Opportunities of combining AI and Crypto
Chain abstraction, artificial intelligence, opportunities for the combination of AI and Crypto Golden Finance, from a technical perspective, can chain abstraction solve the fragmentation problem?

Why is a single tool fatal? Because no single tool can cover all risks. It's like having a sentry monitor enemies from all directions simultaneously; there will always be blind spots. A recent research report released by MetaComp, a licensed digital asset service provider in Singapore, revealed this harsh reality using test data. Analyzing over 7,000 real transactions, the study found that relying on just one or two KYT tools for screening could result in up to 25% of high-risk transactions being mistakenly approved. This means that a quarter of the risks are being directly ignored. This is no longer a blind spot, but a black hole. Figure 1: Comparison of False Clean Rates for Different KYT Tool Combinations. Data source: MetaComp Research - Comparative Analysis of On-Chain KYT for AML&CFT, July 2025. The chart shows that when the risk threshold is set to "medium-high risk," the false clean rate for a single tool can reach as high as 24.55%, for a two-tool combination it can reach as high as 22.60%, and for a three-tool combination it drops sharply to 0.10%. This significant risk exposure stems from inherent flaws in the KYT tool ecosystem. Each tool is built on its own proprietary dataset and intelligence-gathering strategy, resulting in inherent discrepancies and blind spots in several areas: Differences in data sources: Some tools may have close ties to US law enforcement, providing stronger coverage of risky addresses in North America; others may have deep expertise in the Asian market, providing more up-to-date intelligence on localized fraud networks. No single tool can be the ultimate global intelligence provider. Different risk focus: Some tools excel at tracking addresses associated with OFAC sanctions lists, while others are more skilled at identifying mixers or darknet markets. If the tool you choose doesn't identify the primary risk types facing your business, it's essentially useless. Update delays and intelligence lags: The lifecycle of black market addresses can be short. A risky address flagged by one tool today might not be synchronized with another tool for days or even weeks. This intelligence lag is sufficient for money launderers to complete multiple rounds of operations. Therefore, when an institution pins all its hopes on a single KYT tool, it is effectively gambling—a bet that all the risks it encounters fall within the tool's "scope of knowledge." Malnutrition caused by data silos: How can water flow without a source? If a single tool is narrow-minded, then data silos are thoroughly malnourished. A KYT system is never an isolated system; its effectiveness is built on a comprehensive understanding of counterparties and trading behavior. It requires continuous data sourcing from multiple sources, including KYC (Know Your Customer) systems, customer risk rating systems, and business systems. When these data channels are blocked or the data itself is of poor quality, KYT becomes a dead end, lacking a basis for judgment. This scenario is common in many fast-growing payment companies: the KYC team, responsible for customer onboarding, stores their data in System A; the risk control team, responsible for transaction monitoring, stores their data in System B; and the compliance team, responsible for AML reporting, uses System C. These three systems are managed by different departments and provided by different vendors, with little real-time data exchange between them. As a result, when the KYT system analyzes a live transaction, the customer risk rating it relies on might still be static information entered by the KYC team three months ago. This customer may have exhibited various high-risk behaviors over those three months, but this information is trapped in the risk control team's System B, oblivious to the KYT system. The direct consequence of this malnutrition is that the KYT system is unable to establish an accurate behavioral baseline. One of the core capabilities of an effective KYT system is to identify "anomalies"—transactions that deviate from a customer's normal behavior patterns. But if the system doesn't even know what "normal" is for a particular customer, how can it identify "anomalies"? Ultimately, it will degenerate into relying on the most primitive and crude static rules, producing a large number of worthless "junk alerts," moving it one step closer to becoming a "zombie." Static rules are like "carving a boat to find a sword": using an old map to find a new world. Criminals' methods are constantly evolving, from traditional "smurfing" to cross-chain money laundering using DeFi protocols to fabricated transactions through NFT markets. Their complexity and concealment are increasing exponentially. However, the rule bases of many "zombie KYT systems" remain stuck in the past few years, like searching for a new world with an old nautical chart—a recipe for failure. Static rules, such as "alarm if a single transaction exceeds $10,000," are trivial to today's black market operators. They can easily use automated scripts to split large sums of money into hundreds or even thousands of small transactions, effectively circumventing these simple thresholds. The real threat lies in complex behavioral patterns: A newly registered account engages in high-frequency, small-value transactions with numerous unrelated counterparties within a short period of time. Funds quickly flow in and are then immediately transferred out through multiple addresses without stopping, creating a classic "peel chain" scenario. Transaction paths involve high-risk coin mixing services, unregistered exchanges, or addresses in sanctioned regions. These complex patterns cannot be effectively described or captured by static rules. They require machine learning models that can understand transaction networks, analyze capital flows, and learn risk characteristics from massive amounts of data. A healthy KYT system's rules and models should be dynamic and self-evolving. "Zombie systems" lack this capability. Once established, their rule bases are rarely updated, ultimately leaving them far behind in the arms race with the black market and becoming completely "brain dead." "Heartbeat Arrest" at the Process Level: From "One-Stop Solution" to "Alert Fatigue" If technical flaws lead to system "brain death," then the collapse of process management directly leads to "heartbeat arrest." No matter how technologically advanced a system is, without the right processes to drive and respond to it, it's just a pile of expensive code. In the world of compliance, process failures are often more insidious and devastating than technical ones. The Illusion of "Launch is Victory": Treating the Wedding as the End of Love Many companies, especially startups, approach compliance development with a project-based mindset. They believe that the procurement and launch of a KYT system is a project with a clear start and end point. Once the system is successfully launched and passes regulatory acceptance, the project is declared a success. This is a classic illusion in compliance theater—treating the wedding as the end of love, believing that peace of mind is assured. However, in the lifecycle of a KYT system, launch is only Day One. It's not a tool that can be "set and forget," but a living organism that requires ongoing care and optimization. This includes:
Continuous parameter calibration: The market changes, customer behavior changes, and money laundering methods change. The KYT system's monitoring thresholds and risk parameters must adjust accordingly. A reasonable $10,000 alert threshold a year ago may no longer make sense after business volume has increased tenfold.
Regular rule optimization: As new risks emerge, new monitoring rules need to be continuously developed and deployed. At the same time, the effectiveness of old rules must be regularly evaluated to eliminate "junk rules" that only generate false positives.
Necessary model retraining: Systems that use machine learning models must be regularly retrained with the latest data to ensure their ability to identify new risk patterns and prevent model decay. When an organization falls prey to the illusion of "launch is victory," these crucial ongoing maintenance tasks are often neglected. Without accountability and budgetary support, a KYT system is like a sports car abandoned in a garage. No matter how good the engine, it will slowly rust and eventually become a pile of scrap metal. "Alert fatigue" crushes compliance officers: the last straw. The most immediate and catastrophic consequence of a poorly configured and poorly maintained "zombie system" is a massive surge in false positives. Industry observations show that at many financial institutions, 95% or even over 99% of the alerts generated by KYT systems are ultimately verified as false positives. This isn't just a matter of inefficiency; it can lead to a deeper crisis: "alert fatigue." Imagine a compliance officer's daily routine: Every morning, he opens his case management system and sees hundreds of pending alerts. He clicks on the first one, investigates it for half an hour, and finds it's normal client business behavior, so he closes it. He clicks on the second, and the same goes for the third. Day after day, he drowns in an endless sea of false alarms. His initial vigilance and diligence gradually give way to apathy and perfunctory responses. He begins looking for "shortcuts" to quickly close the alerts, and his trust in the system plummets. Eventually, when a truly high-risk alert appears among them, he might just glance at it, habitually mark it as a "false positive," and then close it. "Alert fatigue" is the final straw that breaks the compliance defense line. It psychologically destroys the compliance team's fighting spirit, transforming them from risk "hunters" into alert "scavengers." The entire compliance department's energy was consumed in a futile battle against a "zombie system," while the real criminals, shielded by the cacophony of alarms, swaggered through defenses. At this point, the KYT system had completely "heartstopped." It continued to generate alarms, but these "heartbeats" had lost their meaning. No one responded, no one believed them. It had become a complete zombie. A friend's company, in order to secure a license and appease investors, staged a classic "compliance drama": They loudly announced the purchase of a top-tier KYT tool, using it as a publicity stunt to promote their commitment to the highest compliance standards. However, to save money, they only used the services of a single vendor. The management's logic was, "We're using the best, so don't blame me if anything goes wrong." They selectively forgot that any single tool has blind spots. Furthermore, the compliance team was understaffed and lacked technical expertise, so they could only use the most basic static rule templates provided by the vendor. They considered their mission accomplished by monitoring large transactions and filtering out a few publicly blacklisted addresses. Crucially, once business volume increased, system alerts began to pour in. Junior analysts quickly discovered that over 95% of these were false positives. To meet their KPIs, their job shifted from investigating risks to shutting down alerts. Over time, no one took the alerts seriously. Professional money laundering rings quickly smelled rotting meat. They employed a simple yet effective method to transform this "zombie system" into their own ATM: using a "Smurf" tactic known as "breaking the whole into pieces," they split the funds from illegal online gambling into thousands of small transactions below the monitoring threshold, disguising them as e-commerce payments. Ultimately, it wasn't their own team members who sounded the alarm, but their partner banks. When the regulator's investigation letter arrived on the CEO's desk, he was still bewildered. Later, it was reported that his license had been revoked. Figure 2: Comparison of Risk Levels of Different Blockchain Networks. Data source: MetaComp Research - Comparative Analysis of On-Chain KYT for AML&CFT, July 2025. The chart shows that, in the sampled data, the proportion of Tron transactions rated as "serious," "high," or "medium-high" risk was significantly higher than that of Ethereum. The stories around us hold up a mirror, reflecting the countless fintech companies currently engaged in the "compliance theater." They may not have collapsed yet, simply because they've been lucky and haven't been targeted by professional criminal gangs. But it's ultimately a matter of time. Act Two: From "Zombie" to "Sentinel"—How to Awaken Your Compliance System? Having revealed the pathology of "zombie systems" and witnessed the tragedy of "compliance theater," we can't just stop at criticism and lamentation. As frontline practitioners, we are more concerned about: How to break the deadlock? How can we reawaken a dying "zombie" and transform it into a truly capable "frontline sentinel" capable of both fighting and defending? The answer lies not in purchasing a single, more expensive, or more "authoritative" tool, but in a complete transformation from concept to tactics. This methodology has long been a tacit secret among true practitioners within the industry. MetaComp's research is the first to systematically quantify and publicly demonstrate its effectiveness, providing us with a clear and actionable playbook.
First, we must fundamentally abandon the theatrical mindset of "buy a tool and be done with it." True compliance isn't a one-man show, but a positional battle that requires a defense-in-depth system. You can't rely on a single sentry to hold off a vast army; you need a multi-dimensional defense network comprised of sentries, patrols, radar stations, and intelligence centers.
Tactical Core: A Multi-Tool Combination
The tactical core of this defense system is a "multi-tool combination." Blind spots in a single tool are inevitable, but the blind spots of multiple tools are complementary. Through cross-validation, we can minimize the space where risks can hide. So, the question is, how many tools are needed? Two? Four? Or the more, the better? MetaComp's research provides a crucial answer: a three-tool combination is the golden rule for achieving the optimal balance between effectiveness, cost, and efficiency. We can understand this "three-piece set" in a simple way: The first tool is your "frontline sentinel": it likely has the widest coverage and can detect most common risks. The second tool is your "special patrol": it may possess unique reconnaissance capabilities in a specific area (such as DeFi risks or regional intelligence), detecting hidden threats that the "sentinels" cannot see. The third tool is your "rear-line intelligence analyst": it may possess the most powerful data correlation and analysis capabilities, connecting the scattered clues discovered by the first two to outline a complete risk profile. When these three tools work together, their power far exceeds the sum of their parts. Data shows that upgrading from a two-tool to a three-tool approach can significantly improve compliance effectiveness. MetaComp's report indicates that a well-designed three-tool screening model can reduce the "false clean rate" of high-risk transactions to below 0.10%. This means that 99.9% of known high-risk transactions will be caught. This is what we call "effective compliance." By contrast, upgrading from three to four tools, while further reducing the underreporting rate, has minimal marginal benefits, while the resulting costs and time delays are significant. Research shows that screening times with four tools can be as long as 11 seconds, while with three tools, they can be kept to around 2 seconds. In payment scenarios requiring real-time decision-making, this 9-second difference can be the difference between life and death for the user experience. Figure 3: Effectiveness and Efficiency Tradeoffs of KYT Tool Combinations. Data source: MetaComp Research - Comparative Analysis of On-Chain KYT for AML&CFT, July 2025. The chart visually demonstrates the impact of increasing the number of tools on reducing false negative rates (effectiveness) and increasing processing time (efficiency), clearly demonstrating that a three-tool combination is the most cost-effective option.
Choosing the right "three-piece set" combination only completes the equipment upgrade. The more critical issue is how to command this multi-service force to operate in a coordinated manner. You can't have three tools speak independently. You need to establish a unified command center—your own "rule engine" independent of any single tool.
Step 1: Standardize Risk Classification—Speak the Same Language
Don't let tools dictate the same risk. Different tools may use different labels like "Coin Mixer," "Protocol Privacy," and "Shield" to describe the same risk. If your compliance officer has to remember each tool's dialect, it would be a disaster. The correct approach is to establish a unified, clear set of internal risk classification standards and then map all risk labels of connected tools to your own standard system.
For example, you can establish the following standardized classification:
Table 1: Example of a risk category mapping table
In this way, no matter which new tool you connect to, you can quickly "translate" it into a unified internal language, thereby achieving horizontal comparison and unified decision-making across platforms.
Step 2: Unify risk parameters and thresholds - draw clear red lines
With a unified language, the next step is to develop unified "rules of engagement." You need to set clear, quantifiable risk thresholds based on your risk appetite and regulatory requirements. This is a critical step in transforming subjective risk appetite into objective, machine-executable instructions. This set of rules should go beyond simple monetary thresholds and should encompass a more complex, multi-dimensional combination of parameters, such as: Severity level definitions: Clarify which risk categories are considered "serious" (e.g., sanctions, terrorist financing), "high risk" (e.g., theft, dark web), and "acceptable" (e.g., exchanges, DeFi). Transaction-Level Taint %: Defines the percentage of funds in a transaction indirectly derived from high-risk sources that triggers an alert. This threshold needs to be scientifically determined through extensive data analysis, not simply determined on a whim. Wallet-level Cumulative Taint % threshold: Defines the percentage of funds a wallet must have transferred to or from high-risk addresses throughout its entire transaction history before it is labeled high-risk. This effectively identifies addresses that have long engaged in shady transactions. These thresholds serve as the "red lines" you draw for your compliance system. Once crossed, the system must respond according to pre-defined scenarios. This makes the entire compliance decision-making process transparent, consistent, and defensible. Step 3: Design a multi-layered screening workflow—a comprehensive, point-to-point approach. Finally, you need to integrate standardized classifications and unified parameters into an automated, multi-layered screening workflow. This process should be like a sophisticated funnel, filtering through layers and gradually focusing to precisely target risks while avoiding excessive disruption to a large number of low-risk transactions. An effective workflow should include at least the following steps: Figure 4: Example of an Effective Multi-Layer Screening Workflow (Adapted from MetaComp KYT Methodology) Initial Screening: All transaction hashes and counterparty addresses are first scanned in parallel using the "three-part suite" of tools. If any tool raises an alert, the transaction proceeds to the next stage. Direct Exposure Assessment: The system determines whether the alert is a "direct exposure," meaning the counterparty address itself is flagged as a "critical" or "high-risk" address. If so, this is the highest priority alert and should immediately trigger a freeze or manual review process. Transaction-Level Exposure Analysis: If there is no direct exposure, the system begins "fund tracing," analyzing the percentage of the transaction's funds (Taint%) that can be indirectly traced back to the source of risk. If this percentage exceeds the preset "transaction-level threshold," the system proceeds to the next step. Wallet-Level Exposure Analysis: For cases where transaction-level risk exceeds the threshold, the system further conducts a "comprehensive physical examination" of the counterparty's wallet, analyzing the overall risk profile of its historical transactions (Cumulative Taint%). If the wallet's "health" also falls below the preset "wallet-level threshold," the transaction is ultimately determined to be high-risk. Final Decision (Decision Outcome): Based on the final risk rating (critical, high, medium-high, medium-low, low), the system automatically or manually prompts a user to perform the corresponding action: release, intercept, return, or escalate. The ingenuity of this process lies in transforming risk identification from a simple "yes/no" judgment into a multi-dimensional assessment process, from point (single transaction) to line (fund chain), and finally to the entire wallet (wallet profile). It effectively distinguishes between "direct hit" severe risks and "indirect contamination" potential risks, thereby optimizing resource allocation—responding quickly to the highest-risk transactions, conducting in-depth analysis of medium-risk transactions, and quickly releasing the vast majority of low-risk transactions, perfectly resolving the conflict between "alert fatigue" and "user experience." Epilogue: Dismantling the Stage, Returning to the Battlefield We've spent considerable time dissecting the pathology of "zombie systems," reviewing the tragedies of "Compliance Theater," and exploring the "playbook" for awakening the system. Now, it's time to return to square one. The greatest danger of "Compliance Theater" isn't the budget and manpower it consumes, but the deadly, false sense of security it fosters. It lulls decision-makers into believing risks are under control and numbs implementers through day-to-day, ineffective labor. A silent "zombie system" is far more dangerous than a nonexistent system, because it leaves you defenseless and vulnerable. In today's era of simultaneous advancements in black market technology and financial innovation, relying on a single tool for KYT monitoring is tantamount to running naked in a battlefield rife with gunfire. Criminals have an unprecedented arsenal of weapons—automated scripts, cross-chain bridges, privacy coins, and DeFi mixing protocols. If your defenses remain at the level of a few years ago, it's only a matter of time before they're breached. True compliance is never a performance designed to please an audience or pass inspections. It's a tough battle, a protracted war requiring sophisticated equipment (a multi-layered toolkit), rigorous tactics (a unified risk methodology), and outstanding soldiers (a professional compliance team). It doesn't require a flashy stage or false applause; it requires a reverent attitude towards risk, honesty with data, and continuous refinement of processes. Therefore, I appeal to all practitioners in this industry, especially those with resources and decision-making power: Please abandon the illusion of a "silver bullet" solution. There is no magic tool that can solve all problems once and for all. Building a compliance system has no end point; it is a dynamic, lifelong process that requires continuous iteration and improvement based on data feedback. The defenses you build today may reveal new vulnerabilities tomorrow. The only way to cope is to remain vigilant, continuously learn, and evolve.
It's time to dismantle the false stage of "Compliance Theater." Let us, armed with a truly effective "Sentinel System," return to the real battlefield of risk, brimming with challenges but also opportunities. Because only there can we truly protect the value we aim to create.
Chain abstraction, artificial intelligence, opportunities for the combination of AI and Crypto Golden Finance, from a technical perspective, can chain abstraction solve the fragmentation problem?
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