What's in a name? How to Effectively Identify Politically Exposed Persons (PEPs)

Over $2 trillion (USD) is laundered each year, according to the United Nations Office of Drugs and Crime (UNDOC) ^[1].

A significant portion of these funds are laundered through the assistance of politically exposed persons (PEPs) who are prominent politicians, government officials, or close associates and family members. Heads of international organizations (HIOs), senior officials and even board members are also considered PEPs.

Political Corruption is Alive and Well

The impacts of financial crimes perpetrated by PEPs have devastating effects on the people,  resources and economies of the countries they serve:

• In 2021, Alex Saab was extradited to the United States (US) to face bribery and corruption charges as the front man for Nicolas Maduro’s Venezuelan government. Saab was accused of siphoning over $350 million (USD) from critical housing and food projects, while thousands of Venezuelan people remain hungry^[2].
• In 2020, Isabel dos Santos, daughter of former Angolan president Jose Eduardo dos Santos, was charged with laundering over $2.1 billion (USD) embezzled from state-run oil company Sonangol. Angola is one of the poorest countries in the world^[3].
• In 2019, Keith Schembri, Chief of Staff to Malta’s Prime Minister Joseph Muscat, resigned from his post due to alleged links in the assassination of journalist Caruana Galizia. Schembri was later charged with bribery and corruption while in office^[4] – the very crimes Galizia was reporting on when she was murdered.

Even if officials themselves are not corrupt, their proximity to financial systems and decision makers within the jurisdictions in which they operate, can make them high-risk clients.
It’s critical that financial institutions consistently and accurately identify PEPs at AML onboarding and manage them in accordance with prescribed AML/KYC risk procedures to avoid both enforcement action and reputational damage.

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Two Major Challenges

At MinervaAI, we know first-hand that identifying PEPs is difficult. Traditional methods like occupation screening and name matching cannot address the two key challenges faced by financial institutions:

1. Broad and varied definitions acrossBroad and varied definitions across  multiple jurisdictions; and
2. The lack of centrally managed,The lack of centrally managed, reputable lists that identify PEPs.

The label of PEP can be applied to individuals in a wide variety of political roles and extend to their family members and close associates. Every jurisdiction has a varied definition of PEP, though most definitions are based on the Financial Action Task Force (FATF) definition^[5]:

“Foreign PEPs are individuals who are or have been entrusted with prominent public functions by a foreign country, for example Heads of State or of government, senior politicians, senior government, judicial or military officials, senior executives of state owned corporations, important political party officials.

Domestic PEPs are individuals who are or have been entrusted domestically with prominent public functions, for example Heads of State or of government, senior politicians, senior government, judicial or military officials, senior executives of state-owned corporations, important political party officials.

Persons who are or have been entrusted with a prominent function by an international organisation…members of senior management, i.e., directors, deputy directors and members of the board or equivalent functions.

[…]requirements for all types of PEP should also apply to family members or close associates of such PEPs.”

The second challenge is the lack of reliable and centrally managed PEP and HIO lists. While non-government organizations (NGOs) and investigative groups have created valuable publicly-available lists, they are not always up to date, nor do they capture the full extent of the core PEP definition, let alone the broader jurisdictional variations.

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The Case for AI Powered Enhanced Due Diligence

Challenges with traditional PEP identification is the catalyst to deploy an open source, AI risk assessment platform like MinervaAI.

MinervaAI allows organizations to access data outside of the typical information provided by customers at onboarding, as part of “Know Your Customer (KYC)” procedures. MinervaAI’s deep-learning artificial intelligence capability examines multiple source lists and internet data to intelligently link your search subject to applicable PEP indicia – in seconds.

Manual search methods take hours to complete or miss critical information, putting the financial institution at risk. Regulators expect that you are doing more than the bare minimum to identify PEPs and expect that firms are deploying multiple technologies in the pursuit of a compliant AML/KYC program.

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An Open-Source Case Study: How to Effectively Identify PEPs

This was a question MinervaAI’s customer, a fast-growing digital bank, was asked by their regulator. Lack of available source lists and gaps in antiquated keyword matching technology made our customer vulnerable to PEP and HIO risk.

Leveraging MinervaAI’s proprietary natural language processing and sentiment analysis programs, we were able to augment screening of publicly available PEP source lists with open source data. This included thousands of watchlists, news outlets and scans of personal and non-personal websites. The customer was able to create a more complete picture of their PEP exposure, while minimizing false positives caused by keyword matching alone.

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Using MinervaAI’s API capability, the initial screening of over 25,000 existing clients took less than four hours to complete. This is an exponential time-savings over manual open source searches which were taking between two to six hours each to complete (that’s close to 13,000 business days of effort!).

Moreover, the client was able to identify 267 potential PEPs, equal to 1% of their customer base – a very manageable population. Before they had implemented MinervaAI, the client’s existing methods were only able to confirm a single PEP which is what triggered the regulator’s scrutiny in the first place.

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Open Source Automation with MinervaAI

Open source data is a critical component of your AML program. Access to meaningful open source intelligence can augment existing procedures to ensure you have sufficient breadth and depth of coverage when managing both your know your customer program and regulator expectations.

In an independent review of competitors, MinervaAI’s PEP accuracy score was over 40% higher than a leading provider.

Implementing AI financial crime platforms like MinervaAI can weed out the noise and assemble in seconds what manual processes take hours or days to do. AI powered enhanced due diligence provides a comprehensive view of risk while managing false positives.

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_{[1] https://www.unodc.org/unodc/en/money-laundering/overview.html}

_{[2] https://www.justice.gov/opa/pr/colombian-businessman-charged-money-laundering-extradited-united-states-cabo-verde#:~:text=Saab%20is%20charged%20in%20an,Venezuela’s%20government%2Dcontrolled%20exchange%20rate.}

_{[3] https://www.icij.org/investigations/luanda-leaks/isabel-dos-santos-charged-with-embezzlement-will-sell-portuguese-bank-stake/}

_{[4] https://www.state.gov/public-designation-of-former-maltese-public-officials-konrad-mizzi-and-keith-schembri-due-to-involvement-in-significant-corruption/}

_{[5] Standards on Combating Money Laundering and the Financing of Terrorism & Proliferation: The FATF Recommendations, Financial Action Task Force, October 2021.}