Governments and international organisations issue sanctions lists to prevent illicit activity, but what exactly are they?
Sanctions lists are lists of people and companies that are subject to broad or specific restrictions under international and domestic sanctions regimes.
People and entities are added to these lists for a variety of reasons, including terrorism, terrorist funding, proliferation of weapons of mass destruction, arms trafficking, narco-trafficking, and war crimes. Sanctions can ultimately be used to prohibit assets and impede commerce in order to achieve foreign policy and national security objectives.
Because sanctions are frequently accompanied by severe civil and criminal penalties, banks and financial institutions should regularly evaluate their compliance status and link their usage of sanctions lists with their internal systems and processes for detecting and reporting financial crime.
Which Organisations Publish Sanctions Lists?
Sanctions are maintained by governments and financial agencies all around the world. Some of these are public listings. These are some of the lists:
- United Nations Sanctions (UN)
- US Consolidated Sanctions (US Sanction Lists)
- OFAC — Specially Designated Nationals (SDN)
- Office of the Superintendent of Financial Institutions (Canada)
- Bureau of Industry and Security (US)
- Department of State, AECA Debarred List (US)
- Department of State, Nonproliferation Sanctions (US)
- EU Financial Sanctions
- UK Financial Sanctions (HMT)
- Australian Sanctions
- Consolidated Canadian Autonomous Sanctions List
- Consolidated Sanctions List Of The Kyrgyz Republic
- EEAS Consolidated List
- SDFM Terror List
- Us Cia World Leaders Pep List
- World Presidents Pep List
- CoE Assembly Pep List
- Every Politician Pep List
- Switzerland Consolidated List
- Capital Market Board Of Turkey Operation Banned List
- Interpol Wanted List
- Turkish Terror Wanted List
- Interpol Yellow Wanted List
- Interpol UN Wanted List
How Does a Sanctions List Work?
A number of targeted sanctions lists are maintained by governments and financial agencies across the world. Sanctions lists are often made accessible online so that firms may search and reference them before engaging in commerce with a foreign individual or company.
Sanctions lists should be an essential part of a financial institution’s anti-money laundering (AML) strategy, since they will have a considerable impact on how and with whom it does business.
Who is on a Sanctions List?
Sanctions can be imposed in response to criminal action or to achieve a foreign policy or diplomatic goal. They are usually passed by a government act or by an international authority, such as the United Nations Security Council.
Targets implicated in the illicit funding of terrorist operations are included on several sanctions lists. The Patriot Act, for example, bars US firms from providing ‘material assistance’ to terrorist organisations, while the UN Security Council Committee enforces laws like the Al Qaida and Taliban Order (2006), which serves a similar purpose. Sanctions lists, in general, are intended to counter:
- Terrorism and Terrorist financing
- Weapons proliferation
- Human rights violations
- Narcotics trafficking
- Violation of international treaties, e.g arms embargo
- Money laundering activities
Processing Sanctions List
Although sanctions lists are relatively simple, in reality they require the processing of massive volumes of data, which includes not only the names of listed persons but also details like known aliases and physical location.
Considering that several organisations work with enormous numbers of clients and transactions on a daily basis, navigating sanctions lists on a case-by-case basis offers a considerable administrative difficulty – if not outright impossible.
Companies may utilise a variety of screening techniques to streamline the search process. Compliance standards should be a consideration when selecting a screening platform. Officers in charge of anti-money laundering should ensure that their system is updated on a regular basis to maintain validity and accuracy.
The Responsibility to Report
Financial institutions need to know what to do if they obtain a name match on a sanctions list in order to preserve regulatory compliance. They must determine the possibility of a match: many people have similar names, therefore false positives are likely.
Secondary information, such as location, may be utilised to determine the reliability of the match. Screening services can help by providing contextual elements to searches, improving the speed of the process.
Institutions should notify the relevant financial authorities and wait for instructions if they are confident that a proper match has been returned.
Technological Challenges in Sanctions Screening
Businesses must check both new and existing employees against different sanctions lists. Financial institutions either keep their own watchlists or subscribe to third-party watchlists. Following that, they use particular tools to examine and match their customer and third-party databases in real-time or on a regular basis for prospective sanctions alerts. Customers or third parties who have been confirmed to be matches are blacklisted and reported. The goal of a sanctions screening programme is to avoid negative experiences for genuine consumers as well as to detect sanctioned clients and prevent them from transacting.
Financial institutions have moved away from rudimentary name matching methods and toward rules-based screening solutions as a result of recent changes in the sanctions area and the enormous volume of entries to be checked. The number of alerts issued for screening matches, on the other hand, remained high, with a false positive rate of more than 95%. False positives are a productivity drain since they take a lot of time and resources to fix. This can result in massive alert backlogs, expensive operational costs, a poor customer experience, and business loss. False negatives can occur when ineffective methods are used, allowing designated businesses to slip through the compliance net and incur heavy fines.
The Impact of Machine Learning on the Efficiency of Sanctions Screening
Reasons why existing screening tools remain inefficient and produce large false positives:
- Inability to merge relevant data from multiple systems into a standardised structure
- limited consideration for secondary information such as date of birth, occupation, address and bank identification codes.
- Inadequate support for data in non-Latin characters
- Ineffective handling of name ordering, mis-spelling qualifiers, titles, prefix and suffix
- Lack of evidence-based alert review mechanism
To be effective, sanctions screening technology must be simple to use and have adjustable risk-based settings, allowing financial institutions to avoid over-screening and tailor screening criteria to their risk appetite. Financial institutions will be able to fine-tune their screening method to reflect the company’s risk exposure when dealing with imprecise or erroneous data, reducing the number of false positives.
Our Smart Screening Solution
Tookitaki created a Smart Screening solution using powerful machine learning and Natural Language Processing (NLP) techniques as part of its award-winning Anti-Money Laundering Suite (AMLS). The solution helps reliably score and detect a genuine match from a false match across names and transactions in real-time and batch mode while solving the aforesaid difficulties. The solution covers politically exposed individuals (PEPs), unfavourable media, and local/internal blacklist databases in addition to sanctions lists. Within a financial institution, the transaction screening function triages and assesses dollars, products, and assets between parties or accounts.
Tookitaki Smart Screening solution offers the following benefits to the customers:
- More focus on alerts that matter
The solution offers a smart way to triage screening alerts by segregating them into three risk buckets – L1, L2 and L3 – where L3 is the highest-risk bucket. The highly accurate alert classification helps clients allocate time and experience carefully and effectively address alert backlogs. Compliance analysts can focus on those high-risk cases (L3 and L2) that require more time to investigate and close. Meanwhile, they can close low-risk alerts (L1) with minimal investigation.
- Better risk mitigation with reduced undetermined hits
To derive vivid connections and accurately score all hits, the Tookitaki solution employs natural language processing (NLP) to process free texts and infer entity attributes such as age, nationality, and job title, as well as adverse media information, payment reference information, and the stated purpose of the payment in a SWIFT message.
- Superior screening accuracy with improved name matching
It can handle typos, misspelling, nicknames, titles, prefix, suffix, qualifiers, concatenations, transliteration limitations and cultural differences for accurate hits detection.
- Time/cost savings with faster implementation
Enabling faster go-live, the Screening solution comes with ‘out-of-box’ risk indicators across primary and secondary information of a customer for screening to accurately detect a true hit from several watchlist hits.
- Low model maintenance costs
Too many lists with frequent updates have made screening more complex, prompting banks to introduce new rules and change thresholds. Tookitaki’s Smart Screening solution can self-learn from incremental data and feedback to provide consistent performance over time.
- Easy integration and flexible deployment
The solution has connectors to seamlessly ingest varied data points from multiple internal and external source systems and convert into a standardised format. Further, it provides API-based integration with primary screening systems, making the integration process easy, seamless and cost-effective. In addition, it offers on-premise and cloud deployment options.
- Faster decisions with explainable outcomes
Equipped with an advanced investigation unit that provides thorough explanations for each alert and facilitates faster decision-making, reducing alerts backlog. Its actionable analytics dashboard for senior management helps monitor a bank’s sanctions risk across business segments, jurisdictions, etc. over a time period.
Recently, our AMLS solution went live within the premises of United Overseas Bank (UOB), one of the top 3 banks in Singapore, making us the first company in the APAC region to deploy a complete AI-powered AML solution in production concurrently to transaction monitoring and name/sanctions screening. By deploying AMLS, UOB could effectively create workflows for prioritising alerts based on their risk levels to help the compliance team focus on those alerts that matter.
A complete revamp of existing sanctions compliance processes is imperative for financial institutions given that the international sanctions space is becoming more complex. It is time to embrace modern-era intelligent technology to enhance efficiency and effectiveness in AML compliance programs, establish next-gen financial crime surveillance and ensure robust risk management practices.
For more details on our Smart Screening solution, please contact us.
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