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Binance said its artificial intelligence systems helped protect more than 8 million users and prevent about $4.6 billion in potential losses during the first half of 2026.
The cryptocurrency exchange said it continues to expand AI across its security, anti-fraud and compliance systems.
Binance said its AI tools monitor abnormal trading activity, account takeover attempts, scams and transaction fraud.
According to Binance, its systems intercepted millions of scam and phishing attempts during the first half of 2026.
The company also blacklisted more than 42,000 malicious addresses.
Binance said it issued more than 14,000 real-time warnings every day during the period.
The company currently operates more than 100 AI models across its anti-fraud and anti-scam systems.
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Binance said its teams develop, train and supervise these models in-house. Human risk analysts set thresholds, review complex cases and retrain models as new threats emerge.
Binance said AI models now handle 80% to 90% of real-time risk decisions across its fraud controls.
The technology also supports about 45% of human review workflows.
Human analysts continue to handle cases that require detailed checks and contextual judgment.
The company said AI systems assess activity across identity verification, account security, payments and transaction screening.
Most decisions happen automatically in real time. Binance said this allows its systems to protect users at scale without disrupting legitimate activity.
Binance said its AI-powered KYC review systems have achieved up to 100 times greater operational efficiency than manual processes in specific workflows.
Specialists still review higher-risk cases.
The company uses both proprietary systems and external AI and foundation models. Its internal models focus on risks specific to the Binance platform.
Binance also operates an internal Red Team to test its security controls and identify potential weaknesses.
Chief Security Officer Jimmy Su said the exercises help the company identify vulnerabilities before attackers exploit them.
He said the process also helps Binance test its controls under realistic conditions and strengthen its security systems.
Binance said social engineering remains a major challenge, particularly in peer-to-peer trading.
The company uses computer vision models to detect fake proof-of-payment images.
These systems analyse transaction details and identify subtle image manipulation.
AI handles large-scale screening, while human reviewers provide deeper checks. Binance then uses findings from new attack patterns to improve its models.
The company also applies governance controls throughout the AI model lifecycle. These controls cover development, validation, deployment and ongoing monitoring.
Binance said its compliance teams also use AI-assisted automation for KYC fraud detection and transaction monitoring.
The company has deployed more than 24 AI initiatives across user onboarding, screening escalations and partner due diligence.
AI is also expanding across Binance’s internal operations.
The company said its internal agentic tool has reached about 72% adoption among its teams. Binance supports the rollout through training, prompt-engineering programmes and structured oversight.
Binance said its AI systems follow a privacy-focused approach. The framework includes data minimisation, purpose limitation and user-rights safeguards.
The company said it will continue combining AI-driven automation with human expertise as digital scams become more sophisticated.
[…] Binance AI Systems Protect 8 Million Users and Prevent $4.6 Billion in Potential Losses […]