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Identifying Pre-Trade Sanctions is Critical to Mitigating Reputational and Financial Risk

Data Science and Technology

By Oliver Bodmer  |  November 30, 2020

Effective sanctions monitoring remains an essential component in avoiding severe fines and reputational damage, but in the current environment of ever-changing comprehensive sanctions, staying clear of those sanctioned entities and tainted companies remains a major challenge. Effective monitoring of individuals or organizations who have been sanctioned by government bodies is now more essential than ever before; unfortunately, so many complex global ownership structures make revealing those relationships increasingly difficult.


The complexity around security-level sanctions began in 2014 with the Russian sanctions as a result of the Crimea crisis. The sanctions targeted specific industries and companies and individuals rather than broad-based country-level sanctions. Specifically, on the investment side, the sectoral sanctions designations prohibited dealings with new debt or equity of listed Russian financial institutions and energy companies.

When a compliance program is placed, banks need to decide which regulator they want to comply with. In the case of a European bank that has no U.S. operations but serves U.S. citizens out of Switzerland, it might not be sufficient only to follow Swiss EU sanction regulation; even though they have no foot on U.S. soil, OFAC sanctions also need to be followed.

The intricate relationship points of these sanctions become exponentially more complicated due to the effects of the current health crisis, further complexities created by domestic protectionism, and increased sanctions activities involving the United States, the EU, China, Germany, Iran, and North Korea, to name a few.

Sanctions Matrix

A sanction matrix is a tool that helps to implement a compliance program where sanctions are visualized in a normalized way. On the vertical row, you have a listing of the country, the regulator, or the issuer of the sanctions; on the horizontal header, you list the sanctioned targets. This matrix format enables you to cross-reference and determine which jurisdictions allow investment in certain financial instruments, as well as investments that are banned by other jurisdictions. Sadly, this approach cannot be used as a rule of thumb.

The Data Challenge

Understanding the challenge of complex data management in sanctions compliance planning is crucial. Sourcing, analyzing, and integrating sanctions list seems like an easy task, and to some extent, it is. Still, since sanctions regulation goes beyond the classical KYC/AML approach, every bank must assess if they want to go into a relationship with a client, execute outgoing transactions, or accept incoming transactions.

Many data points need to be considered to determine the sanction status of a financial instrument. First, you need access to a significant amount of reference data. Data points include issuer information, issue dates, maturity dates, and cross-reference information like ISIN, SEDOL, and CUSIPs to match the information in your existing trading systems. For this data to be effective, it needs to be obtained quickly. 

New sanctions issues need to be identified and blocked before your firm invests in them. To take it a step further, firms that invest in structured products need to monitor these securities as well. While bank-issued structured products are not generally sanctioned, they could contain a sanction security in the underlying basket. If the structured product specifies a physical delivery at maturity, a firm would not want to be in a situation where suddenly, they would receive a sanctioned instrument paid out by that structured product. 

Historical corporate actions data is crucial to fuel a sanctions-monitoring program as sanctions standards can change from one day to another. Sanctions teams must be aware of corporate actions, including ordinary and extraordinary general meetings, capital change, securities assimilations, and issued capital information. For example, capital increases need to be considered since they bring money to sanctioned entities, which is prohibited under sectoral sanctions against Russia issued by the United States. The European Union, Switzerland, Canada, and Australia take the same position. You need to analyze the company structures and determine if sanctioned persons are beneficial owners of them. Where the U.S. is applying an ownership rule, the European Union is considering the more complex control relationships.

A Tree of Complexity

When applying OFAC 50 Percent Rule on an ownership tree (Figure 1), we need to look at the direct and indirect ownership relations—starting on top with a sanctioned entity, illustrated in red, that owns three sister companies. Two of the individuals or entities are owned by more than 50% and therefore by application of law fall under the OFAC 50 Percent Rule and are also sanctioned. The third system, which is below the 50% threshold, remains unsanctioned. The complexity of these relationships increases on the third level, where we see another company that is owned by the two sisters with minority ownerships of 35% each. Despite the fact these sister companies are sanctioned, bringing together twice the 35% up to accumulated ownership of 70%, which is above the 50% threshold, therefore this third level is sanctioned in the view of OFAC.

Figure 1: Tree of Complexity 

identifying pretrade sanctions

This diagram is an elementary example, but you need to keep in mind that the complexity of direct and indirect ownership increases with the number of participating entities or individuals. As the complexity increases, there is also an increase in the amount of data firms need to comply with in short term; most of them tend to go with the manual approach, but as we have seen, the huge amount of disorder and the sheer volume of this data can’t be handled manually and require advanced screening systems to be put in place to reduce human error.

Due to the complexity of these calculations, it is necessary to have enterprise IT systems that will sufficiently handle the load. This is particularly true when you consider there are approximately more than 120 million companies in the market and over 150 million shareholders that sit in these reports, which could potentially be sanctioned or have a controlling risk in some of the entities. With over seven million active financial instruments, you need an ID system and add equated matching algorithm as the first line of defense. The complexity of these calculations becomes even more critical when there are an estimated 25,000 movements a week. These movements can be that an individual is added onto a sanction list, there is an ownership change, a new instrument is being issued, and it can also be the case that somebody is removed from the sanction list, and all that you have is a name change.

The second layer of defense requires machine learning to eliminate false positives. The data that is delivered by the regulators doesn't come with a unique company identifier or individual identifier that is accepted across industry standards, so matching these names to quality historical data into back-end data systems using the Levenshtein algorithm and advanced logic is often required.

The third line of defense is to have flexible data aggregation and scrubbing to avoid compliance mistakes. As many as 80 cases need to be reviewed to identify sanction relationships and avoid operational and reputational risk.

Stay Ahead of Sanctions Enforcement

It should be the goal of sanctions monitoring teams to ensure the sanction screening process is being executed pre-trade and not post-trade, to avoiding significant fines caused by missing data. This also allows you to trade with confidence and prevented your trading activities to be tainted with sanctioned individuals or companies.

As the challenges around sanctions monitoring continue to increase, it is clear that manual processes are not fit for use. It is no longer enough to look at an organization from a surface level to ensure sanctions control; maintaining a timely and clear view of data to be linked and analyzed for any effective compliance is more vital than ever.

For information on monitoring sanctioned securities, visit the SIX partner page on Open:FactSet Marketplace.

This blog post has been written by a third-party contributor and does not necessarily reflect the opinion of FactSet. The information in this report is not investment advice.

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Oliver Bodmer

Senior Product Manager, SIX

Mr. Oliver Bodmer is Senior Product Manager at Financial Information, SIX based in Switzerland. In this role, he is proposition manager for KYC, AML, and Sanctions and is responsible for the corresponding SIX product offering. Mr. Bodmer has led multiple customer implementation projects in the above areas in various international locations. Prior, he has worked in the HQ of a banking group as well as for large international technology corporations leveraging his experience from his assignment at the Swiss antitrust department. Mr. Bodmer earned a master's degree in economics and an Executive MBA from EPFL Switzerland and the University of Texas at Austin.