Giannis Andreou

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.
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.
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.
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.

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Understanding Gas Fees
Gas fees are an integral part of the Ethereum blockchain. They are transaction costs that users pay to execute operations on the network. These operations can range from simple transactions, like sending Ether (ETH) from one address to another, to more complex interactions with smart contracts.
The concept of gas was introduced as a form of remuneration for validators who maintain and secure the Ethereum blockchain. Validators, who verify and process transactions on the network, receive these fees. The fees are priced in tiny fractions of the cryptocurrency Ether, known as gwei (10^-9 ETH).
How are Gas Fees Calculated?
The calculation of gas fees involves two key components: the gas limit and the gas price. The gas limit is the maximum amount of work a user estimates a validator will do for a particular transaction. The gas price, on the other hand, is the price per unit of work done. Therefore, the transaction cost is the product of the gas limit and the gas price.
In some cases, transactions may also include tips, which are added to the gas price. A higher tip may potentially expedite the transaction. Conversely, if a user estimates a lower gas limit, their transaction will have a lower priority in the queue.
Why Do Gas Fees Exist?
Gas fees serve as an incentive for validators to participate in the network’s validation process with their Ether. Without these fees, there would be little motivation for anyone to contribute their ETH and help secure the network.

Moreover, gas fees also help prevent network spamming. By attaching a cost to every transaction or smart contract execution, the network discourages malicious actors from overloading the network with unnecessary transactions.
Gas Fees and the Ethereum Virtual Machine (EVM)
The Ethereum Virtual Machine (EVM) is a large virtual computer that runs applications on the Ethereum blockchain. Many decentralized applications, cryptocurrencies, and tokens have been created using the EVM. Because these applications use the Ethereum blockchain, users need to pay gas fees in gwei to conduct transactions on the chain.
Gas Fees and Network Congestion
The cost of gas fees is influenced by supply and demand dynamics. If the network experiences high traffic, gas prices may increase. On the other hand, they could decrease if there is not much traffic. Therefore, understanding network congestion and timing transactions accordingly can help users manage their gas costs effectively.

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