In 2024, several significant developments in the realm of politically exposed persons (PEPs) and money laundering have made headlines. These include a series of high-profile cases and regulatory changes aimed at enhancing transparency and accountability. Greg Pinn, Babel Street’s Director of Name Screening, dives into the importance of screening PEPs, and how technology such as AI, holds the key to advancing the process.
About half of the world’s population will be going to the polls in elections this year, and a number of these elections will have a considerable impact on global stability. The possible elevation of so many people to positions of power raises the risk of bribery and corruption, dramatically elevating the need for Politically Exposed Persons (PEP) screening and mitigation.
PEPs are individuals who are or have been entrusted with prominent public functions. As such, they have a higher likelihood of being involved with bribery, corruption, and/or money laundering due to their potential influence on laws, contract awards and political outcomes.
Changes in PEP regulations
PEP regulations around the world are changing, with stricter requirements for due diligence and the costs of non-compliance violations increasing every year. Financial institutions (FIs) must conduct PEP screening to assess the level of risk associated with each individual and determine the extent of due diligence required to understand and mitigate risk.
In a risk-based approach to PEP screening, FIs and other regulated entities evaluate various factors to determine the level of risk posed by a particular PEP.
There are four key areas to consider:
1. Country risk: Whether using a commercial country risk ranking tool or one provided by a non-governmental organisation, understanding comparative risks for countries, including their level of election integrity and freedom of the press, is essential to assessing PEP risk.
Limitations in press freedom can often obscure corruption research, making it nearly impossible to bring such crimes to light. While country risk is an important part of PEP screening, some institutions are too reliant on this single point. No PEP should be dismissed outright solely based on location.
2. Event timelines: Elections are highly correlated with corruption, both immediately before and after they take place. Buying influence before a candidate is elected or illegally funnelling money to their campaign are key corruption events.
3. Position level and locale: The type of position is also an important measure of PEP risk. It is common practice to consider PEPs with greater influence (presidents, prime ministers, national legislators) as being at a higher risk than local leaders. However, not all local politicians are equal. The mayor of Paris, London, Mexico City or Tokyo will have a higher risk profile than the mayor of a smaller city. Exceptions must be made to increase the risk profiles of PEPs when their jurisdictions elevate their power potential.
Legislation around the world differs for how domestic versus foreign PEPs should be treated. For example, in the UK, new rules went into effect in January 2024 classifying domestic PEPs as lower risk than foreign PEPs.
They still require enhanced due diligence, but less than that applied to foreign PEPs.
4. Relationships: Bribery schemes may go beyond direct cash payments, to involve remuneration such as paid tuition for children, trips, cars or luxury goods for spouses or close associates or purchasing homes for parents.
AI and PEP risk mitigation
Newer technologies using AI — such as automated workflows, AI-based screening, and advanced name-matching technologies — can enhance efficiency and effectiveness in PEP risk management. Regulators encourage the use of newer technologies in risk identification and screening processes. At the same time, the use of Artificial Intelligence (AI) in regulation technology has increased substantially.
These solutions help address some common problems in traditional PEP screening approaches.
Often, having more data to screen against means more false positives, which can lead to compliance gaps. As a result, some PEP screening processes are simply turned off, especially when teams are too small to remediate high numbers of false positives. In other cases, teams use large ‘If-then’ spreadsheets to assess PEP risk based on organisational policy, which creates large files that require actively managed, manual updates – again, likely leaving gaps.
A best practice approach is using software to automate PEP identification and ongoing monitoring. Advanced solutions enable risk scoring of PEPs, enabling users to allow them to configure workflows based on identified risks, while also allowing for more auto-resolve options without increasing risk. By improving efficiency and freeing up human resources for other tasks, FIs can achieve strong returns on investment by including scoring methodology in the technology workflows.
Mitigating PEP risk with name-matching
Prioritising the reduction of false positives by enhancing record-matching practices is key to streamlining PEP risk assessment. By leveraging statistical modelling, name databases, and Artificial Intelligence, it is possible to reduce the repetitive, manual tasks of Level 1 remediation. This eliminates the need for FIs to spend resources and time remediating matches that could reliably be resolved through an advanced name matcher.
Implementing such name-matching technology can be done via a replacement of software solutions or by integrating a post-processing engine, which refines the matches produced by an existing system to a more manageable number.
Technology holds the promise to stem the tide of corruption
Elections are not the only situations associated with bribery and corruption risk. Forceful takeovers and coup d’états are happening routinely around the globe. FIs should move now to review their PEP screening measures to ensure teams are operating for the best efficiency and optimal impact.
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