Key conclusions

• South Korea is shifting from cryptocurrency market surveillance to artificial intelligence-based systems in which algorithms automatically detect suspicious trading activity, replacing manual processes.

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

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• By 2026, the Financial Conduct Service plans to improve artificial intelligence capabilities with tools to detect coordinated networks of trading accounts and track sources of financing for manipulation.

• Regulators are exploring proactive intervention measures, such as temporarily suspending transactions or payments, to freeze suspicious activity sooner and prevent the withdrawal of illicit profits.

South Korea is improving supervision of the cryptocurrency market by switching to artificial intelligence-based supervision. Algorithms now perform initial detection of suspicious activity instead of relying solely on human investigators.

As cryptocurrency trading becomes faster, more decentralized, and increasingly complex to monitor manually, regulators are using artificial intelligence to more quickly identify irregularities and anomalies.

A key element of this evolution is the Financial Supervisory Service’s (FSS) enhanced Virtual Asset Analysis for Transaction Analysis (VISTA) system. This update reflects the recognition that conventional, manual and individual research is no longer able to keep pace with today’s lively digital asset markets.

This article explains how South Korean regulators are using improved artificial intelligence systems to automatically detect cryptocurrency market manipulation, improve supervision, analyze trading patterns, and plan advanced tools. It is also exploring faster intervention and adapting crypto supervision to broader financial markets.

Why South Korea is improving its cryptocurrency monitoring tools

Cryptocurrency markets generate massive amounts of data across exchanges, tokens, and timelines. Manipulative tactics such as pump-and-dump schemes, trade smuggling, or counterfeiting often cause sudden outbreaks that are complex to detect. Manually identifying suspicious periods of crypto activity is becoming more and more challenging in the current scale of the market. As interconnected trading patterns become more intricate, automated systems are designed to constantly scan and flag potential problems.

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

What VISTA does and how the latest update improves it

VISTA serves as the FSS’s primary platform for investigating unfair trading in digital assets. In the earlier version, analysts had to specify a time frame for suspected tampering before running analyses, which constrained the scope of detection.

The latest update added an automated detection algorithm that can independently indicate potential tampering periods without the need for manual data entry. The system now searches the entire data set, allowing investigators to view suspicious intervals that might otherwise go unnoticed.

According to the regulator, the system successfully identified all known periods of internal test manipulation based on completed investigative cases. Additional intervals that were complex to detect using conventional methods were also marked.

Did you know? Some cryptocurrency exchanges process more individual transactions in an hour than conventional exchanges do in an entire trading day, making continuous automated oversight indispensable for regulators looking to monitor risk in real time.

How auto-detection works

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

The model scans every possible sub-period, identifying patterns related to tampering, without requiring investigators to determine where misconduct may have occurred. Examples of such patterns include acute spikes in price followed by acute reversals or unusual increases in volume.

Rather than replacing 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 cryptocurrency markets, price manipulation can sometimes occur in windows lasting less than five minutes, a time frame too tiny for most human monitoring systems to reliably capture it.

Upcoming AI improvements by 2026

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

• Tools designed to identify networks of coordinated trading accounts: The purpose of these systems is to detect synchronized groups of accounts, which is a common feature of organized manipulation schemes.

• Large-scale analysis of trade-related texts across thousands of crypto assets: By examining unusual promotional activities or narrative spikes along with market data, regulators hope to better understand how attention shocks and price changes interact.

• Tracing the origin of funds used for manipulation: Linking suspicious transactions to funding sources could strengthen law enforcement and reduce the ability of dishonest actors to cover their tracks.

Did you know? Early market surveillance algorithms in conventional finance were originally designed to detect insider trading in stocks, not cryptocurrencies. Many state-of-the-art tools are adaptations of models built decades ago for stock exchanges.

Moving towards proactive intervention in South Korea

South Korea’s push for AI oversight demands a faster response. The Financial Services Commission is considering introducing a payment suspension mechanism that could temporarily block transactions linked to suspected manipulation.

This approach is intended to prevent premature withdrawal or laundering of profits. While not yet finalized, it suggests a shift by regulators from reactive to preventive enforcement.

Preventive action raises significant governance questions about thresholds, supervision and the risk of false positives, issues that regulators will need to address in detail.

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

Strengths and limitations of AI surveillance

AI-based systems are excellent at detecting repeated pattern-based inappropriate behavior, such as illegal transactions or coordinated price spikes. They enhance consistency by flagging suspicious behavior, even if it occurs in compact or short-lived windows.

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

Traders and issuers should expect to see greater scrutiny of subtle patterns of manipulation that previously went unnoticed. Although detection begins algorithmically, real-world penalties remain significant.

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

AI tools support, not replace, human researchers.

Shaping a novel law enforcement framework

South Korea’s strategy includes artificial intelligence models based on continuous monitoring, automatic prioritization and faster action. As these systems evolve, it will be critical to balance efficiency with transparency, due process and accountability.

The implementation of these models will have an impact not only on Korean cryptocurrency markets, but also on other jurisdictions’ approaches to regulating digital assets in an era of algorithmic trading and mass participation.