BSA/AML Analyst
Alert Disposition
What You Do Today
Review automated transaction monitoring alerts — sometimes 50-100 per day — and determine whether each one warrants investigation or can be cleared as a false positive. 95% are false positives, but you can't afford to miss the 5%.
AI That Applies
ML models that score alerts by true-positive probability based on historical disposition data, customer risk profiles, and contextual factors. AI-assisted triage that auto-clears obvious false positives.
Technologies
How It Works
The system ingests historical disposition data as its primary data source. Machine learning models identify the patterns in historical data that most strongly predict the target outcome, then apply those patterns to score new inputs. The output is a prioritized alert queue, with the highest-confidence findings surfaced first for immediate review. The investigation judgment.
What Changes
False positive rates drop significantly. The AI pre-scores alerts so you focus on the highest-risk ones first. Obvious false positives — the retiree who deposits their Social Security check on the same day every month — clear automatically.
What Stays
The investigation judgment. The AI can score the alert, but the analyst decides whether the activity pattern is genuinely suspicious or just unusual. That distinction is why this job exists.
What To Do Next
This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.
Establish Your Baseline
Know where you are before you move
Before adopting AI tools for alert disposition, understand your current state.
Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.
Define Your Measures
What to track and how to calculate it
Time per cycle
How to calculate
Measure how long alert disposition takes end-to-end today, then after AI adoption.
Why it matters
The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.
Quality of output
How to calculate
Track error rates, rework frequency, or stakeholder satisfaction scores before and after.
Why it matters
Speed without quality is just faster mistakes. Measure both.
Start These Conversations
Who to talk to and what to ask
your Chief Compliance Officer
“What data do we already have that could improve how we handle alert disposition?”
They set the risk appetite for AI adoption in regulated processes
your legal counsel
“Who on our team has the deepest experience with alert disposition, and what tools are they already using?”
AI in compliance creates new regulatory interpretation questions
a regulatory affairs peer at another firm
“If we brought in AI tools for alert disposition, what would we measure before and after to know it actually helped?”
They can share how regulators are responding to AI-assisted compliance
Check Your Prerequisites
Confirm readiness before you invest
Check items as you confirm them.