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BSA/AML Analyst

Sanctions Screening

Enhances✓ Available Now

What You Do Today

Screen customers, transactions, and counterparties against OFAC SDN, sectoral sanctions, and other watchlists. True matches require immediate action — you can't just flag it for later.

AI That Applies

AI-enhanced sanctions screening with contextual matching that reduces false positives. ML models that distinguish between true matches and coincidental name similarities using additional data points.

Technologies

How It Works

The system ingests additional data points as its primary data source. NLP models process the text input by identifying entities, classifying intent, and extracting the structured information needed for downstream decisions. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.

What Changes

False positive rates drop from 98% to under 50% through contextual matching. The AI considers name, geography, date of birth, and transaction patterns — not just string matching.

What Stays

True match escalation. When there's a potential sanctions match, the decision to block, reject, or escalate requires compliance expertise and often legal counsel. The timeline is hours, not days.

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.

1

Establish Your Baseline

Know where you are before you move

Before adopting AI tools for sanctions screening, understand your current state.

Map your current process: Document how sanctions screening works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: True match escalation. These are the boundaries AI won't cross.
Assess your data readiness: AI tools for this area need data to work. Check whether your organization has the historical data, integrations, and data quality to support Machine Learning tools.

Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.

2

Define Your Measures

What to track and how to calculate it

Time per cycle

How to calculate

Measure how long sanctions screening 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.

When to check: Check after 30 days of consistent use, then quarterly.
The commitment: Give new tools at least 30 days before judging. The first week is always awkward.
What NOT to measure: Don't measure AI adoption rate as a KPI. Adoption follows value — if the tool helps, people use it.
3

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 sanctions screening?

They set the risk appetite for AI adoption in regulated processes

your legal counsel

Who on our team has the deepest experience with sanctions screening, 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 sanctions screening, what would we measure before and after to know it actually helped?

They can share how regulators are responding to AI-assisted compliance

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.