Why is PEP Screening Important?
In today’s global economy, financial institutions like banks and insurance companies are exposed to new types of risk. One such type of risk is known as Politically Exposed Persons (PEP).
PEPs are individuals that have gained a prominent public position. They are typically heads of state, high-ranking politicians, and members of the military and judiciary. Because of their access to state assets, they pose a higher risk of corruption, bribery, and money laundering. For instance, a recent case in the news was the arrest of Georgia’s former Defense Minister for alleged money laundering. Another case is that of a Malaysian ex-Prime Minister jailed over embezzlement of public funds.
Financial institutions, which frequently deal with PEPs, are required under Anti-Money Laundering (AML) and Counter-Terrorist Financing (CTF) regulations to conduct PEP screening during onboarding and periodic reviews. Failure to comply can lead to hefty fines as well as reputational loss, operational restrictions, and legal actions. For instance, some recent cases of sanctions include:
- Guaranty Trust Bank UK Ltd received a £7.6 million ($10.2 million) fine from the UK Financial Conduct Authority (FCA) for severe weaknesses in AML systems, including inadequate customer risk assessments and due diligence on high-risk clients, such as PEPs.
- Commerzbank AG received a £37.8 million ($50.9 million) fine for failing to comply with PEP sanctions screening.
It is therefore critical that financial institutions screen individuals against PEP information.
There are in fact very many sources of PEPs. Some are official and authoritative sources such as the CIA World Leaders List, World Statesman, and government websites, records, and directories such the European Parliament Directory. There are also commercial data providers that aggregate PEP data into databases with millions of records of government officials, their relatives, and close associates. These records include identity information (e.g., full name, date of birth), network information (e.g., relatives, associates), and a risk profile with adverse information such as negative news stories, investigations, sanctions, etc. A main challenge is how to collect up-to-date adverse information for such a large and ever growing number of records.
Why is it Challenging to Maintain Up-to-Date PEP Information?
The official and commercial sources listed above are an excellent resource, but to be exhaustive and up-to-date they must collect daily information, especially adverse information, from news media and other sources. The challenge is that these data sources involve large amounts of unstructured data that is much too voluminous for manual review by humans. Think millions of news articles and social media posts per day and thousands of news sources from all over the world.
Here is where an AI-based technology called Entity Extraction makes it possible to capture data about PEPs from unstructured data in a scalable, real-time manner.
How Does Entity Extraction Help?
Entity Extraction enables automated identification of semantic concepts in text such as named entities (“John Smith”). Advanced Entity Extraction goes beyond just named entities and identifies more complex semantic concepts such as relationships and events.
Event extraction is particularly valuable in order to collect risk information about PEPs because it identifies adverse events in texts such as any arrests, indictments, and crimes that a PEP has been involved in, in addition to information about other participants and the event’s date and time. For instance, given the sentence below, event extraction identifies two adverse events (jail and steal money) involving this PEP:
“Former Malaysian prime minister Najib Razak was jailed in 2022 over the embezzlement of Malaysia’s state-owned wealth fund”
Event Type: jail
Inmate: Najib Razak
Date: 2022
Event Type: steal money
Perpetrator: Najib Razak
Victim: Malaysia’s state-owned wealth fund
Relationship extraction is also critical to assess PEP risk because it makes it possible to discover previously unknown relatives and associates that should be added to a PEP’s network and monitored for adverse information. For instance, a financial institution would want to know about allegations of corruption by a PEP’s brother. In the examples below, entity extraction identifies the sibling relationship between Kamchybek Tashiev, who is a PEP, and Shairbek Tashiev as well as the arrest and corruption events by the latter. By establishing the relationship between Shairbek and Kamchybek, any adverse information regarding the former is available in a search for the latter.
“Shairbek Tashiev, the brother of powerful security official Kamchybek Tashiev”
Relationship: sibling
Person: Shairbek Tashiev
Person: Kamchybek Tashiev
“Authorities in Kyrgyzstan have arrested Shairbek Tashiev over alleged corruption”
Event type: arrest
Person arrested: Shairbek Tashiev
Event type: corruption
Perpretrator: Shairbek Tashiev
Summary
PEP data providers face the enormous challenge of collecting up-to-date adverse information about millions of PEPs. AI-based adverse event extraction finds negative information about PEPs while relationship extraction discovers a PEP’s relatives and closed associates.
