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Revisión de la lista: Definición clasificada de un cribado de listas

Posted in Cumplimiento de las sanciones on diciembre 13, 2023
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List Screening is an essential element in sanctions screening and is usually implemented by applying programmatic screening software.

What Is A List Screening?

There are many different sanctions lists, and targets can be named on more than one list. Most software solutions are designed to allow users to select which lists to screen. The following are the lists most commonly used for screening: the UN Security Council Consolidated Sanctions List; the EU Consolidated Financial Sanctions List; all the US sanctions lists, including the SDN List and Denied Person List; and the UK Her Majesty’s Treasury Consolidated List of Financial Sanctions Targets. You also want to include particular countries’ lists if they are relevant for your business operations.

Moreover, the selection of lists should consider whether an organization’s host country has its own autonomous sanctions or equivalent as well as those countries with which the organization regularly engages.

List Screening

Inequalities List

Another set of controls is the “inequalities” list, which is compiled by financial organizations, as opposed to outside vendors. The purpose of this list is to limit the number of parties that might be identified as possible matches to names on sanctions lists. Once flagged and classified as false-positive data, the software solution should suppress future hits based on similar data.

An inequalities list is a list of words or names that automated screening tools often mistake as matches and thereby create potential matches to targets named on sanctions lists. These are words or names that the organization’s compliance team has checked and confirmed do not actually match up with a target’s record on a sanctions list, such as Andrew and Andrea. An addition to an inequalities list will apply the inequality to all future screened instances and decrease the likelihood of a future match.

Therefore, inequalities lists should have sufficient controls (at least dual controls) for additions to the list and periodic review. In the above example, having Andrew and Andrea may be fine when screening against static customer data where the data quality is generally better. However, for payment screening, Andrew and Andrea may easily be the result of a typo by a third party and may therefore unintentionally exclude potential matches. For this reason, dual controls and periodic reviews are important.

Why Sanctions Is Challenging?

I also want to briefly speak about what makes sanctions screening challenging. The biggest reason is that sanctions lists change every day. Sanctions are increasingly used by governing bodies to impede the actions of high-risk individuals. As a result, the sanctions list constantly evolves, with people being added and removed on a daily basis.

Sanctions are also becoming more complex. Historically, sanctions were levied against states or organizations, whereas now, they are often imposed on individuals and even target specific sectors.

Last but not least, sanctioning bodies are increasing. The number of bodies issuing sanction lists is steadily increasing. As a result, keeping your finger on the pulse has become impossible without the use of a sophisticated compliance solution.

Final Thoughts

Screening is an essential component of anti-money laundering (AML) compliance and the fight against financial crime. Customers must be screened for sanctions, watchlists, politically exposed persons (PEPs), and negative media lists by banks. Anti-bribery and corruption efforts, as well as third-party risk management programs, require screening of suppliers, vendors, agents, and business partners.

Screening customers and transactions has become more expensive and ineffective as a result of increasing complexity, exploding data volumes, and rising regulatory requirements. Outdated technology and manual processes are also significant obstacles. Furthermore, many banks are unsure of the quality of critical data elements or how well their screening engines perform in comparison to industry standards and benchmarks.