South Korea deploys real-time AI crypto surveillance

South Korea’s Financial Supervisory Service has deployed a real-time AI system that scans trading data, news and online content to flag suspected crypto price manipulation. Summary Generative AI and machine learning will screen trades, news, exchange notices, and online discussions. The platform targets rapid price manipulation, wash trading, collusive activity, and false promotional claims. Human investigators will review AI-generated reports before opening a detailed analysis or formal investigation. Future updates will add cross-exchange fund-flow analysis and on-chain transaction tracking. AI crypto surveillance screens price and volume spikes The Financial Supervisory Service said in its Aug. 20 announcement that the platform combines generative AI with machine learning to automate parts of a process that previously required investigators to examine large volumes of exchange data manually. Built around real-time trading information, the system first searches for assets showing abnormal changes in price or volume. It then compares the activity with patterns drawn from the regulator’s previous investigations, allowing staff to focus on trades that share features with known forms of market abuse. Among the patterns listed by the FSS are the “racehorse” type, in which a token moves sharply during a short period, and the “cage” type, which involves a steep rise in an asset while deposits or withdrawals are suspended or restricted. You might also like: South Korea proposes new FIU powers to investigate unregistered crypto firms The latest platform extends an algorithm introduced in January, when the regulator began using AI to identify suspected price manipulators and isolate the periods and orders linked to their activity. Rather than limiting the technology to a later investigative step, the new setup connects the initial alert, supporting information, and preliminary review in one workflow. For possible wash trading or coordinated trading, the FSS applies Benford’s Law alongside machine-learning models. Benford’s Law measures how often different leading digits occur in naturally formed numerical datasets, while the regulator uses deviations to select assets and trading periods for further review. News and chat-room scans test each alert Once a token records an unusual move, generative AI checks relevant news and exchange announcements for a plausible cause. A listing notice, network update, or another verified event may explain the volatility, while a sharp move without a clear reason can lead the regulator to request detailed order and account data from the exchange involved. Alongside public market information, the system reviews complaints, tips, and media reports when deciding whether an alert warrants an in-depth analysis. Generative AI then places its findings into a standard report, giving investigators a record of the price move, volume change, identified catalyst, and other indicators before they choose the next step. Online promotion has also entered the surveillance process.