Address Attribution

NTerminal collects information that allows users to link individual blockchain addresses to wallets and/or business entities. These entities include major crypto-market venues, wallet interface addresses, government agencies, crypto-asset ATMs, coin tumblers, crypto-mining operations, gambling websites, darknet traders, criminal (including ransomware and malware) operations, and other blockchain industry businesses.

There are multiple ways that NTerminal can be used for address attribution and tagging:

  • Through integrating 3rd party sources where attributions are posted. - Bitinfocharts and whale-alert are examples of this sort which are integrated in NTerminal.
  • Through defining characteristic activity patterns of an entity. Users can create their own algorithms (or have them built by INCA developers) for identifying addresses which have certain behaviors. Most of these methodologies are confidential to the clients who develop them, but many are focused around identifying OTC providers, or classifying important cryptocurrency traders.
  • Users can upload their own datasets which contain address attributions, and keep them confidentially within their own platform.
  • By recognizing blockchain addresses within natural language events. Our NLP module automatically recognizes blockchain addresses and generates events via analysis of the dark web, social media, and chat channel traffic. This enables our customers to tap into an ocean of unstructured data instead of waiting for another 3rd party service to update their databases. - In addition to identifying addresses on traditional or social media sources, we can integrate crypto specific sites where addresses are reported. Current examples of this include bitcoinabuse and cryptoscamdb

NTerminal’s aggregated tags can be referenced with custom commands through web portal access to give attributions. Please visit the Data Access pages for more examples.

NTerminal also goes beyond identifying suspicious activity and investigating particular addresses. Its ability to overlay known addresses with other datasets enables various KYC/AML and market surveillance use cases. As an example, below is a dashboard that tracks large movements of Bitcoin.