Verifex
Verifex Research

Report VR-2026-02PEP data··12 min read

Four in five: what a Wikidata-derived PEP list really contains 

Verifex Research · Self-administered study · Not independent validation

Abstract

Many open PEP datasets draw on Wikidata, the structured database behind Wikipedia. We rebuilt the Wikidata-derived part of a PEP dataset of about 930,000 rows from the full Wikidata dump, under an explicit, testable definition of who counts as a politically exposed person. Across read-only shadow runs in September and October 2026, the new definition removed about 208,000 rows. 165,364 of them, about 80%, were people who had died. The rest had no office, only offices outside the defined categories, no screenable name, or an impossible birth year. Only 216 living office holders were lost. We describe the method, show why a simple per-country safety cap cannot tell a clean-up from a bug, and list what compliance teams should ask of any PEP data provider.

Key findings

  1. 1About 80% of the rows removed (165,364 of about 208,000) were people who had died. Dead people in a PEP list create alerts that can never be true.
  2. 2About 28,000 rows carried only the occupation label “politician”, with no office held. Without an office there is no political exposure to assess.
  3. 3In some countries most rows were legitimately removed: 89% in the Vatican and 77% in Japan, mostly historical office holders. A blanket 60% per-country safety cap would have blocked a correct clean-up.
  4. 4A wider rule for assigning a country to generic offices added about 19,000 people to win back about 2,500. Goodwill ambassadors and a chess grandmaster appeared among the PEPs. Precision mattered more.
  5. 5Under the final definition, 216 living office holders were lost across all countries, and every country kept at least 95% of its living PEPs except three small ones, which were over by 4 to 8 people each.

Why PEP data quality matters

A politically exposed person is someone entrusted with a prominent public function, such as a minister, a judge or an ambassador. International standards ask regulated firms to identify PEPs and apply enhanced due diligence. The work is only as good as the list: every wrong row costs an analyst's time, and every missing row is a gap an examiner may find.

Much open PEP data traces back, at least partly, to Wikidata. It is large, open and current. It is also crowd-edited, and it records history: every minister who ever held office, alive or not. How a provider turns that raw material into a screening list decides how many of its alerts are real.

A definition you can test

We started with a definition precise enough to count against. A PEP, for this dataset, is a person who holds an office in one of ten categories and has a name that can be screened.

10
office categories: head of state, head of government, minister, MP, ambassador, governor, mayor, judge, central bank governor, legislator
≤5%
pass mark: no country may lose more than 5% of its living PEPs
156
Azerbaijani minister records (128 people) used as a known-answer check

Method: shadow runs

The previous dataset was built by querying Wikidata live, country by country. Queries that time out silently drop people. The new method reads the complete Wikidata dump once a month and applies the definition to every person in it.

Before changing anything, we ran the new method in shadow mode: it computed what it would add and remove and wrote a report, while screening kept using the old data. We repeated shadow runs, inspected the would-be removals, fixed the rules, and ran again. The live switch is bound to the exact list of removals that was reviewed, rows are switched off and never deleted, and the change history is kept.

What the new definition removed

Figure 1

Rows removed by the new PEP definition, by reason

Show the data
CategoryValue
Deceased165,000
Occupation only, no office28,000
Office outside the 10 categories8,000
No usable name4,000
Implausible birth year, no death2,700
Shadow run, October 2026. The deceased total is exact (165,364); the other counts are rounded in our records. Total about 208,000 rows.

The largest group by far is people who have died. A deceased former minister cannot open an account, and their record turns every customer with a similar name into a false alarm. The second group, about 28,000 rows, carried the occupation “politician” without any office held: candidates, activists and party members whose exposure, if any, is not what PEP rules are about.

Figure 2

Share of removed rows that were people who had died

  • Deceased165,364 (79.5%)
  • All other reasons42,636 (20.5%)
Show the data
PartCountShare
Deceased165,36479.5%
All other reasons42,63620.5%
165,364 of about 208,000 removals. Other reasons combined account for the remainder.

Why a blanket safety cap fails

A sensible safeguard for any data update is a cap: if an update would remove more than a set share of a country's rows, stop and ask. Our monthly update uses a 30% cap. For the one-time switch we first proposed 60%. The shadow runs showed why no single number works.

Figure 3

Share of a country's existing PEP rows removed by the clean-up

Show the data
CategoryValue (%)
Vatican89
Japan77
Two countries from the shadow runs, against the 60% one-time cap that was first proposed and the 30% monthly cap. Both removals were correct: mostly historical office holders who have died.

Countries with long, well-documented political histories carry many deceased office holders. A correct clean-up in those countries looks, to a blanket cap, exactly like a software bug. The fix was to replace the cap with review: the switch may only remove rows that were individually approved.

Recall against precision

One shadow run found that about a quarter of the living people it would remove held a generic office, such as “mayor” or “minister”, with no country attached. We tried assigning such offices the holder's citizenship. The wide version of that rule backfired.

Figure 4

A wider country rule: people added against real PEPs recovered

Show the data
CategoryValue
People added19,000
Real PEPs recovered2,500
Shadow run 6. Assigning any country-less office that reaches a PEP category added about 19,000 people to recover about 2,500. The rule was narrowed to the category roots themselves.

Among those added were UNICEF and UNESCO goodwill ambassadors, whose title happens to contain “ambassador”, and a chess grandmaster. More rows is not better coverage. Each wrong PEP is an alert an analyst must clear and explain.

Who we kept

The pass mark was about living people. Under the final definition, 216 living office holders were lost in total, and every country kept at least 95% of its living PEPs except three small ones, which were over the line by 4 to 8 people each. The pass mark also required Azerbaijan to keep its living ministers, our known-answer check.

216
living office holders lost across all countries
3
small countries over the 5% line, by 4 to 8 people each
0
rows deleted: removals are switched off, with history kept

What compliance teams should ask

  1. What is your definition of a PEP, and can you show it in writing?
  2. How do you handle people who have died, and how quickly do you remove them?
  3. Do you list people by occupation, or only by office held?
  4. When your data changes, how do you know the change is correct and not a bug?
  5. Is PEP data ever scored like sanctions data? (It should not be.)

Read how Verifex keeps PEPs apart from sanctions on the PEP screening page, and our companion report on measuring screening accuracy.

This report describes measurements and general regulatory context. It is not legal advice; confirm your obligations with a qualified adviser.

Limitations

  • Wikidata is crowd-edited. A missing date of death means the death is not recorded, not that the person is alive.
  • Counts other than the deceased total are rounded in our records and are shown as “about”.
  • The study covers the Wikidata-derived rows only. About 398,000 rows from national PEP registers (for example Brazil) were not in scope.
  • Local council members (about 8,000 people) are outside the 10 categories by decision; some regulators may expect them.
  • Relatives and close associates of PEPs are a separate problem and are not measured here.
  • Shadow runs measure what an update would do. Results after the live switch will be reported separately.

Data and code

Figures come from internal, read-only shadow runs of the Verifex PEP update (September to October 2026) recorded in the Verifex engineering decision log. No customer data was used. Per-country tables are available to customers on request.

How to cite

Verifex Research (2026). Four in five: what a Wikidata-derived PEP list really contains. Verifex Research Report VR-2026-02. https://verifex.dev/research/pep-data-quality-wikidata

References

  1. FATF (2013). FATF Guidance: Politically Exposed Persons (Recommendations 12 and 22). Paris: FATF.
  2. Directive (EU) 2015/849 on the prevention of the use of the financial system for money laundering, Article 3(9): definition of politically exposed person.
  3. Vrandečić, D., & Krötzsch, M. (2014). Wikidata: a free collaborative knowledgebase. Communications of the ACM, 57(10), 78–85.
  4. Wikidata. Database download (JSON and truthy dumps).
  5. Verifex. PEP screening.

More research