Main Takeaways
- In the first half of 2026, Binance's AI-driven risk systems helped protect more than 8 million users and prevent approximately $4.6 billion in potential losses.
- AI now informs 80% to 90% of real-time risk decisions across identity verification, account security, payments and transaction screening, with human reviewers handling the edge cases that need finer judgment.
- Over 100 AI models power anti-fraud controls to detect scams, forged documents, and social engineering attempts at scale.
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A forged payment screenshot or tampered identity document can now be generated in seconds, and a scam script can be personalized for a single target at almost no cost. As deception becomes cheaper to produce, the only realistic way to keep pace is to evaluate risk continuously, at scale and in real time.
That’s where AI has become central to how Binance protects its users. In the first half of 2026, Binance's AI-driven risk systems helped protect more than 8 million users and prevent approximately $4.6 billion in potential losses, covering abnormal trading activity, account takeover attempts, scams, and transaction fraud.
This blog breaks down the numbers: where AI runs across the user journey, how in-house and external models work together, why human reviewers remain central to the loop, and how the approach reaches beyond loss prevention into compliance and internal operations.
Binance Turns 100+ AI Models Into Real-Time User Protection
In H1 2026, Binance intercepted millions of scam and phishing attempts, blacklisted more than 42,000 malicious addresses, and issued over 14,000 real-time warnings daily.
These outcomes come from AI infrastructure that Binance builds, trains, and supervises in-house. We now run more than 100 AI models across our anti-fraud and anti-scam controls, with human risk analysts setting the thresholds, reviewing edge cases, and retraining models as new scam patterns emerge.
In KYC (identity verification), our AI-enabled review pipelines have delivered up to 100x operational efficiency gains over manual processes in specific workflows, while keeping specialists in the loop on higher-risk cases.
Binance Embeds AI Risk Protection Across the User Journey
AI is embedded in the full user journey, wherever a real-time risk decision needs to be made. That includes identity verification, account security (such as detecting signs of account takeover), payments, and broader transaction protection and screening.
Every action is evaluated as it happens, and the large majority are resolved automatically. This is what allows protection to operate at platform scale without slowing down legitimate users, since most people never see the checks running behind their activity.
A Hybrid Model Stack, Built In-House and Beyond
Binance uses a blend of proprietary and external technology. In-house models are purpose-built for the specific risks observed on the platform, while leading external AI and foundation models handle broader reasoning tasks.
This hybrid approach keeps detection at the frontier of AI capability while preserving an edge that is tailored to Binance's users, products, and scale. It also means new capabilities can be adopted quickly as the wider AI field advances.
Alongside the detection models, Binance runs an internal Red Team that tests defenses the way a real adversary would, including probing how emerging technology could be turned against the platform.
Jimmy Su, Chief Security Officer at Binance, explains: “These exercises help us identify weaknesses before attackers do, validate that our controls work under realistic conditions, and continuously strengthen the people, processes and technology protecting our users. In security, you cannot simply assume your defenses will work – you have to challenge them.”
Example: Layered Defenses Against Social Engineering
Some attacks are designed to bypass technology entirely and target trust instead. P2P trading is one area where social engineering attempts are especially common.
At Binance, in-house computer vision models detect fake proof of payment images by analyzing transaction details and subtle image manipulations. Natural language processing models identify scam messages in chat. Traditional machine learning models assess the risk level of each order. On top of that, large language models help interpret message intent and catch scam text embedded inside images.
Machines Screen, Humans Decide, Models Learn
While AI is exceptional at discovery and coarse screening, humans remain essential for fine-grained validation and contextual judgment. Liveness and identity document forgery detection is a good example. AI can rapidly pre-screen large volumes and surface suspicious ones, but many edge cases still require human reviewers to label and judge more precisely.
As new attack patterns emerge, AI helps uncover and identify them, and those findings are then used to fine-tune the models. The result is a continuous loop where AI provides scale and people provide accuracy. Across fraud controls, AI models make 80% to 90% of real-time risk decisions and assist in around 45% of human review workflows.
To keep AI accountable at this scale, Binance applies structured model governance throughout the model lifecycle, from development and validation to deployment and ongoing monitoring. Models are tested for bias and accuracy before they go live, and their performance is tracked continuously against real-world outcomes.
When a model's precision drifts or false-positive rates rise beyond acceptable thresholds, as found by the aforementioned human review process, it’s automatically flagged for retraining. This ensures that automated decisions remain fair, explainable, and auditable — not just fast.
Beyond Loss Prevention: Compliance and Internal Efficiency
Beyond protecting our users, our compliance teams use AI-assisted automation tools to scale and standardize processes such as KYC fraud detection and transaction monitoring execution.
More than 24 AI initiatives have been deployed across user onboarding, screening escalations, and partner due diligence. These models sort cases by priority, identify patterns across complex datasets – including on-chain activity and device fingerprints – and route the right issues to human reviewers while minimizing false alerts.
Internally, the effect is similar. Analysts use AI to move faster across analysis, monitoring, and model deployment, while operations teams use it to assist with investigations and case details. In both cases, the aim is the same: handing off repetitive work so people can focus on higher-value judgment calls. That's reflected in adoption – Binance’s internal agentic tool now sees roughly 72% uptake across teams, supported by company-wide training, prompt engineering programs, and structured oversight.
Underpinning all of this is a commitment to privacy by design. Binance's AI systems operate within a privacy-first framework, where data minimization, purpose limitation, and user-rights safeguards are embedded into how AI models are built and deployed. The goal is straightforward: protect users from financial abuse and harm without compromising their right to data privacy — and to do so transparently, so that users understand that safeguards are working in the background without their data being used beyond what the task requires.
Final Thoughts
At Binance, user protection is a priority we keep investing in. AI provides the scale; people provide the judgment. As the tools for producing deception get cheaper, that combination has to keep improving – which means retraining models, refining thresholds, and keeping experienced analysts on the cases that need them.
Disclaimer: This content is presented to you on an "as is" basis for general information and educational purposes only, without representation or warranty of any kind. It should not be construed as financial advice, nor is it intended to recommend the purchase of any specific product or service. Digital asset prices can be volatile. The value of your investment may go down or up and you may not get back the amount invested. You are solely responsible for your investment decisions and Binance is not liable for any losses you may incur. Not financial advice. For more information, see our Terms of Use and Risk Warning.
