We built an alert triage system. Then we watched analysts ignore it.
A company developed an alert triage system to address the high volume of false positives in anti-money laundering (AML) alerts. Initially, attempts to improve detection rules only marginally reduced alert volume without addressing the underlying context issues. The solution involved integrating systems to provide comprehensive context, significantly reducing noise and improving the efficiency of compliance teams.
- ▪95% of AML alerts are considered noise, leading analysts to waste time on false positives.
- ▪Initial attempts to reduce alert volume by tightening detection rules were only marginally successful.
- ▪The breakthrough came from connecting systems to provide context, allowing for more accurate alerts.
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| Original publisher | DEV.to (Top) |
| Canonical URL | https://dev.to/stuart_watkins_555e9d30ee/we-built-an-alert-triage-system-then-we-watched-analysts-ignore-it-50l3 |
| Publication time | Wed, 27 May 2026 07:40:15 +0000 |
| Retrieval time | 2026-05-27T08:07:57.149Z |
| Last seen | 2026-05-27T08:07:57.149Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | U5OjtK5Eh_NK |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3764405) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Stuart Watkins Posted on May 27 • Originally published at dev.to We built an alert triage system. Then we watched analysts ignore it. #architecture #machinelearning #productivity #security TL;DR: 95% of AML alerts are noise. We spent years assuming better detection models would fix that. They didn't. The real problem was that our systems couldn't talk to each other. Context, not cleverness, is what separates signal from noise.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).
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