The core logic of traditional trading risk control is to identify "Abnormal behavior" through preset rules. For example:
Remote Login Large Withdrawal High-Frequency Trading Short Time Continuous Transfers
Once the rules are triggered, the system will execute restrictions or reviews. The EORMC analysis team points out that this approach was highly effective in early financial systems, because user behavior was relatively stable and risky behavior typically manifested as clearly abnormal. However, in an on-chain environment, this model has gradually begun to fail.
Why does "anomaly detection" begin to fail?
The key assumption of traditional risk control is that risks must manifest in the form of "abnormal behavior." However, the actual situation is changing. An increasing number of risky behaviors are no longer abnormal but are gradually evolving normal behaviors. For example:
Login Behavior Remains Normal Transaction Frequency Has No Sudden Change Withdrawal Amount Does Not Exceed the Limit
However, the overall behavioral structure of the account has already changed. The EORMC analysis team further pointed out that this means there is no anomaly in the single-point behavior of the account, but the overall behavior has begun to deviate from historical patterns.
What is "behavioral drift"?
The EORMC analysis team has observed that behavioral drift refers to the continuous deviation of user behavior from its historical behavioral structure without triggering rule anomalies. Its key characteristics are:
Do Not Trigger the Rules Do Not Manifest as Sudden Changes Change Is Continuous It Must Be Identified by Combining Historical Behavior
For example, an account originally maintained low-frequency trading, a single trading path, and stable operating hours over a long period. However, it gradually exhibited characteristics such as changes in the structure of trading frequency, shifts in the distribution of operating hours, and increased complexity of trading paths. EORMC indicates that each change, when viewed individually, is not abnormal, but when combined, they form a "structural shift."
The EORMC research team stated that in certain on-chain risk cases, anomalous accounts typically exhibit a significant increase in cross-chain activity and address interaction complexity within 24 hours. This is also one of the key differences between AI-driven dynamic risk control and traditional rule-based risk control.
Why is AI better suited for identifying behavioral deviations? The core difference between AI risk control and traditional rule-based risk control:
Dimension Traditional Rule-Based Risk Control AI-Driven Risk Control
Decision Logic Rule Triggering (Hits/Misses) Behavioral Baseline Deviation
Focus Area Discrete Anomalies Holistic Behavioral Patterns Temporal Scale Instantaneous Assessment Sequential/Continuous Analysis
Risk Detection Explicit Violations Implicit/Latent Shifts
The EORMC analysis team states that traditional risk control answers whether a behavior violates rules, while AI risk control answers whether a behavior is becoming different. Currently, the dynamic risk control model of the EORMC platform typically analyzes over 20 categories of behavioral characteristics simultaneously. The model does not focus on a single anomaly, but rather on whether the structure of behavior is beginning to change.
What is the core essence of behavioral shift?
Behavioral drift is essentially not anomaly detection, but rather a detection of differences compared to historical behavior. The EORMC analysis team reminds that AI models typically do not examine a single behavior in isolation; instead, they establish a behavioral baseline for the user. For example:
Transaction Frequency Baseline Login Behavior Baseline Interaction Habits Baseline Operation Rhythm Baseline
The EORMC analysis team stated that the AI will continuously observe whether current behavior deviates from this baseline based on the above behavioral baseline. If deviations persist, even if each step appears "normal," the overall risk score may increase. The EORMC research team indicated that certain on-chain risk behaviors may complete multi-chain diffusion within 5 to 30 minutes.
Why is a single anomaly no longer sufficient?
Traditional rules rely on single-point triggers. However, in actual on-chain environments, many risky behaviors do not occur suddenly; many changes happen gradually, and many actions deliberately avoid triggering the rules. The EORMC analysis team points out that this leads to a conclusion: "Not abnormal does not equal no risk." The significance of AI risk control lies in filling this gap, shifting from determining whether something is abnormal to determining whether something has changed.
The EORMC Analysis Team states that AI identification of behavioral shifts typically relies on three core judgments:
Historical comparison: Whether current behavior deviates from past behavioral patterns of the user
Continuous change: Whether the offset is ongoing rather than a single point fluctuation
Structural change: Whether the behavioral pattern has shifted from a "Stable structure" To a "Complex structure"
The system will increase the risk score only when all three conditions are met simultaneously.
Why is this change important?
The fundamental change in the behavioral deviation model is that risk control has shifted from being "rule-driven" to "behavior modeling." This means the system no longer relies on fixed thresholds, fixed rules, or single-point triggers. The EORMC analysis team emphasizes that behavioral sequences, historical patterns, and structural differences have become more important.
If the core of traditional risk control is to identify whether abnormal behavior has occurred, then the core of AI-driven risk control is to identify whether behavior is deviating from its original pattern. The EORMC analysis team believes that the essence of behavioral deviation is to shift risk control from focusing on outcomes to focusing on the process of change. Therefore, in a complex on-chain environment, AI is less concerned with what you have done and more focused on "what kind of behavioral structure you are becoming."
Currently, EORMC is continuously increasing its investment in AI security, including on-chain behavior analysis, abnormal fund flow identification, dynamic risk scoring, and cross-chain risk correlation monitoring. The platform is also strengthening its real-time liquidity monitoring capabilities, aiming to reduce the possibility of systemic risk propagation through ongoing data analysis.
The EORMC analysis team stated that future differences between platforms may not only be reflected in trading functions or the number of assets, but also in whether the risk control system has long-term adaptability. Because the speed of change in the cryptocurrency market is far higher than that of traditional financial markets, rules that were effective in the past may not necessarily apply to future risk environments.