories Apate claims to extract — mule bank accounts, crypto wallet addresses, phone numbers, malicious domains and impersonated brands — are the same inputs a fraud, KYC or trust-and-safety pipeline consumes as blocklists and risk signals. If you build payments, onboarding, telco or public-service software, the decision to make is whether you buy this class of adversarial-agent intel or keep relying on post-victim reports; the FBI's cited 2025 figures (22,000+ AI-related crime complaints, ~$893M in losses, $632M of it investment scams) show how much volume is now AI-driven on the attacker side. Treat the 17-years/2.5-million-conversations numbers as vendor-reported until you can validate a sample feed against your own fraud data.
Discussion angle
Would you plug an externally sourced scam-intel feed into an automated blocklist or account-freezing flow, and what would you require before trusting it — sample data, a precision/recall figure, or a manual review step? Worth arguing both sides live.