Sanction Screening the Intensive Care Patient…Innovation the Cure!

Sanction Screening the Intensive Care Patient…Innovation the Cure!

by | Anti-money laundering, Screening, Terrorist financing

However, minor the task of sanction screening or name filtering sounds it contributes to a significant amount of false positives and is a time consuming task that leaves less time for other AML patients.

And in today’s environment of tighter AML regulations, constantly evolving instant payment initiatives, open banking (i.e. API) and mobile wallets, as the complexity increases so do the false positives.

While this presents major opportunities it puts a lot of pressure on the risk and compliance systems at financial institutions, which need to detect and flag actual threats in real-time. And this new reality has arisen, let us not forget, at a time when regulators are imposing ever-increasing responsibility on those people who are tasked with keeping a financial institution from being compromised for money laundering and terrorist financing.

Therefore, screening individuals and entities is a key task as well as a legal requirement of any compliance program.

“A financial institution discovered, after employees returned from the weekend, hundreds of SWIFT payments had not gone out, because the system had falsely identified the beneficiaries as a sanctioned name or entity”   

The Challenges of Name Screening

Sanctions lists

Sanctions lists can be found in all formats and sizes. Some are country-based, often following United Nations resolutions to promote world peace and human rights; they prohibit certain if not all transactions. Other sanctions are motivated by politics and foreign policy at a national level, as is the case with the United States’ economic embargo against Cuba. A third category imposes targeted sanctions (e.g. the freezing of assets, travel bans and arms embargos) against specific persons, groups, undertakings and entities, as is the case with any terrorist group such as the ISIL (Da’esh) and Al-Qaida sanctions lists.

Many of the national sanctions lists are based on sanctions imposed under UN resolutions, so many of the names appearing on the UN lists also appear on supranational lists such as those issued by the European Union, as well as national sanctions lists such as the USA’s OFAC and the UK’s HMT lists.

Sanctions lists are fairly straightforward. The course of action regarding persons and entities on sanctions lists is clear – they are a no-go for most financial institutions and when confirmed a Suspicious Activity/ Transaction Report (SAR/STR) must be submitted to the local financial investigation unit (FIU) authority. Complication is manifested when a company is not on any official sanctions list, but a shareholder is, therefore you are required to treat it as a sanctioned entity.

Watch lists

Watch lists serve the purpose of assessing a client’s potential risk and includes (among others) PEPs. A politically exposed person (PEP) is someone who has been entrusted with a prominent public function and therefore presents a higher risk for potential involvement in bribery and corruption by virtue of their position and influence. The Financial Action Task Force on Money Laundering (FATF) issued its latest definition of PEPs in 2012:

  • Foreign PEPs: individuals who are or have been entrusted with prominent public functions by a foreign country, for example Heads of state or Heads of government, senior politicians, senior government, judicial or military officials, senior executives of state-owned corporations, important political party officials.
  • Domestic PEPs: individuals who are or have been entrusted domestically with prominent public functions, for example Heads of State or of government, senior politicians, senior government, judicial or military officials, senior executives of state-owned corporations, important political party officials.

This distinction is important for a risk-based approach. Also it’s important to note that there are still countries who do not subscribe to the notion of domestic PEPs being a risk at all.

In addition, persons who are not politically active but who have been entrusted with a prominent function by a state-owned enterprise or an international organization, for example members of senior management, directors, deputy directors and members of the board or equivalent functions may also appear on watch lists.

Being on a PEP or other watch list obviously does not mean that a person is corrupt, but that person presents increased risks owing to the possibility that an individual holding such a position will have far greater opportunity to misuse power and influence for personal gain, or may be open to malign influence by a third party. A point that is often overlooked, but really important as bribery convictions reach all-time highs, is the risk that business partners may pose if they qualify as “public officials” based on their company’s ownership structure if fully or partially state-owned.

Law enforcement agencies, security authorities, national and regional agencies also disseminate various lists. These lists (e.g. Interpol’s Red Notices, the FBI’s Crime Alert List, Europe’s Most Wanted, Singapore Investors Alert and IOSCO consumer protection) can help financial institutions and other organizations avoid doing business with a wrong party and from being drawn into a fraudulent scheme or unwanted scandal.

Adverse media lists

Adverse media comes from a range of local, national and even global sources as well as online social platforms. Adverse media can support a financial institution or a corporate company’s decision to engage or not to engage in a business relationship based on the risk associated with the client from negative news. Adverse media can reveal potential involvement in money laundering, terrorism, various criminal activity and other potential crimes that could have a reputational backlash for a firm.

Lists in general

Although, many lists are publicly available, there are technical challenges because these sources have different ways of presenting information. Some offer well-structured information in downloadable XML files, others in CSV or delimited text files, while others can be drawn from social feeds, blogs, web posts and many are unstructured, and still other sources have online lists across multiple web pages, and some are even in PDF format only.

Not to mention the URLs are constantly being moved, without notice. Therefore, a firm’s name screening might not be including an important source, because the URL changed without notice.

Despite the apparent simplicity and straightforwardness of list screening, selecting the lists that will benefit all areas of your financial crime prevention program can therefore be a daunting task. Here are a few factors to consider:

  • The geographical jurisdiction(s) in which you operate
  • The requirements of local and foreign regulators in the area you operate
  • Your organization’s risk assessment – this must be consulted as a guideline
  • Is an appropriate data structure provided?
  • Does the list provider deploy technology that enables more cost-effective means of data deployment (e.g. through the cloud or interfacing via API)?
  • What formats are data files available in?
  • How up-to-date and “clean” is the data? The lists can hold millions of entries. How well does it manage duplications, expired records etc.?
  • An appropriate update schedule and updating by delta files are a “must have”
  • If an online search function is provided, what techniques are being used to match names?

Data

  • In many cases and for many reasons an institution’s data will have gaps and inconsistencies following the old data processing axiom of garbage in garbage out (GIGO). On the other hand, we’re trying to match against hundreds of lists that have different ways of presenting the information.
  • Inconsistency in basic things like abbreviations (Sr./Senior, Inc./Incorporated, AG/Aktien Gesellschaft, nicknames, etc.) and translations of words that have the same meaning but are spelled different e.g. Germany (EN), Allemagne (FR), Deutschland (DE) can all impact screening results.

Transliteration

A majority of the relevant lists published are in a Latin character set, while many of the names on them originate from countries that do not use the Latin alphabet. Therefore, names that are Chinese, Greek, Islamic, Russian and Thai, etc. must be transliterated from their home language to a Latin one. However, the complication does not end there. For example, in the Arabian Peninsula, Jamal is pronounced Jamal, in Egypt Gamal, and in Algeria Djamal. These are all the same Arabic word, but one that is spelled (transliterated) in various regional ways when written in English.

A further example of transliteration is the voiceless uvular plosive used in Arabic and other languages. It is pronounced approximately like English [k], it’s pronunciation varies between different languages and different dialects of the same language. The consonant is sometimes transliterated into “g”, sometimes “k”, and sometimes “q” in English.

For example, the former Libyan leader’s name can be spelled in various ways:

  • Gaddafi
  • Qadhafi
  • Kaddafi
  • Gadhafi
  • Ghathafi
  • Qaddafi
  • Ghadafi

Beneficial owners

Opaque ownership structures present a real challenge for KYC as criminals, and politically exposed persons (PEPs), etc. hide behind corporate structures.

A company might not be on an official sanctions list, but according to an Office of Foreign Assets Control (OFAC) rule it can be blocked if stakeholders who are on lists have ownership equal to or above 50 percent (this is known as the 50 Percent Rule); thus there is a good chance that the company in question will itself be treated as a sanctioned entity. To put it simply, if company X is blocked and it owns 50 percent of company Y, company Y is also considered blocked, even if that entity doesn’t appear on the OFAC Specially Designated Nationals (SDN) list.

For this reason, it is imperative that corporate ownerships are verified when dealing with certain countries and corporate structures to ensure that none of the beneficial owners are prohibited persons under OFAC regulations.

Practical Actions to take Now

Given the various points raised above, here are some practical steps you should take if you wish to make efficient use of lists and increase the effectiveness of sanction and Pep filtering:

  1. Data Integrity. Get your data in order. A database built on the principles of good data, properly spelled names, sound data structure, and format will go a long way to improving the identity matching process.
  2. Automated Data Collection at every point of customer engagement
  3. Do not simply perform risk assessment, “live it”. This is critical in leveraging the understanding of how these risk exposures impact technological decisions and operational areas of the institution.
  4. Test, test, test – perform random checks to ensure that technology and operational processes are working appropriately and are being consistently applied. Review reports to understand when and why changes are necessary.
  5. Check AML data providers for company credibility, data accuracy,
    well-structure data, depth of content, customer service/support, data quality verified by third party, etc

Apply Innovative Technology

Artificial Intelligence 

True, there is a lot of hype about Artificial Intelligence (AI) and most AI examples that you hear about today – from Google Assistant, Alexa, Siri, or Bixby, to self-driving cars – rely heavily on deep learning and natural language processing. Using these technologies, computers can be trained to accomplish specific tasks by processing large amounts of data and recognizing patterns in the data

Therefore, if you are serious about sanction screening and tackling money laundering with an acceptable return on your efforts and investment, you need to acquaint yourself with artificial intelligence (AI) and machine learning (ML).

Artificial Intelligence and Machine learning uses two types of techniques: Supervised, models are trained on data with known inputs and outputs (also known as categorized data) to identify potentially suspicious transactions. while Unsupervised, models are exposed to raw data to find hidden patterns or intrinsic structures that might signal money laundering or other financial crimes.

The importance of this is demonstrated with the use of supervised learning in sanctions screening where every payment transaction must be screened to check if any beneficiaries are on a sanction or watch list.

However, screening systems produce a lot of false positives that must be dispositioned by a human reviewer, before the transaction can leave the gateway or employees which are greeted by thousands of false positives after an overnight batch screening.

Hopefully, AI can be trained, well enough, to eventually takeover much of the task of reviewing these false positives. There can’t be enough said to the urgency of experimenting with artificial intelligence (AI) now as these models and algorithms need to be constructed, systems set up and then trained, tested, trained, tested and trained until these technologies are taught to address the repeatable high-volume of false positives.

That said, AI is not in itself a “silver bullet” and the process of getting these models up and running can be laborious, therefore, banks should consider cloud-based multi-tenant solutions that share out the cost burden and a can improve time to deployment.

Blockchain Technology

Other technological advances, such as distributed ledger (e.g. blockchain) technology, will help to improve banks’ ability to monitor complex, multi-part transactions. These “smart contracts” with advance algorithms, will allow financial institutions to securely parse data through an AML engine on the blockchain,” in this way banks can store and share data, thus eliminating excessive complex bureaucracy involved in information sharing.

Paul Allen Hamilton

I can be contacted on LinkedIn @ https://www.linkedin.com/in/paulhamilton2/

Photon Photo – Shutterstock

Instant payments: Enormous Potential versus Financial Crime Risks

Instant payments: Enormous Potential versus Financial Crime Risks

by | Anti-money laundering

   

The world of banking continues to evolve at a breathtaking pace and is becoming ever more competitive. Once a new technology has come to market, banks are faced with a dilemma: do we embrace it and run with it, or do we let our competitors gain a first-mover advantage? Delay implies a commercial risk. But the operational and compliance risks that you take on as a first mover may be even greater.

Given the harmonisation of national payment systems across regions, the focus has shifted to international payments and to improve the overall user experience like speed, cost, reliability and traceability. Therefore, payment processors today are seeing some major developments, with new tools appearing such as SWIFT’s gpi and SEPA’s instant payment. These instant cross border payment initiatives are a prime example of what will become the norm in payments.  

The rapid pace of digitalisation of payments brought growing market pressure which have led cross-border payments to undergo significant infrastructure modernisation. The overall trend in digital transactions which are increasing at 6% per year alone in Europe. Total number of traceable transactions in Europe increased from 2013 to 2017: from 113B in 2013 to 144B in 2017 (+27,4%; CAGR +6%)

Number of Digital Payments: Global E-Payments increased from 28,6B in 2013 to 56,5B in 2017 (CAGR: 18,6%); Global M-Payments increased from 24,6B in 2013 to 70,4B in 2017 (CAGR: 30,1%)

Banks that offer this service will gain a competitive advantage over banks that don’t provide it. Clients want their payments to be processed quickly because for them it increases efficiency, transparency, convenience, and financial control. For small and medium-sized companies, this form of payment processing helps alleviate liquidity stress and counter party risk. And, in general, people have grown accustomed to things moving fast, so they have little patience and understanding when payment processing is slow.

Instant payment allows sellers and buyers to exchange money and purchase services in seconds. Funds are received in the payee bank account almost immediately, instead of requiring few business days. That can make a significant difference to a small business’s cash flow, in particular, and it means less time spent waiting for money to clear from the buyer’s point of view. Fast transactions are a common requirement in the new economy, especially with increased mobility: the current generations of customers (so-called millennials and beyond) want to be able to make payments anytime, anywhere, using their mobile devices.

So, what’s not to like about instant payment?

Well, quite a lot, actually. Instant payment processing makes it more difficult to detect financial crimes like money laundering and financial fraud. Criminals want to move money as quickly as possible through a number of accounts at different international banks to disguise the origin of funds. There is no faster way to do this than with instant payments. How can a bank possibly detect money laundering activity in a real time world when transaction monitoring is conducted in a batch process needless to mention the more complex criminal activity?

The Bangladesh Bank heist is a perfect illustration of the future complexity involved in monitoring instant payments.

From the $ 81 million stolen from the Bangladesh Bank in February 2016 only $ 15 million has been recovered and there is still no word on who was responsible. Cyber attackers illegally transferred US$ 81 million from the Central Bank of Bangladesh (CBB), to several fictitious bank accounts around the world, by subverting their SWIFT accounts. The hackers used the SWIFT credentials of the CBB to send dozens of fraudulent payments to fake accounts in the Philippines, and other Asian banks. This was without questioned a well-planned attack that used time differences and regional holidays brilliantly.

How will current anti-money laundering systems work in a world of instant payments?

Its difficult enough for financial institutions to monitor against money laundering violations when it takes three to five days for a transaction to be cleared, or at best overnight. With instant payment, the near-impossible becomes totally impossible using conventional methods as transactions clear in a matter of milliseconds. By conventional, we mean here rule-based approaches, where suspicious transactions are put in a queue and investigated in batch mode.

Even in a world operating in batch, traditional AML systems generate too many false positives (typically between two and 15% of all transactions) and therefore imposes a huge workload on banks and investigators.

Number of Suspicious transactions reported to UIF: +51% (’12-16), from 67K in 2012 to 101K in 2016

With instant payment, this problem is greatly increased because banks are under pressure from customers and consumers to clear transactions as quickly as possible in order to meet the agreed level of service.

Transaction monitoring systems built on current technology and based on machine learning offers the only credible answer. By creating algorithms that learn from past results with the expertise and knowledge of AML compliance officers, the system learns to identify false positives, and compliance officers can focus on alerts where there is a higher probability that money laundering is actually occurring.

Another technology-based approach that has been developed recently, called visual mapping, provides insights into how instant payments are moved around. Suspicious payments can be tracked as they move between bank accounts, regardless of whether the payment amount is split between multiple accounts, or those accounts belong to the same or different financial institutions. The software creates a visual map of where and when money has moved, providing new insights and intelligence for fraud and compliance teams to take action.

By bringing together transactional data from multiple financial institutions and running sophisticated algorithms, such solutions can identify the so-called “mule accounts” that are used for money laundering and other illegal activity. Many of these accounts are not set up directly by the criminals themselves but via a number of scams including phishing, spam email, instant messaging etc.

It is worth pointing out that while technology is a necessary condition for successful AML compliance in the new world of instant payment, it is not a sufficient condition. In addition, financial institutions will need to review their compliance procedures and their service offerings to strike the optimum balance between competitiveness and security.

What should be the upper threshold for an instant payment?

Should they give priority to VIP and profitable customers when reviewing suspicious transactions? What about social and political issues? (For example, Muhammad is the world’s most common name, and also appears a lot on sanctions list. But that also means a significantly large number of false positives, which could lead to claims of unfair profiling.) And finally, even with advanced technology and effective redesign of processes and procedures, banks may still need to increase their staffing in order to meet the challenge. They need to ensure that they have enough staff with sufficient knowledge and authority to be available to review transactions quickly.

Some banks have offshored or outsourced simple customer due diligence functions to keep pace. That said, the trend is definitely towards investment in more technology. As a recent article in The Economist put it, “Now, the biggest question for bank controllers is how many humans they can replace with bots without compromising compliance […] Banks are going into partnership with some of the hundreds of ‘Regtechs’ that have sprouted in recent years.” Technology must be a large part of the solution, but banks will just need to take care and seek expert independent advice in reviewing the new Regtech apps: the regulators and the markets will penalize them should their techno-experiments fail.[1]

For more articles on financial crime and Anti-Money Laundering join the AML Knowledge Centre at https://www.linkedin.com/groups/8196279/

[1] “The past decade has brought a compliance boom in banking”, Economist 2 May 2019.