by Admin | Anti-money laundering, Compliance, Screening
How
did we get here?
Since 9/11 financial institutions have made enormous investments in
their anti-money laundering compliance programmes, but they are still faced
with manual processes and complexities in complying with financial crime
regulations. “One would be hard-pressed to suggest that banks are ignoring the
need for better customer due diligence,” KPMG reported in March 2019. “Indeed,
according to a recent Forbes article, some banks spend up to US$ 500 million
each year in an effort to improve and manage their KYC and AML processes. The
average bank spends around US$ 48 million per year. In the US alone, banks are
spending more than US$25 billion a year on AML compliance.”
Much of the growth in costs is driven on the one hand by fear of fines
for non-compliance (US$24 billion in non-compliance fines since 2008, but the
cost to reputation can be higher) on the one hand and, on the other hand, by
the need to address the high false-positive rates resulting in significant
remediation efforts that rules-based systems generate.
Even though, financial institutions are the ones being fined they are by
no means in this alone. There is an ecosystem of consultants, research
companies, software vendors who also need to be held accountable to their
clients.
The reality is that few of the first generation AML systems were built
to fight crime! These systems were mostly the by-product of business
intelligence (BI) software products (as opposed to AML or financial crime solutions)
that were originally created to provide insights into corporations’ past
performance. Many of these systems are programmed using code that dates back to
the eighties!
After 9/11, many vendors seized the opportunity to position their BI
software as AML compliance solutions. Consequently,
financial institutions find themselves combatting sophisticated fraud, money
laundering, and terrorist financing schemes with outdated BI software using
code that dates back to the eighties!
This is analogous to competing in this
year’s Formula One driving Jackie Stewart’s Tyrrell or Alain Prost’s Renault.
No wonder, the expectations of many financial institutions’ compliance
departments haven’t been met. With an exceptionally
high rates of false positives, i.e. somewhere between 75 and 90 percent of
alerts. In fact, many financial institutions’ compliance programs have
continuously felt under-resourced compared to the volume of alerts and reports
they must review while trying not to disrupt good
business. Thus, financial institutions felt compelled at least those that
could afford to do so built offshore entities taking advantage of lower labour
costs to maintain their systems and manage their KYC alerts.
With all the attention on false positives, false
negatives have a much better chance of slipping through the workflow.
The risk-based approach was supposed to help financial institutions to
get a grip on the high-rate of false positives by profiling customers and segmenting
them based on their risk exposure. However, clustering customers into
segments/categories based merely on products, channels, transactions,
geographies, and industries and then building generic rules with arbitrarily
selected thresholds was a simplistic approach, to say the least, and somewhat
naïve from regulators.
Moreover, all this is happening at a time when legislation has made it
easier for customers to swap from one financial institution to another. Rather
than risk annoying good customers by constantly asking intrusive questions as a
result of false alerts, banks have often further refined the number of segments
… to the point that they become unmanageable.
The light at the end of the AML tunnel
Everyone in the AML ecosystem has come to realize that a better approach
is needed, especially when it comes to verifying false positives. Basically,
the whole industry is banking (pun intended) on big data, artificial
intelligence (AI) and machine learning (ML) to help simplify complex processes
and automate repetitive tasks. Therefore, the expectations for AI are
overwhelmingly high according to PriceWaterhouse Coopers estimates, the
contribution of AI to the gross world product by 2030 to be around 14 billion
euros.
AI has the potential to remove the chains from AML compliance staff allowing
them more time to deal with non-routine events and complex cases as well as
having better information through a cleaner, more traceable process to make
objective decisions.
Of course, machine learning models can process tremendous amounts of
data, but ML systems still need to learn the difference between a false
positive and a false negative and that in real-time. But there are challenges
that need to be sorted out.
As with every new technology wave: CRM, business intelligence, big data,
predictive analytics, or artificial intelligence, etc. technology
companies can’t resist the temptation to sprinkle the latest
buzzwords on every bit of their software like fairy dust. This gold-rush
atmosphere also has its downsides. In March the Financial Times reported that
40 percent of European AI start-ups do not actually use AI programs in their
products, according to an investigation by the investment company MMC Ventures.
First, there simply isn’t enough well-structured data at most companies that can be used for teaching these ML / AI models. IBM’s Watson, named after the company’s first CEO, has learned this the hard way. Originally developed to answer questions using natural language processing (NLP), and then as a system to assist in the diagnosis of cancer, as of June 2017, the Watson artificial intelligence platform had been trained on six types of cancers which took years and input from a thousand medical doctors.
A second, challenge is that criminals are always adjusting and trying
new schemas and a third is that the financial services landscape is itself changing
continuously, leaving ML and AI platforms with a real-time knowledge gap.
So, caveat emptor: these systems require months and in many cases
years of laborious training, and a lot of support by expensive compliance
experts and data scientists. The experts must feed vast quantities of
well-structured data into the platform for it to be able to draw meaningful conclusions,
and these conclusions are only based upon the data that it has been trained on,
i.e. what happened in the past. An implementation project will involve:
- Learning the transaction behaviour of similar customers
- Pinpointing customers with similar transactions behaviour
- Discovering the transaction activity of customers with similar traits (business type, geographic location, age, etc.)
- Identifying outlier transactions and outlier customers
- Learning money laundering, fraud, and terrorist financing typologies and identify typology-specific risks
- Dynamically learning correlations between the alerts that produced verified suspicious activity reports and those that generated false positives
- Continuously analysing false-positive alerts and learning common predictors
For the most part, financial crime will be driven by advances in
technology and this marriage of regulation and technology is not new in
itself. However, with the continual increase in regulatory expectations, the
staggering levels of cyber-attacks against financial institutions and the disruption
of new instant payment initiatives will not make it easier on people working in
compliance.
In brief, these innovations address many gaps in today’s financial crime
programmes by improving automation in the detection of suspicious
activity, which would be a significant move from monitoring to preventing
financial crime while being more cost-effective and agile. Financial
institutions that have already been down this path before and have been
disappointed would be wise to start with small-scale pilot projects with
limited scope, using agile software delivery methodologies.
And above all, they need to invest in data quality as it is a key
component of any successful financial crime program. High-quality data leads to
better analytics and insights that are so important not only for accurately training
ML and AI models, but also to drive better decisions.
Paul Allen Hamilton and Volha Miniuk
Visit us at: AML Knowledge Centre (LinkedIn)
by Admin | 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:
- 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.
- Automated Data Collection at every point of customer engagement
- 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.
- 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.
- 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
by Admin | Anti-money laundering, Cryptocurrency, Screening
Hardly a day goes by without a news item concerning the use of cryptocurrencies for money laundering. In June the Financial Action Task Force (FATF) told countries to tighten oversight of cryptocurrency exchanges amid growing concern among international law enforcement agencies that cryptocurrencies are being used to launder the proceeds of crime. Countries will now be required to register and supervise cryptocurrency-related firms such as exchanges and custodians, which will
have to carry out detailed checks on customers and report suspicious transactions. Many governments are already acting on this; for example, on 5 August the Thai government announced that it would bring cryptocurrencies under existing financial regulations, monitored by its
anti-money laundering office, AMLO. In a statement to the Thai press, Police Major General Preecha, secretary-general of the Anti-Money Laundering Office (Amlo), neatly summarized the
current lack of visibility over the issue, saying, “We may not find any clue, but that doesn’t mean the wrongdoing does not occur.” Elsewhere, there have been successes. Earlier this year, Europol broke up a Spanish drugs cartel that laundered cash using two crypto ATMs, machines that issue cryptocurrencies for cash. The concern is that cryptocurrencies can be used to transfer money across borders, break down large criminal money transfers into smaller amounts that are harder to detect, and to make payments on the dark web. And while some of this money laundering activity is still conducted using well-known cryptocurrencies, notably bitcoin, criminals are increasingly switching to more anonymized
cryptocurrencies. Yet, while cryptocurrencies are further complicating the AML landscape, it has been argued that the very technology supporting them – blockchain – may contribute massively to reducing the costs and
the challenge of know your customer and anti-money laundering (KYC/AML) through what has already been dubbed the KYC blockchain.
The cost of KYC
As is well known, Know Your Customer (KYC) is hideously expensive for banks. The cost of conducting KYC due diligence investigations of a company or individual can run into tens of thousands. KYC processes provide the backbone of financial institutions’ efforts to combat the financing of terrorism and to detect and prevent criminal behaviours around the world, such as trade-based money laundering (TBML). According to recent estimates, in excess of US$25 billion is spent each year on financial crime risk management in the banking sector, the majority of which is due to KYC. The reason for the high cost is simple: KYC at many financial institutions is extremely inefficient, involving labour-intensive manual processes, duplication of effort and a high risk of error. Up to 80
percent of the effort associated with KYC is dedicated to information gathering and processing, and only 20 percent to assessing and monitoring that information for critical insights. It can take weeks or even months to identify a beneficial owner by locating and validating the relevant physical and computer records. Moreover, the work is typically done many times over, even for the same customer. Each Line of Business (LoB) within a bank performs its own customer checks. The legal entity – be it an individual or an organization – typically provides KYC documents each time it requires services from different LoBs within the same institution. It is difficult or impossible for LoBs to share the information in a secure and easy manner while protecting confidentiality and privacy. Poor customer experience and high operational costs for the bank are not a good business model in a competitive environment. In other articles we have considered how machine learning – a subset of artificial intelligence – may help to address this challenge, but a key problem remains, which is that the trail of transactional records that is required to identify money laundering is typically spread across multiple LoBs, financial institutions and legal jurisdictions. And this is precisely where blockchain, the underlying technology for bitcoin and other cryptocurrencies, could reduce inefficiencies and duplication of effort in KYC information gathering
between legal entities within a large financial institution or even between competing banks.
The Singapore trial
But how realistic is such an approach? A prototype tested in Singapore in 2017 involving OCBC Bank, HSBC, Mitsubishi UFJ Financial Group (MUFG) and the Infocomm Media Development Authority (IMDA) was the first KYC blockchain in South-east Asia and the most public trial to date. It was
claimed that the prototype could solve the current practice of collecting and verifying personal information from customers repeatedly, reducing the costs by 25-50%, according to KPMG. Like cryptocurrencies, a KYC blockchain prototype operates on a distributed ledger technology and
enables structured information to be recorded, accessed and shared across a distributed network using advanced cryptography. With the customer’s consent, LoBs can share information accurately and efficiently with a clear audit trail generated on the blockchain. With a KYC blockchain LoBs can securely search customer information, generate requests for KYC
documents from other LoBs that have already verified customers, store validated customer documents and re-use them where required. The infrastructure can also be used for sharing customer profiles and alerts, which can trigger mitigation procedures when required in response to
alerts. The Singapore prototype reportedly remained stable even with a high volume of information, was resistant to tampering and maintained data confidentiality. Some fintech companies have now built their own blockchain technology-based distributed ledger systems.
Self-sovereign identity systems
A further development of blockchain-based technology that may reduce KYC costs is “self-sovereign identity” (SSI) systems. Through the use of distributed ledger technology, SSI enables individuals to retain control over their data while at the same time being verifiable for banks and other relying parties through the public recording of verified claims. SSI could be the next step in identity management, combining traditional means of identification with new technology-based systems (such as asymmetric key, one-time password, biometrics) in a distributed system. Its relevance to KYC is that it adds a layer of security and flexibility allowing the identity holder to reveal only the necessary data for any given transaction or interaction. Under existing practice, a bank has to access highly centralized pools of data time and again in order to verify identity. This has a high degree of dependence on data sources that are vulnerable to hacking. Data vulnerability is potentially damaging to both the bank and the bank’s customers, whose identity may be stolen and either used to carry out fraudulent transactions or to provide that identity to another person, who
can then use it for (among other things) for the purpose of money laundering. SSI could reduce the bank’s dependence on a centralized data pool and processes that were not designed for a decentralized, distributed and instantly connected world. An identity blockchain in which a bank has node status would provide a solution that resolves the conflicting demands of financial security and personal privacy. Such solutions for managing self-
sovereign digital identities are already in a fairly advanced stage of development.
How much of this is hype?
While there are similarities between the technology behind bitcoin and the proposed systems to assist with anti-money laundering, we should be careful that we are not blinded by the hype. As
Investopaedia recently reported:
Compare that open, permissionless blockchain to the “private” or “permissioned” blockchains that established tech and financial services players, along with a gaggle of start-ups, are developing on their own or through consortia. Rather than a trustless network of thousands of strangers, they propose to build small networks of known, vetted actors – or in some cases, to keep the blockchain to themselves. The result makes compliance with [AML and KYC] laws easier … but at some point, these purported blockchains have little to do with the innovation that underpins bitcoin. The truth is that technological change tends to be incremental and evolutionary, building on earlier advances. In the case of blockchain, this “revolutionary” technology is based on the successful combination of several pre-existing technological approaches: primarily, decentralized networks, cryptography, and consensus models. Blockchain makes it possible to exchange values in a decentralized system. Cryptocurrencies and the proposed KYC blockchains have this in common, but the commonality ends there. The blockchain hype cycle has peaked and is now in what Gartner terms the “trough of disillusionment”. This is inevitable. No new technology has ever solved more than a small fraction of the problems faced by humankind (well, not since the wheel). Blockchain (we will increasingly see the terms distributed ledger systems or hyperledgers) will bring benefits in many areas of human endeavour, including AML and KYC, but it will be no “silver bullet”. Doubts remain about its scalability, and the competitive nature of the market, concerns about confidentiality etc. will set limits on its application. That said, there is no question that these technological developments are highly positive. The bottom line? A realistic assessment is that KYC blockchains and SSI-supported onboarding will
not fundamentally transform due diligence processes but, especially if combined with other technologies, they could reduce the cost of KYC by something in the range 20%-30%. That will have a significant impact on banks’ ability to combat common forms of money laundering.
Any major financial institution would jump at that!
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