They may have seen them coming, but we can still ensure they make a difference.
Russian sanctions will have effects and obviously given the chain of events, are necessary. However, the Russian financial system has not been idly wondering whether it should act! Companies have been established, relationships nested, and malign influence infiltrating over many years. The key to influencing Russian behavior and managing such influence is managing the money that has already been placed, uncovering the layers, finding the integration points, and blocking and seizing the assets rapidly.
We should consider any new sanctions in the context of historical sanctions of Russia in relation to Ukraine in 2014:
- March 6th-U.S. Presidential Executive Order 13660 declaring a national emergency and ordering sanctions, including travel bans and the freezing of U.S. assets, against individuals who had asserted governmental authority in the Crimea region.
- April 10-the Council of Europe suspended the voting rights of the Russia delegation.
- April 28-both the EU and US took further steps targeting specific oligarchs. This targeted seven government officials and seventeen companies linked to Vladimir Putin.
- July 31-the EU introduced a third round of sanctions (Council Decision 2014/512/CFSP, and Council Regulation (EU) No 833/2014) which included an embargo on arms and related material, and embargo on goods and technology intended for military use or had a dual use that included military usage. The orders also targeted extraction industries and a restriction on the issuance of and trade in certain financial instruments. Canada, Japan, Australia and Switzerland also followed with specific actions.
- December 19-the US issued Executive Order 13685 targeting an additional fourteen defense companies, six of Russia’s largest banks, and four energy companies. Individuals in Putin’s inner circle were also targeted.
Did these sanctions make a difference? They may have delayed the actions, but Putin has continued down the path to the invasion of Ukraine on February 21, 2022. The key now must be to focus on:
1) Effectively continuing the implementation of these sanctions and the new sanctions that have been imposed; and
2) Working to leverage open source intelligence to investigate the nested relationships that exist across the global business network behind these sanctioned entities. This will especially be key if Russian access to the SWIFT network is removed.
Open Source Intel – Data Extraction Industry
A good example of leveraging open source intelligence is targeting private military organizations such as ChVK Vagner, aka Wagner Group. These fronts for Russian mercenaries that have been active in the Ukraine, Central Africa and Syria. Reports suggest that no single entity controls or owns these companies; rather, there is a complex network of linked individuals and entities. The Five Eyes Alliance (made up of law enforcement and intelligence agencies from the US, UK, Canada, Australia and New Zealand) and EU must operate to identify the linkages between these individuals and entities through public private partnership investigations with financial institutions. The complete Financial Institution AML program must be integrated to leverage transaction monitoring, sanctions screening, law enforcement referrals and open source intelligence, to piece the network of funds and entities together. This task is unattainable by law enforcement agencies in a rapid manner without the partnership of financial institutions.
In the private sector, an effective AML program must ensure it has developed and can leverage a risk typology that defines Russian foreign malign influence combined with quality external data sources to identify the linkages between transaction monitoring alerts and referrals. The scale and speed of these transactions requires a virtual investigator that can “read” the volume of data with ethical AI (artificial intelligence). The AI approach must determine the confidence of a match to the correct entity and the risk relevance of a specific article. If private sector institutions do not embrace AI to deal with the pace of the laundering, they deservedly face massive reputational risk.
Quantifind’s Introducing “Risk Cards” to Make Risk Labels Standardized and Computable blog outlines how we prepare virtual investigators with collaboration between data scientists and subject matter experts that define a risk card on a specific risk typology to improve risk model performance and explainability.
Quantifind has a series of Risk Cards each including the following key elements. Here we highlight the structure of a risk card with example components from our Russian State Influence Card:
- Definition: A simple operational definition. Russian State Influence is defined as entities influenced by the Russian government either through direct membership or through a controlling relationship. This is leveraged by our public/private partnership with subject matter experts for selecting, defining and labeling training data.
- Entities and Networks: To really define the objective of a supervised model you need training data. In this case, that means “naming names” of people and organizations that do (and don’t) deserve that label. For example understanding that the new sanctions cover 25 of State Corporation Bank for Development and Foreign Economic Affairs Vnesheconombank (VEB) subsidiaries designated on 2/22/2022 pursuant to E.O. 14024 for being owned or controlled by, or for having acted or purported to act for or on behalf of, directly or indirectly, VEB or the Government of Russia. These subsidiaries represent a wide range of businesses, including banks and other financial firms, electronic component producers, a coal mining company, a sporting activities company, among others, in Russia and three other countries. All entities owned 50 percent or more, directly or indirectly, by VEB are subject to blocking under E.O. 14024, even if not identified on OFAC’s Specially Designated Nationals and Blocked Persons List (SDN List).
- Topics and Terms: The Risk Card summarizes features that need to be considered by the topic model meant to capture the risk in question. For example, our Risk Card on Russian State Influence covers “Chastnaya Voennaya Kompaniya” (ChVK) which translates to Private Military Company (PMC) to ensure completeness of the ultimate model.
- Sources and Signals: Each risk typology has its own niche set of data sources that need to be evaluated by any data scientist trying to achieve reasonable coverage in their model. For example Rupep PEP: PUBLIC DATABASE OF DOMESTIC POLITICALLY EXPOSED PERSONS OF RUSSIA AND BELARUS
In addition, these new technologies can supercharge existing sanctions screening with KYC and adverse media systems. As we explained in our Sanctions Screening: Intelligent Entity Matching Reduces False Positives by Over 75% Blog Post: Linking with adverse media can indicate the true match confidence and generate a detailed report on the successfully blocked customer or counterparty. This enables a broader review by an FIU of the network relationship linked to the sanctioned entity, and establishes whether broader action is required in the form of SAR filings and customer exit decisions. Adverse media screening can also even be used to provide alerts to entities at high risk of being put on a sanctions list in advance of their formal listing.
Sanctions and watchlist screening have never been more challenging, but new techniques that leverage open source data and machine learning are fortunately now available that can help significantly improve their efficiency and reliability, with a reduction in false positives that can make a 4X improvement in operational productivity without increasing non-compliance risks.
Avoid a Reckless System of Controls
The costs of addressing this with traditional risk management tools, which lead to an overdependence on human investigators, is no longer possible given the scale of data an individual investigation must consume. The prudent financial institution must supplement its high value money laundering investigators and transaction monitoring with optimized AI that can tackle this unstructured data problem. If a financial institution does not leverage speed in processing risk in the same way it values speed in processing a transaction it will face international condemnation and financial penalties for a reckless system of controls that effectively undermine the west’s sanctions strategy.