How South Korea is using AI to detect crypto market manipulation

Key takeaways

• South Korea is moving crypto market surveillance toward AI-based systems, in which algorithms automatically detect suspicious trading activities, replacing manual processes.

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

• By 2026, the Financial Monitoring Service plans to enhance AI capabilities with tools to detect networks of coordinated trading accounts and trace sources of financing for manipulation.

• Regulators are exploring proactive response measures, such as temporary suspensions of transactions or payments, to quickly freeze suspicious activity and prevent the withdrawal of illicit gains.

South Korea is strengthening its surveillance of the cryptocurrency market by moving to AI-based surveillance. Algorithms now perform the initial detection of suspicious activity instead of relying solely on human investigators.

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

The Financial Monitoring Service’s (FSS) Enhanced Virtual Asset Intelligence for Transaction Analysis (VISTA) system is at the heart of this development. This upgrade reflects the recognition that traditional, manual, case-by-case investigations can no longer keep up with the dynamic pace of today’s digital asset markets.

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

Why South Korea is improving its crypto surveillance tools

Crypto markets produce huge volumes of data on trades, tokens, and time frames. Manipulative tactics such as pump and dump schemes, wash trading or impersonation often create sudden outbursts that are difficult to detect. Manually identifying suspicious periods in crypto activity has become increasingly difficult at today’s market scale. As interconnected business models become increasingly complex, automated systems are designed to continually analyze and report potential issues.

This automation is part of Korea’s broader efforts to strengthen oversight of digital markets, especially as crypto has become more deeply integrated with retail investors and the financial system as a whole.

What VISTA does and how the recent update improves it

VISTA is the FSS’s primary platform for examining unfair trading of digital assets. In its previous version, analysts had to specify time limits for suspected manipulation before running scans, which limited the detection range.

The recent upgrade adds an automated detection algorithm that can independently identify potential tampering periods without manual input. The system now searches the entire data set, allowing investigators to examine suspicious intervals that might otherwise go unnoticed.

According to the regulator, the system successfully identified all known periods of manipulation during internal testing using completed investigation files. He also reported additional intervals that are difficult to detect with traditional methods.

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

How Automated Detection Works

By applying a sliding window grid search approach, the algorithm divides trading data into overlapping time segments of varying durations. It then evaluates these segments for anomalies.

The model analyzes all possible subperiods, identifying patterns associated with manipulation without investigators needing to determine where misconduct may have occurred. Examples of such trends include sharp price increases followed by rapid reversals or unusual volume increases.

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

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

Upcoming AI Improvements through 2026

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 acting in synchrony, a common feature of organized manipulation schemes.

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

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

Did you know? The first market surveillance algorithms in traditional finance were initially designed to detect insider trading in stocks, not cryptocurrencies. Many current tools are adaptations of models built decades ago for stock markets.

Shift to proactive intervention in South Korea

South Korea’s AI surveillance campaign aims to get faster answers. The Financial Services Commission is considering a payment suspension mechanism that could temporarily block transactions linked to suspected manipulation.

This approach aims to prevent winnings from being withdrawn or laundered prematurely. Although not yet finalized, it suggests that regulators will move from reactive to preventative enforcement.

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

This crypto-focused initiative parallels efforts in conventional capital markets. The Korea Stock Exchange is implementing an AI-based monitoring system to identify stock manipulations earlier. The idea is to create a unified approach across asset classes, combining trading data, behavioral indices and automated risk assessment.

Strengths and Limitations of AI Surveillance

AI-based systems are able to detect repetitive misbehavior, such as fictitious trades or coordinated price spikes. They improve consistency by flagging suspicious behavior, even when it occurs in short or short-duration windows.

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

Traders and issuers should expect greater scrutiny of subtle manipulation patterns that previously escaped attention. Even if detection begins algorithmically, the actual penalties remain significant.

But automated monitoring has certain limitations. Cross-site manipulation, off-platform coordination, and subtle narrative engineering remain difficult to detect. AI models also require regular evaluation to avoid bias, drift, or reporting of legitimate activity.

AI tools support, not replace, human investigators.

Development of a new application framework

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

The implementation of these models will shape not only Korean crypto markets, but also how other jurisdictions approach the regulation of digital assets in the era of algorithmic trading and mass participation.