How South Korea Is Using AI to Detect Crypto Market Manipulation

Key takeaways

• South Korea is transitioning cryptocurrency market surveillance to AI-powered systems, in which algorithms automatically detect suspicious trading activities, replacing manual processes.

• The new detection model employs a sliding window grid search technique, scanning overlapping time segments to detect abnormal patterns, such as unusual volume increases.

• Through 2026, the Financial Supervisory Service plans to enhance artificial intelligence capabilities with tools to detect networks of coordinated trading accounts and track sources of manipulative financing.

• Regulators are exploring proactive intervention measures, such as temporary suspensions of transactions or payments, to freeze suspicious activities early and prevent the withdrawal of illicit profits.

South Korea is making progress in cryptocurrency market oversight by moving to AI-powered surveillance. Algorithms now perform initial detection of suspicious activity rather than relying solely on human investigators.

As cryptocurrency trading grows faster, more decentralized, and increasingly difficult to monitor manually, regulators are leveraging artificial intelligence to identify irregularities and anomalies more quickly.

Central to this evolution is the Financial Supervisory Service’s (FSS) enhanced Virtual Asset Intelligence System for Trade Analysis (VISTA). This update reflects the recognition that traditional, manual, case-by-case probes can no longer keep pace with today’s dynamic digital asset markets.

This article explains how South Korean financial regulators are using enhanced AI systems to automatically detect crypto market manipulation, improve surveillance, analyze trading patterns, and plan advanced tools. It also explores faster intervention and aligning cryptocurrency oversight with broader financial markets.

Why South Korea is improving its cryptocurrency monitoring tools

Crypto markets produce huge volumes of data on exchanges, tokens and schedules. Manipulation tactics, such as pump and dump schemes, wash trading, or spoofing, often create sudden bursts that are difficult to detect. Manually identifying suspicious periods in crypto activity has become increasingly difficult at the current scale of the market. As interconnected trading patterns become more complex, automated systems are designed to continually scan and flag potential problems.

This automation aligns with Korea’s broader effort to strengthen oversight of digital markets, particularly as cryptocurrencies have become more deeply integrated with retail investors and the broader financial system.

What VISTA does and how the recent update improves it

VISTA serves as the FSS’s primary platform for examining unfair trading in digital assets. In its previous version, analysts had to specify suspicious tampering timeframes before performing analyses, which restricted the detection range.

The recent update adds an automated detection algorithm that can independently identify potential tampering periods without the need for manual input. The system now searches the entire data set, allowing researchers to review suspicious intervals that might otherwise go undetected.

According to the regulator, the system successfully identified all known manipulation periods in internal testing using completed investigation cases. It also pointed out additional intervals that had been difficult to detect using traditional methods.

Did you know? Some crypto exchanges process more individual transactions in a single hour than traditional stock exchanges do in an entire trading day, making continuous automated surveillance essential for regulators seeking to monitor risks in real time.

How auto detection works

Applying a sliding window grid search approach, the algorithm divides business data into overlapping time segments of different durations. It then evaluates these segments for anomalies.

The model analyzes all possible subperiods, identifying patterns associated with the manipulation without requiring researchers to determine where the misconduct may have occurred. Examples of such patterns include strong price spikes followed by rapid pullbacks or unusual increases in volume.

Instead of supplanting human oversight, the model prioritizes high-risk segments, allowing teams to focus on critical windows rather than manually reviewing the entire data set.

Did you know? In crypto markets, price manipulation can sometimes occur in windows lasting less than five minutes, a period of time too short for most human-run monitoring systems to reliably detect.

Upcoming AI improvements through 2026

The FSS has secured funding for incremental AI improvements through 2026. Key planned features include:

• Tools designed to identify networks of coordinated trading accounts: These systems aim to detect groups of accounts that act in synchronization, a common characteristic of organized manipulation schemes.

• Large-scale analysis of texts related to trading in thousands of cryptoassets: By examining abnormal promotional activity or narrative spikes alongside market data, regulators hope to better understand how attention shocks and price movements interact.

• Tracking the origin of funds used in manipulation: Linking suspicious transactions to funding sources could strengthen law enforcement cases and reduce the ability of bad actors to cover their tracks.

Did you know? The first market surveillance algorithms in traditional finance were originally designed to detect insider trading in stocks, not cryptocurrencies. Many of today’s tools are adaptations of models created decades ago for stock exchanges.

Shift towards proactive intervention in South Korea

South Korea’s AI surveillance push seeks faster responses. The Financial Services Commission is considering a stop-payment mechanism that could temporarily block transactions linked to suspected manipulation.

This approach aims to prevent profits from being withdrawn or laundered early. While it is not yet finalized, it suggests that regulators move from reactive enforcement to preventive enforcement.

Preventive actions raise important governance questions around thresholds, oversight and the risk of false positives, issues that regulators will need to carefully address.

This cryptocurrency-focused initiative parallels efforts in conventional capital markets. The Korea Stock Exchange is implementing an artificial intelligence-based monitoring system to identify stock manipulation earlier. The idea is to create a unified approach for all asset classes, combining trading data, behavioral signals and automated risk assessment.

Strengths and limitations of AI surveillance

AI-based systems are adept at detecting pattern-driven repetitive misconduct, such as wash trading or coordinated price spikes. They improve consistency by flagging suspicious behavior even when it occurs in small or short-duration windows.

For exchanges, AI-powered monitoring raises expectations around data quality and monitoring capabilities. It also increases cooperation with regulators. With AI models, surveillance becomes continuous rather than episodic.

Traders and issuers should expect greater scrutiny of subtle manipulation patterns that previously escaped attention. While detection begins algorithmically, real-world sanctions are still important.

But automated surveillance has certain limitations. Cross-location manipulation, off-platform coordination, and subtle narrative engineering remain difficult to detect. AI models also require regular evaluation to avoid bias, deviations, or the signaling of legitimate activities.

AI tools support, not replace, human researchers.

Shaping a new law enforcement framework

South Korea’s strategy involves AI models built around continuous monitoring, automated prioritization and faster action. As these systems evolve, balancing efficiency with transparency, due process and accountability will be key.

The implementation of these models will shape not only Korea’s crypto markets, but also how other jurisdictions approach digital asset regulation in an era of algorithmic trading and mass participation.