Fraud Detection · Risk Management
Scam proceeds can be moved through money-mule accounts before victims report fraud, making the funds difficult to trace. Banks also need reliable evidence to identify suspect accounts and intervene without generating a flood of false alerts.
Reported impact
Helps financial institutions identify money-mule accounts and intervene before scam transfers proceed. In the described example, a European private bank stopped a transfer after the receiving account matched confirmed scam-account intelligence.
Cybera's Money Mule Intelligence uses AI-generated personas that pose as believable potential victims, engage scammers, and continue conversations until a payment request reveals the receiving account details. Cybera says it operates hundreds of personas across thousands of conversations every day, identifying accounts while they are still active rather than after a victim reports a loss. Cybera verifies each account and attaches evidence and context to the intelligence, giving bank fraud teams more than a risk score when they decide whether to intervene. The service is available through Recorded Future, which gives financial institutions access to the account intelligence. In the described European private-bank example, the receiving account was checked against confirmed scam accounts in real time and the bank stopped a €60,000 transfer. The page does not identify conversation platforms, AI models, data interfaces, or storage architecture; it describes no separate ingestion or persistence products.
AI personas engage scammers until payment requests reveal account details; Cybera verifies the accounts and attaches evidence and context. Money Mule Intelligence is made available through Recorded Future, and a bank can check a receiving account against the intelligence in real time before allowing a transfer.
Cybera builds AI personas that pose as believable potential victims.
Cybera runs hundreds of these personas across thousands of conversations every day.
Cybera’s Money Mule Intelligence is now available through Recorded Future.
the bank checked the receiving account against a list of accounts confirmed to belong to scammers.
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
The CYBERA company profile reports an employee range of 11–50, which falls below the 250-employee SMB threshold.
Related
In-house Sample Oct 27, 2026 Cybera Scam victims often report fraud only after their money has been transferred and become difficult to trace. Banks need to identify money-mule accounts before they receive or move illicit funds, including before a payment… Cybera runs hundreds of these personas across thousands of conversations every day. Money Mule Intelligence · Recorded Future In-house Sample Sep 24, 2026 Mastercard Fraudsters are using AI to find vulnerabilities, adapt tactics, and scale attacks across more payment channels, while traditional transaction-by-transaction controls struggle to catch coordinated fraud and authorized… Last year, scam losses globally were estimated at $442 billion In-house Sample Jun 25, 2026 Telefónica Telefónica's compliance function must manage an expanding set of digital risks and increasingly complex international regulations without becoming inefficient, while also improving fraud detection and preserving rights… 