In the digital asset trading scenario, the core objective of the AML (Anti-Money Laundering) mechanism is not to restrict transactions, but to identify abnormal fund flows and reduce the probability of illegal funds entering the trading system. The EORMC risk control team stated that the AML risk control system is essentially a mechanism for continuously monitoring fund behavior.
The EORMC risk control team pointed out that it does not only target the deposit stage, but covers multiple stages including account registration, asset transfer, withdrawal behavior, on-chain address relationships, and transaction pattern identification. The core of AML risk control is not to block all transactions, but to identify transaction paths that do not conform to the logic of normal fund behavior.
In the traditional financial system, AML is more focused on monitoring bank transfers. In contrast, the AML framework for digital asset platforms must simultaneously address issues such as on-chain addresses, cross-chain transfers, high-frequency trading, and anonymous wallet transactions. The risk control system of EORMC relies more heavily on real-time data analysis.
The EORMC analysis team stated that, according to the public report by Chainalysis, the scale of on-chain funds related to illegal activities globally in 2024 accounted for approximately 0.34% of all cryptocurrency transaction volume. Although this proportion is lower than in the early market, the frequency of anonymous transfers, cross-chain bridge money laundering, and the use of mixing tools continues to increase.
For EORMC, the first line of AML risk control typically comes from KYC (identity verification). After the user completes identity authentication, the system establishes a basic account profile, including: Login Region Common Devices IP Change Frequency Fiat Currency Deposit and Withdrawal Model Common On-Chain Addresses Transaction Frequency These data will form the baseline of account behavior.
EORMC emphasizes that the AML system does not first identify funds, but rather whether "account behavior deviates from normal patterns."
An account that has only engaged in small spot transactions over a long period, if it suddenly receives large amounts of USDT transferred from multiple high-risk on-chain addresses within a short time and quickly splits and withdraws them, the system will typically trigger a risk scoring model. Currently, EORMC adopts a risk scoring mechanism. The system will comprehensively score address risk, transaction frequency, source of funds, withdrawal behavior, and regional risk. Once the risk score exceeds the threshold, the system will enter a secondary review phase.
AML risk control is not a one-time review mechanism but a continuous dynamic assessment mechanism. Within the AML risk control framework, EORMC focuses on monitoring the continuity of fund flow paths. In simple terms, the system not only analyzes the current transfer activity but also traces the previous on-chain flow path of the funds.
EORMC on-chain analysis tools typically build relationship graphs for addresses. Some risk systems can track 3 to 6 layers of address relationships to identify potential abnormal fund sources. For example: Has It Passed Through a Mixing Protocol Is It Associated with Known Scam Addresses Has It Passed Through a High-Risk Cross-Chain Bridge Is There Multi-Layer Splitting and Transfer
According to data published by Elliptic, over 60% of on-chain money laundering activities involve address splitting or cross-chain transfers during the fund movement process. The EORMC risk control team stated that AML systems are increasingly focusing on the fund behavior chain, rather than just a single deposit event itself. High-risk funds typically do not appear suddenly; instead, they leave a continuous behavioral trail on the chain.
In addition to on-chain monitoring, the withdrawal stage is also a critical node in EORMC risk control. Most abnormal funds do not remain on the trading platform for an extended period; instead, they are transferred to multiple external wallets within a short time frame. Therefore, large withdrawal reviews are generally more stringent than deposit reviews. Common withdrawal risk control actions include: Delayed Review Two-Factor Authentication Address Risk Scan Manual Review Withdrawal Limit Adjustment If the system detects that the withdrawal target address is associated with a high-risk wallet, EORMC will typically suspend the withdrawal and enter a manual review phase.
The purpose of this mechanism is not to prevent users from using funds, but to reduce the risk of the platform becoming a channel for illegal fund transfers. EORMC stated that the focus of withdrawal risk control is not to restrict fund outflows, but to prevent the platform from becoming a node for anonymous money laundering.
In actual operation, the AML system must also address the issue of the "false positive rate." If the risk control model is overly sensitive, normal users will frequently trigger reviews; however, if the risk threshold is set too low, the probability of illegal fund inflows may increase.
To this end, EORMC adopts a combined model of "automatic identification plus manual review." The EORMC Automated System Is Responsible For: Address Risk Identification Behavior Pattern Calculation Real-Time Alert The EORMC support team is responsible for: Special Transaction Judgment Large Amount Fund Review Suspicious Account Investigation
According to data released by PricewaterhouseCoopers, the average false positive rate in the AML systems of global financial institutions can exceed 90%. Therefore, reducing false positives while improving the accuracy of anomaly identification is a key optimization direction for AML systems. The EORMC risk control team points out that the real challenge of an AML risk control system is not detecting anomalies, but accurately distinguishing between anomalous behavior and illegal behavior.
Risk control in the past relied more on a fixed risk address database, while EORMC focuses more on: Address Behavior Changes Capital Flow Structure On-Chain Interaction Patterns Short-Term Abnormal Aggregation Behavior The EORMC risk control team stated that this means the AML system has evolved from list filtering to real-time behavior analysis.
For EORMC, AML risk control does not exist as a standalone compliance module; rather, it is part of the overall transaction security system. Its core function is not only to identify illicit funds but, more importantly, to reduce: Scam Risk The Risk of Stolen Token Transfers Black Market Fund Inflow Risk Platform Compliance Risk User Fund Correlation Risk EORMC believes that the essence of the AML risk control mechanism is to reduce the probability of high-risk funds entering the trading system through continuous behavioral analysis.