MistTrack Adds Batch Wallet Screening and Tracing Canvas

Blockchain intelligence platform MistTrack has launched two new tools aimed at reducing manual work in crypto compliance and fund investigations, adding bulk wallet screening alongside an interactive transaction-mapping environment. According to SlowMist’s official quarterly update, KYA Batch Screening and Tracing Canvas are now available through the MistTrack platform for risk management and investigative workflows.

The first feature allows teams to import multiple blockchain addresses into a single screening task rather than checking wallets individually. MistTrack then assigns risk scores and groups results into Severe, High, Moderate and Low categories. Users can filter addresses by risk level, inspect individual Suspicious Transaction Reports and export complete screening results for further analysis or compliance recordkeeping.

Tracing Canvas Maps Complex Fund Flows

The second addition, Tracing Canvas, shifts MistTrack’s investigation workflow toward a visual transaction graph. Addresses, transactions, risk labels and fund movements are displayed within the same interface, allowing investigators to examine upstream and downstream relationships without repeatedly moving between individual transaction records. The graph can be searched, expanded and filtered while displaying information including balances, tokens, transaction counts, amounts sent and received and periods of activity.

That structure is particularly relevant when investigations involve many intermediary wallets, where manually expanding transaction histories can obscure important connections. Similar investigative complexity has appeared in cases involving multi-stage infrastructure compromises affecting crypto platforms, while other attacks have demonstrated how malicious infrastructure can redirect cryptocurrency transfers without compromising the underlying blockchain. Tracing Canvas is designed to organize those visible onchain relationships rather than determine the offchain cause of an incident.

AI Smart Tracing adds automated filtering to that graph. Investigators can define a minimum transaction amount, starting date, forward and backward tracing depth and whether the view should prioritize paths connected to exchanges or other entities. MistTrack then automatically expands and filters the transaction graph according to those user-defined conditions, reducing unrelated transaction noise instead of independently reaching investigative conclusions.

Path Finder Tests Connections Between Addresses

Tracing Canvas also introduces Path Finder for cases where investigators already have two or more addresses and want to determine whether a visible transaction route connects them. Users can specify the blockchain, time period and search depth before the system searches for potential fund paths between the selected wallets. A discovered path can establish an observable onchain transfer relationship, but it does not by itself establish common ownership, control or criminal association.

SlowMist explicitly notes the inverse limitation as well: failure to identify a route does not prove that two addresses are unrelated, because Path Finder only evaluates visible onchain paths under the selected parameters. That distinction is important in a security environment where credential, supply-chain and cross-chain incidents can involve multiple infrastructure layers. Transaction tracing provides evidence about asset movement, while attribution still requires additional technical, behavioral and offchain intelligence.

The latest release extends MistTrack beyond individual wallet checks toward workflows that can process larger address sets and complex transaction networks. SlowMist had already expanded its compliance tooling earlier this year with configurable risk-decay models and fund-association path analysis. The new batch screening and visual tracing functions further consolidate risk assessment, graph analysis and investigative filtering within the same platform, while leaving final interpretation and attribution with the investigators using the data.