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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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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