How South Korea Is Using AI to Detect Crypto Market Manipulation

Key takeaways

• South Korea is transitioning crypto market surveillance to AI-driven systems, in which algorithms automatically detect suspicious trading activity, replacing manual processes.

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

• Through 2026, the Financial Supervisory Service plans to enhance AI capabilities with tools to detect coordinated trading account networks and trace manipulation funding sources.

• Regulators are exploring proactive intervention measures, such as temporary transaction or payment suspensions, to freeze suspicious activity early and prevent the withdrawal of illicit gains.

South Korea is advancing its cryptocurrency market oversight by shifting to AI-driven surveillance. Algorithms now perform the initial detection of suspicious activities instead of relying solely on human investigators.

As crypto 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 Assets Intelligence System for Trading Analysis (VISTA). This upgrade 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 Korea’s financial regulators are using upgraded AI systems to automatically detect crypto 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 enhancing its crypto monitoring tools

Crypto markets produce massive volumes of data across exchanges, tokens and timelines. Manipulative 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 challenging at the current market scale. As interconnected trading patterns grow more intricate, automated systems are designed to continuously scan and flag potential issues.

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

What VISTA does and how the recent upgrade improves it

VISTA serves as the FSS’s primary platform for examining unfair trading in digital assets. In its earlier version, analysts had to specify suspected manipulation time frames before running analyses, which restricted the detection range.

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

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

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

How the automated detection operates

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

The model scans every possible sub-period, identifying patterns associated with manipulation without requiring investigators to determine where misconduct may have occurred. Examples of such patterns include sharp price spikes followed by rapid reversals or unusual volume surges.

Rather than supplanting human oversight, the model prioritizes high-risk segments, enabling 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 lasting less than five minutes, a time frame too short for most human-led monitoring systems to catch reliably.

Upcoming AI enhancements through 2026

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

• Tools designed to identify networks of coordinated trading accounts: These systems aim to detect clusters of accounts acting in sync, a common feature of organized manipulation schemes.

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

• Tracing the origin of funds used in manipulation: Linking suspicious trades to funding sources could strengthen enforcement cases and reduce the ability of bad actors to obscure their tracks.

Did you know? Early market surveillance algorithms in traditional finance were originally designed to detect insider trading in equities, not crypto. Many of today’s tools are adaptations of models built decades ago for stock exchanges.

Shift toward proactive intervention in South Korea

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

This approach aims to prevent gains from being withdrawn or laundered early. While not yet finalized, it suggests a shift by regulators from reactive to preventive enforcement.

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

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

Strengths and limitations of AI surveillance

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

For exchanges, AI-driven oversight 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 manipulative patterns that previously evaded attention. While detection begins algorithmically, real-world penalties remain significant.

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

AI tools support, not replace, human investigators.

Shaping of a new enforcement framework

South Korea’s strategy involves AI models built around continuous monitoring, automated prioritization and swifter 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 regulating digital assets in an era of algorithmic trading and mass participation.