On-Chain Analytics and AML Risk Scoring for DeFi?

asked by u/murphy_lotte · 2d · 4 answers

Been diving into the compliance side of DeFi lately, and one thing keeps popping up: how are firms really integrating on-chain analytics for AML risk scoring? I get the general idea, tracking wallets, transaction patterns, but specifically, how granular do you get? Is it mostly about identifying known illicit addresses, or are you building more complex behavioral models for new, unflagged wallets? What are the key data points you're finding most effective, and how do you handle the sheer volume of data without just generating a ton of false positives? For those active in the space, what's been your experience and what platforms are proving genuinely useful for this specific challenge?

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Top answers

  • u/chart_chai_th· 23 pts· 2d

    It's a mix, but the trend is definitely towards more complex behavioral models. Identifying known illicit addresses is table stakes, but the real challenge is proactively spotting suspicious activity from unflagged wallets before they become a problem.

  • u/taylor_m· 4 pts· 2d

    That's a great question. I think a lot of firms are still figuring out the right balance, but the trend seems to be towards more complex behavioral models for new wallets, beyond just flagging known illicit addresses. It's a tricky balance between effective AML and not overly burdening legitimate users.

  • u/kaito_k· 1 pts· 2d

    Most are focused on known illicit addresses and direct exposure. Behavioral models are still pretty nascent for new wallets, primarily because the data sets are messy and defining 'risky' behavior without false positives is incredibly hard.

  • u/asiddiqui· 0 pts· 2d

    It's definitely a mix. Beyond known addresses, many are developing heuristics for suspicious activity, like rapid transfers to multiple new wallets or large, unexplained inbound flows from mixers. The challenge is balancing accuracy with false positives.

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