Bybit raises $300 million for thousands of users through AI-enhanced fraud prevention: report

In addition to the chargebacks, Bybit blocked three million credential stuffing attempts in 2025 related to account takeover plans.

Bybit has reported recovering $300 million for thousands of users at a time when crypto-related fraud remains high across the industry.

The exchange attributed these efforts to an AI-powered fraud detection system that intervenes before people lose their money.

Results of the security initiative

Bybit shared the results of its 2025 Security Initiative, to report on social media,

“We raised the standard by 2025, catching $300 million worth of impersonation scams and fraud through our new AI-driven risk framework.”

The announcement comes as crypto fraud continues to burden the industry, with data from Chainalysis showing that $17 billion in digital assets will be stolen through scams and fraud in 2025.

The report shows that the exchange reported $500 million in withdrawals for review in the fourth quarter alone. Of that amount, $300 million was successfully intercepted and recovered, protecting the savings of more than 4,000 users.

During the same period, Bybit’s proprietary AI models identified 350 high-risk investment fraud addresses using on-chain data, protecting 8,000 people from potential withdrawal losses. The company also reported that in 2025 it had blocked more than 3 million credential filling attempts related to account takeover attempts.

Additionally, the system automatically tagged 350 suspicious addresses, and manually tagged another 600 through internal ticketing operations, preventing an additional $1 million in impending fraud losses.

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David Zong, Head of Group Risk Control at Bybit, said in a statement that the company’s goal by 2025 was to transform risk management into an active and intelligent guardian by integrating AI and on-chain monitoring.

“By integrating AI-driven on-chain monitoring with real-time intelligence from industry partners like TRM, Elliptic and Chainalysis, we are not only protecting Bybit users, but also helping to map the DNA of fraudulent networks,” he wrote.

Three-tiered risk framework

Bybit’s protection model structures potential scam scenarios into three escalating levels, while maintaining normal trading activity. At the lowest risk level, the platform uses big data analytics to detect unusual activity, such as mass withdrawals to newly created addresses, and deploys automated surveys to support the Risk Operations team in blacklisting suspicious destinations.

Real-time alerts are triggered during the withdrawal process for medium-risk cases, such as accounts flagged via data-population databases or linked to questionable withdrawal addresses. This in turn prompts individuals to review transactions that could be influenced by social engineering tactics.

At the highest level, wallet addresses associated with confirmed scams face immediate withdrawal blocking and a mandatory one-hour cooling-off period.

The report concluded with an overview of standardized monitoring indicators for broader industry use, including an anti-credential stuffing engine, real-time on-chain AI pattern recognition for pork slaughter flows, an integrated intelligence hub combining tools from TRM Labs, Elliptic and Chainalysis, and an end-to-end cross-chain tracing model for tracking illicit funds.