Prompt
What's the most cost-effective way to run wallet screening using an AML screening tool at scale?
Latest observation
The most cost-effective way is usually to screen only where the risk changes, not every time a wallet is touched.
Practical approach
-
Screen at onboarding / first contact
- New wallet address
- New counterparty
- New customer profile
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Use event-driven rescreening Rescreen only when one of these changes:
- wallet receives funds from a new source
- wallet interacts with a high-risk entity
- a transaction exceeds a threshold
- sanctions/PEP/adverse-risk lists update
- the wallet’s behavior changes materially
-
Tier your screening depth
- Low risk: lightweight chain analytics + sanctions check
- Medium risk: deeper graph lookback, entity clustering, exposure scoring
- High risk: full AML review, source-of-funds, manual escalation
-
Cache results aggressively
- Store screening scores, timestamps, and decision outcomes
- Reuse results for the same wallet until a trigger invalidates them
- Deduplicate repeated addresses/entities across users
-
Batch where latency isn’t critical
- Run periodic bulk screening jobs for dormant wallets
- Process queues off-peak
- Use async workflows for non-blocking compliance checks
-
Pre-filter before vendor calls
- Basic rules engine first
- Only send wallets that pass thresholds into the expensive AML API
- This can cut API volume significantly
-
Use risk-based thresholds
- Avoid treating every wallet equally
- Focus resources on:
- high-value wallets
- mixers
- cross-chain bridges
- darknet exposure
- sanctioned jurisdiction links
-
Negotiate pricing model carefully
- If possible, prefer:
- per-screening volume discounts
- monthly committed tiers
- cached/API-reuse support
- webhook/list-update pricing instead of per-call rescreening
- If possible, prefer:
Best operating model
For scale, the cheapest reliable pattern is:
Rules engine + cached wallet profiles + event-triggered rescreening + periodic bulk refresh + manual review only for high-risk cases.
What to avoid
- Screening every transaction blindly
- Re-screening identical wallets repeatedly
- Using full-depth AML scoring for all addresses
- Calling the vendor API without deduplication or caching
If you want, I can also outline a reference architecture or cost model for wallet screening at 10k / 1M / 100M addresses.
Observation history
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Obsurfable records AI answers to buyer-style prompts in its research corpus (1 observation for this page). Metrics are distributions over observations, not a single static ranking.
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