
Every two weeks, we spotlight an AI task from Unit21’s task library, something many compliance and fraud teams are configuring and running inside their workflows today.
This edition covers PEP Watchlist Analysis. The problem it solves is one that most compliance programs know well but rarely address systematically: the baseline investigation required for every PEP alert is almost entirely manual. Analysts verify the match, research the individual, check family connections, and document their findings before making a single judgment call. This task does all of that automatically.
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A Politically Exposed Person is an individual who holds or has held a prominent public function: senior government official, legislator, military officer, state-owned enterprise executive, or a close family member or associate of any of the above. Financial institutions are required under BSA/AML frameworks and FATF guidance to apply enhanced due diligence to PEP relationships, and that obligation doesn’t end when the account is onboarded. It surfaces every time a PEP-linked entity triggers an alert.
When that alert opens, the investigator’s first task isn’t to review transactions; it’s to establish who they’re actually looking at. The watchlist match needs to be verified against the customer entity on file. If the match is confirmed, the individual’s current political position and status need to be established. Adverse media, corruption, bribery, and legal proceedings need to be checked. Family members and related parties need to be investigated for their own political exposure and adverse media. Every source needs to be documented.
Done carefully, that’s significant time on baseline work that precedes the actual investigation. Done under caseload pressure, it gets abbreviated. Some investigators go deep; some don’t. The result is inconsistency across a compliance program that regulators expect to be systematic.
PEP Watchlist Analysis automatically handles the baseline investigation every analyst runs when a Politically Exposed Person alert is flagged. The agent first verifies whether the watchlist match is actually the same person as the customer entity on file, filtering out false positives before any research begins. It then runs online research on confirmed matches, checking political position, current or former status, tenure dates, and adverse media, including corruption, bribery, and legal issues. Family members and related parties are investigated the same way: political role, government connections, and any adverse media are automatically surfaced.
The agent automatically reviews:
PEP alerts are high-stakes by definition. The individuals flagged hold or have held positions of public trust, and the compliance obligation isn’t just to flag them, it’s to understand who they are, what they do, and who they’re connected to. That baseline research has to happen on every alert, every time, regardless of caseload.
The problem is that it’s manual by default. An investigator opens a PEP alert and must verify the match, search for the individual, review the results, check family connections, and document their findings. Done carefully, it takes significant time. Done under pressure, it gets abbreviated.
What follows from that inconsistency is a compliance program that doesn’t behave uniformly. Some investigators find the adverse media; some don’t. Some check family connections; some run out of time. When a regulator asks whether enhanced due diligence was applied systematically, “we left it to individual investigators” isn’t a satisfying answer.
PEP Watchlist Analysis does that work automatically. The match is verified, the individual is researched, family associations are surfaced, and a recommendation is waiting before the investigator has made any decisions. The sources are linked, the findings are summarized, and the judgment call, the part that actually requires a human, is all that’s left.
Because it’s built on Unit21’s Build Your Own Task framework, it runs inside your existing workflow. Nothing new to learn. And because it surfaces a recommendation with documented sources, investigators can act on it, challenge it, or escalate it with the backing to defend any of those choices under examination.
The AI Task Spotlight runs every two weeks. Each edition covers one task from Unit21's library, covering what it does, how it works, and who it's for. If a task is solving a real problem for one team, it can probably solve the same problem for yours.
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Gal Perelman is the Product Marketing Lead at Unit21, where she spearheads go-to-market strategies for AI-driven risk and compliance solutions. With over a decade of experience in the fintech and fraud sectors, she has led high-impact launches for products like Watchlist Screening and AI Rule Recommendations.
Previously, Gal held marketing leadership roles at Design Pickle, Sightfull, and Lusha. She holds a Master’s degree from American University and a Bachelor’s from UCLA, and is dedicated to helping banks and fintechs navigate complex regulatory landscapes through innovative technology.