BMT Series (31): AI Explanation in On-Chain Anomaly Detection Applications
In an era where on-chain data is flooding in, anomaly detection has become a major challenge in the crypto world. Bubblemaps introduces AI explanation mechanisms to make it more intuitive and efficient. Simply put, AI is not just a cold algorithm; it acts like a smart assistant that can automatically analyze on-chain clusters and identify hidden anomaly patterns. For instance, when we look at a token distribution map, AI scans the connections between bubbles to identify unusual transfer paths—perhaps multiple wallets are obtaining funds from the same source while posing as independent holders.
Take the case of SHIB, for example; the default bubble map may only display direct links among top holders, but AI explanation can delve deeper into intermediary addresses. Data shows that the magic node function is an extension of AI, automatically expanding non-holder addresses to reveal anomalies like the source of gas fees or deposit paths. If several clusters suddenly form tight connections after a token's issuance date, AI will flag this as potentially involving insider trading or money laundering activities. In version 2, the AI model is specifically unlocked for $BMT holders, generating detailed reports: not only listing anomalies but also explaining why—based on historical data comparisons, calculating transfer frequency and amount deviations.
This application is immensely powerful in actual investigations. Imagine traders using Bubblemaps to monitor market signals; if AI detects cross-chain anomalies, such as irregular flows under LayerZero bridges, they can avert risks in advance. The community-driven Intel Desk also benefits greatly, with user-voted cases often involving AI-assisted anomaly detection, accelerating the process of extracting intelligence from noise. Bubblemaps' AI is not science fiction but a practical tool of InfoFi, shifting on-chain transparency from passive observation to active alerts.
Of course, AI explanation still has room for optimization, such as imposing supernode restrictions on high transaction volume addresses to ensure computations do not lag. But overall, it transforms raw blockchain data into actionable insights, pushing Web3 towards a more intelligent direction. In the future, as multi-chain expansion continues, AI's role in anomaly detection will become even more critical, helping users capture fleeting opportunities or risks.
