In this blog, we will explore the intricate landscape of regulations as it applies to cryptocurrencies, the critical issue of sanctions compliance in the cryptocurrency space, and how KYC2020’s DecisionIQ, with the power of AI and collaborative intelligence, is transforming the way regulated entities benefit from these advancements while maintaining the importance of auditability and transparency over opaque solutions and systems.

The Crypto Conundrum: Money Laundering and Terrorist Financing

As the cryptocurrency industry becomes more ubiquitous in the financial systems, it faces similar challenges to traditional financial systems in combating money laundering and terrorist financing. Terrorists and criminal organizations seek to exploit the pseudonymous nature of cryptocurrencies to disguise the illicit origins or destination of their funds. By doing so, they are attempting to evade law enforcement to fund further criminal activities, while leaving a trail of damage in their wake.

Anti-Money Laundering (AML)/Counter Terrorist Financing (CTF) measures have been in place in traditional finance for some time now to help detect, deter, and in some cases prevent financial crimes from happening. While it is estimated that only 5-10% of ML/TF is actually detected, regulators worldwide have issued comprehensive guidelines and directives to at least try to make a dent. The rapid growth of the crypto industry has outpaced these efforts and is creating opportunities for criminals to exploit the regulatory gaps while they play catchup. The complexity of crypto, as it relates to AML/CTF regulations, has made it challenging for cryptocurrency exchanges to comply with new and ever-expanding regulations.

The Rising Tide of Regulatory Obligations

Many custodial and non-custodial Virtual Asset Services Providers (VASPs) are subject to Know Your Customer (KYC) obligations. Among the primary aspects of dealing with cryptocurrency is to put in place transaction monitoring, adherence to the Travel Rule, and screening for sanctions, PEP and Adverse Media on persons and entities who are involved in those transactions. Failure to perform these and other functions that are required by AML/CTF regulations exposes these businesses to significant reputational risk, civil penalties and criminal charges that could lead to prison.

The FATF and the Future of Crypto Regulation

The Financial Action Task Force (FATF) provided clarity related to AML/CTF regulations in its “Guidance for a Risk-Based Approach to Virtual Assets and Virtual Asset Service Providers”. It makes recommendations on how countries, regulators, and virtual asset service providers should manage ML/TF risks related to dealing in cryptocurrencies. This guidance promotes a risk-based approach, similar to that of traditional regulated entities including those that would be most affected like financial institutions and MSBs. It is the member countries’ responsibility  to implement the recommendations to ensure that there are safeguards against widespread abuse in the use of cryptocurrencies in ML/TF related crimes.

KYC for Crypto Companies

A strong AML/CTF compliance program, rooted in a risk-based approach, is essential to align with the FATF recommendation and country-specific regulations. Customer screening and monitoring, understanding the customer’s financial activities and source of funds/wealth, assessing risk and compliance with the Travel Rule, are all key aspects in preventing, detecting and deterring ML/TF offences in financial systems. The role of a qualified compliance officer is pivotal in maintaining the integrity of a cryptocurrency exchange’s compliance efforts. Any lapses in compliance could result in severe fines and penalties, underscoring the importance of rigorous compliance in cryptocurrency exchanges.

To onboard and engage with high-risk customers, businesses must implement Customer Due Diligence (CDD) procedures as of their AML compliance regime. CDD procedures include screening against watchlists including sanctions, regulatory, criminal, and negative news. For businesses that deal with crypto transactions, this is not enough.

OFAC has expanded its purview by incorporating digital currency addresses into the SDN List, aiming to notify the public about specific digital currency identifiers linked to blocked individuals. Originally, the incorporation of cryptocurrency addresses into these due diligence processes was observed during the Silk Road crackdown and in the context of Darknet markets like Hydra and AlphaBay, as well as mixer services such as TornadoCash and Blender. More recently, we’ve seen the inclusion of Digital Currency Addresses related to the terrorist group Hamas in OFAC’s SDN and other watchlists.

Following this development, it is imperative that businesses implement rigorous screening processes for digital currency addresses. This is necessary to guarantee that they do not engage in transactions or trade involving digital currency identifiers associated with a blocked person.

In Conclusion: Screening for Cryptocurrency addresses with DecisionIQ

The importance of robust compliance solutions that include cryptocurrency address screening for sanctions and other related ML/TF exposure has never been greater. KYC2020, with its innovative DecisionIQ, all-in-one Sanctions, PEP & Adverse Media Screening platform, is aimed at transforming the way VASPs, DeFi, and blockchain innovators meet their regulatory obligations.

Businesses need more than just a compliance screening tool; they need a partner that understands the nuances of cryptocurrency compliance as it relates to sanctions.

DecisionIQ combines the power of AI and collaborative intelligence to significantly reduce false positives, provide transparency in the results, and ultimately demonstrate to the regulator or examiner that all steps have been taken to reduce the risk of dealing with a person or entity on a sanctions list. DecisionIQ ensures that every screening decision is recorded and auditable, similar to the blockchain concept of transparency through “proof of work”, or in our case, what we call a “proof of decision”.

KYC2020’s AI-driven technology with collaborative intelligence empowers those dealing with cryptocurrencies to maintain a rigorous compliance program without compromising on quality or incurring unnecessary costs.

“A compelling part of the technology is that it provides a proof of decision for every screening decision. Unlike other ‘black-box’ AI solutions, KYC2020 approach is similar to the blockchain concept of transparency via ‘proof of work.'” – Coinchange

Click here to read the full Coinchange Case Study and discover how DecisionIQ transformed their screening efforts related to compliance by reducing the cost and manual effort in dealing with false positives and bad data.

Verifiable Auditability: DecisionIQ’s “Proof of Decision” approach aligns with blockchain principles of transparency. It provides a verifiable audit trail for every screening decision and reviewer action. Unlike other ‘black-box’ AI solutions, DecisionIQ ensures clarity and transparency through its ‘proof of work.’

Reduce False Positives: False positives can be a significant challenge in compliance. DecisionIQ uses high quality and structured data and a screening decision system with auto-clearing capabilities to deliver one of the lowest false positive rates in the industry, saving your business time and resources while ensuring that legitimate customers are not held up in unnecessary reviews.

Faster Onboarding: Speed is of the essence in the crypto world. DecisionIQ’s rapid screening processes ensure faster onboarding, allowing regulated entities to provide a seamless experience to their customers.

Rapid Review and Clearing System: KYC2020’s Rapid Review and Clearing System (aka RABBIT) is the solution to the slowdown caused by case management systems that are not ‘human factored’ for large volume reviews. RABBIT is equipped with built-in functions for visualizing and processing information on a large scale, making it a more efficient and effective bulk review process over the traditional methods deployed by many others.

Watchdog‘s Automated Clearing Technology: We further improved DecisionIQ’s capability to handle edge cases in AI that result in false positives. Watchdog’s tech stack is designed around Collaborative Intelligence (CI), and it includes a hit pattern recognition system, Contextual AI to predict gender and nationality of names, and a proprietary name relevancy scoring system that is more effective and intuitive than fuzzy scoring. This additional intelligence is used as part of a rule-based system to lower the rate of false positives caused by borderline cases that humans can detect better than AI. Watchdog is also supported by a clear proof of work for verifiable auditability.

DecisionIQ offers a range of features designed to meet the unique needs of the crypto industry, ensuring compliance is not a burden but a competitive advantage. With DecisionIQ, crypto companies can focus on what they do best – innovating and providing value to their customers – while leaving the complexities of compliance in capable hands.

Remember, you don’t have to compromise on quality and compliance while lowering costs. DecisionIQ is here to make AML compliance a seamless and efficient journey. Stay ahead in the crypto compliance landscape with KYC2020.­­­

One of the distinctive challenges in this region is the complexity of name screening in Arabic script. Transliterating Arabic names into Latin characters presents specific challenges with significant implications for AML compliance.

This blog delves into the world of AML compliance in the Middle East, discussing the challenges of transliterating Arabic names, navigating the regulatory landscape, and highlighting the key authorities shaping AML requirements in the region. We’ll also emphasize the critical role of precision in AML compliance, particularly when it comes to Arabic language screening.

Understanding AML Regulations in MENA

Significance of the Middle East

MENA covers 6% of the world’s population and contributes to 4.5% of the global economy through 21 diverse countries, it holds a complex regulatory landscape for Anti-Money Laundering (AML) and Counter-Terrorist Financing (CTF).

Some of the key regulators are:

UAE – CBUAE

The Central Bank of the UAE (CBUAE) is the central financial regulator. It oversees, develops, and maintains the UAE’s financial system. The Regulatory Development Division within its Banking and Insurance Supervision Department sets AML/CFT policies.

Qatar – QFCRA

Qatar’s financial sector is regulated by the Qatar Financial Centre Regulatory Authority (QFCRA). Established in 2002, it authorizes and regulates financial services, with a dedicated AML/CFT unit. The QFCRA also actively engages with international standard-setting bodies.

Saudi Arabia – Ministry of Anti-Money Laundering, SAFIU & SAMA

Saudi Arabia takes a coordinated approach to tackle money laundering. The Ministry of Anti-Money Laundering, the Saudi Arabian Monetary Agency (SAMA), and the Saudi Arabian Financial Intelligence Unit (SAFIU). The Permanent Committee for Anti-Money Laundering, coordinates AML efforts at the SAMA head office in Riyadh.

Oman – CBO & CMA

Oman has two primary regulatory authorities. The Central Bank of Oman (CBO) oversees and licenses financial institutions, implementing AML/CFT regulations. The Capital Markets Authority (CMA) supervises Oman’s capital market and insurance sectors.

Egypt – Egyptian FIU

Egypt’s AML/CFT landscape is managed by the Egyptian Financial Intelligence Unit (FIU). It analyzes suspicious transaction reports and ensures compliance. Egypt also commits to combating financial crimes through the National Committee for Combating Money Laundering and Terrorism Financing (NCMLTF).

Jordan – Central Bank of Jordan

The Central Bank of Jordan leads AML/CFT efforts. It regulates and supervises financial institutions, participating in international initiatives against financial crimes.

Lebanon – BDL

Lebanon’s Banque du Liban (BDL) is responsible for AML/CFT oversight, ensuring the stability and integrity of Lebanon’s financial system.

Comprehensive AML Requirements

AML requirements in the Middle East align with international standards, the MENAFATF adopts and implements the FATF 40 Recommendations in the region in a way that does not conflict with the cultural values, constitutional frameworks and legal systems in the member countries. They encompass key areas like Customer Due Diligence (CDD), Transaction Monitoring, Suspicious Activity Reporting, Record Keeping, Employee Training, PEP and Adverse Media screening, Sanctions Compliance, and a risk-based approach.

The Challenges of Arabic Language Screening for AML

In the multifaceted Middle East and North Africa region, where ancient civilizations meet modern finance, an intriguing challenge emerges for Anti-Money Laundering (AML) screening: the diversity of scripts. Regulatory bodies often maintain watchlists with names in Latin scripts, while Middle Eastern banks may have names recorded in Arabic script. This linguistic divergence poses unique challenges for AML screening providers.

Transliteration Complexities

Transliterating Arabic names into Latin script becomes imperative due to the distinctive Arabic script, which features sounds markedly different from English. Currently, there’s no universally accepted standard for this transliteration, leading to variations in spelling that screening providers must navigate. Let’s delve into some transliteration scenarios:

1. The Elusive “ق”: Arabic’s “ق” represents a unique “k”-sound, pronounced deep in the throat. In Latin script, it’s typically transliterated as either “k” or “q,” adding a layer of ambiguity.
2. The Enigmatic ‘ayin: Arabic’s ‘ayin, represented by the letter ع, introduces another challenge. The sound often gets marked with an apostrophe or omitted in English transliterations, generating diverse spellings.
3. Pronunciation vs. Script: Arabic names occasionally defy their written forms. This discrepancy results in multiple Latin transliterations, further compounded by the presence or absence of hyphens.
4. Dialectal Variations: Arabic’s letter “ج” is pronounced differently in Egypt (“g” as in “good”) and elsewhere in the Middle East (“j” as in “jungle”). This linguistic diversity necessitates multiple spelling choices.
5. Segmentation Variants: A single Arabic name can be segmented differently, leading to variants like ” Ghasem Soleymani,” ” Qasmi Sulayman,” or ” Qasem Salimani,” all referring to the same name.
6. Transliteration Discrepancies: Different languages employ distinct rules for transliterating Arabic letters. For instance, English transliterates “ش” as “sh,” while French opts for “ch.” The challenge arises when English documents containing French transliterations intersect, resulting in multiple name variations.

Beyond Transliteration: AML Screening Complexities

Transliteration challenges extend beyond merely matching names in Latin characters. AML screening entails even greater complexities:

• Diverse Naming Conventions: Arabic naming conventions aren’t universally standardized. Varying cultural influences and historical practices contribute to a rich tapestry of naming styles.
• Cultural Diversity: Arabic culture has transcended geographical boundaries, leading to diverse naming traditions across different regions.
• Religious Variability: Arabic names aren’t exclusively linked to Islam; they span various religions, including Arab Christians.
• Traditional Naming Conventions: Some individuals adhere to deeply traditional naming conventions, which may contrast with contemporary culture.

Navigating this linguistic landscape requires not only precise transliteration but also a deep understanding of the cultural nuances surrounding Arabic names. For AML screening providers, it’s a multifaceted journey through the diverse world of script, sound, and meaning in the MENA region.

The Importance of Arabic Name Screening

The significance of understanding Arabic names cannot be overstated. The Middle East is a region where the diversity and intricacies of names pose a unique challenge. Effective screening requires not only a grasp of the complexities of Arabic names but also an appreciation of their paramount importance.

Imagine this scenario: Middle Eastern banks maintain databases replete with names written in Arabic script. Regulated entities, private institutions, and regulatory bodies across the globe, including OFAC (Office of Foreign Assets Control), the UAE Local Terrorist list, the EU Consolidated list, Iraq AML & CFT Office Individuals and Entities, UNSC Consolidated list, and many others, publish lists that feature names in Arabic script, often in advanced formats.

It’s a testament to the global nature of finance and the need for precise AML screening.

Arabic names encompass various components, each laden with significance: the Ism (forename), Kunya (paedonymic), Laqab (surname), Nasab (patronymic), and Nisbah (geographical or clan indicator). Notably, not all Arabic names follow fixed conventions, and Arabic culture transcends geographical and religious boundaries, further complicating the landscape.

To illustrate the challenge, let’s consider “Ali Saed bin Ali al-Houri.” This name comprises the Ism, Nasab, and Nisbah, with each element holding a specific meaning and cultural resonance. Similarly, “abu Hamza al-Masri” combines the Kunya and Nisbah, with “Masri” signifying Egyptian heritage.

Understanding Arabic names is not just a matter of linguistic curiosity; it’s the key to precise AML screening. It enables financial institutions, regulatory bodies, and businesses to differentiate between individuals, eliminate false positives, and adhere to stringent AML regulations with cultural sensitivity. It’s a critical facet of the global effort to combat financial crime and uphold the integrity of the financial system.

KYC2020’s Enhanced Arabic Language Screening

With an increasing number of customers in the Middle East, KYC2020 is committed to meeting the unique challenges posed by the Arabic language. In a region where precision in Anti-Money Laundering AML and counter-terrorism financing CTF screening is of utmost importance, our product team is rising to the occasion.

KYC2020 has responded with an advanced screening engine featuring a customized scoring system for Arabic names. This enhancement excels at detecting diacritics within Arabic script, elevating precision in name matching and identification of unique name parts. It’s a solution designed specifically for demanding lists, including the advanced formats of OFAC’s Specially Designated Nationals list, UAE Local Terrorist list, EU Consolidated list, Iraq AML & CFT Office Individuals and Entities, UNSC Consolidated list, private blacklists uploaded by users and more.

The Arabic language varies across regions and cultures. Latin and Arabic scripts are distinctly different, demanding a customized approach. KYC2020’s scoring engine is meticulously designed to account for these regional nuances. It’s fueled by cutting-edge Natural Language Processing (NLP) techniques and doesn’t just normalize names; it optimizes them. This includes removing diacritics, eliminating unnecessary elements, and enhancing accuracy.

We employ a range of advanced techniques to ensure the accuracy and reliability of our Arabic language screening. This includes a meticulous normalization process that addresses specific intricacies of the Arabic script. Our normalization procedures encompass:

1. Normalization of Hamza with Alef Seat to a Bare Alef: In Arabic script, the Hamza with Alef Seat is a unique character. We normalize this character to a plain Alef to ensure consistent and accurate matching.
2. Normalization of Teh Marbuta to Heh: Arabic script features the Teh Marbuta, a character representing a specific form of the letter Heh. We normalize Teh Marbuta to Heh to ensure uniformity in our database.
3. Normalization of Dotless Yeh (Alef Maksura) to Yeh: The Dotless Yeh, also known as Alef Maksura, is another distinct character in Arabic. We normalize this character to Yeh to facilitate accurate matching and recognition.
4. Removal of Arabic Diacritics (the Harakat): Arabic diacritics, known as Harakat, are marks that indicate vowel sounds and pronunciation. Removing these diacritics helps streamline the screening process and enhance matching precision.
5. Removal of Tatweel (Stretching Character): Tatweel is a character used to elongate certain letters in Arabic script. We remove this character to avoid discrepancies in matching due to variations in character length.

By implementing these normalization techniques, we ensure that Arabic names are accurately represented and matched within our screening engine. This meticulous approach minimizes the chances of false positives or missed matches, contributing to the overall effectiveness of our AML screening solution.

Our customized scoring engine comprehensively addresses phonetic similarities, spelling variations, titles, and honorifics, as well as issues like missing spaces and hyphens, nicknames, and out-of-order components. In essence, it’s a holistic solution that leaves no room for error.

Transliteration between Arabic and Latin scripts is a minefield of potential errors. KYC2020’s engine tackles this using linguistic, orthographic, and phonological algorithms, it minimizes transliteration errors, ensuring that no true positive goes unnoticed. Beyond that, it’s adept at mitigating common errors that often lead to missed matches, such as misspellings, phonetic misunderstandings, and nicknames.

Our commitment to comprehensive screening extends beyond the Arabic language. We’ve obliterated language barriers, regardless of the script. Names in Arabic script or any other non-Roman script are seamlessly processed. Here’s how it works: we translate and transliterate names into Roman characters, normalize them, and integrate them into our persona database. This advanced feature not only facilitates precise searches in the original script but also enables fuzzy searches on Roman name inputs, guaranteeing comprehensive and accurate results across diverse linguistic inputs.

Getting sanctions screening right is a growing priority for financial services institutions in their AML/CFT compliance program. The constant drumbeat of increasing regulatory requirements, the mix of customer profiles that grows more complex by the day, and an ever-expanding menu of financial products and services have only made such due diligence all the more critical.

However, current AML/CFT detection systems and procedures that lean heavily on rules-based screening engines and manual inputs can not keep up with today’s reality. The issue of false positives is arguably one of the most consequential areas this plays out. On one hand, financial institutions need a new set of tools to accurately detect true matches and reduce the occurrence of false positives. On the other hand, too few false positives in an indication that an institution’s AML systems are not appropriately calibrated to catch the wily evaders.

What are False Positives and Why Do They Happen?

In its most basic form, an AML compliance program includes three fundamental functions:

1. Assess the risk of new and current customers
2. Implement screening measures to weed out clients who present a high risk for financial crimes
3. Conduct ongoing monitoring of customer activity

There are really two goals to an organization’s AML program. The first is to create an alert system that can efficiently identify and actively prevent the emergence of money laundering and other financial crimes within the organization. The second is to avoid regulatory sanctions and their fallout like the plague.

These two goals are often at odds with each other. Moving from financial crime identification to financial crime prevention can be a resource-intensive high-wire act.

In order to comply with the strict regulatory requirements and stiff fines for non-compliance, financial institutions will often operate on the side of caution in their screening and monitoring efforts.  That is, they will lean towards “Over-Screening“. In practice, this means the definition of suspicious activity, for example, would include a very broad range of actions and behaviors– each of which will generate an alert.

An AML alert does not necessarily mean that criminal activity of any kind is in fact happening. It is quite common for a subsequent manual review to result in a cleared warning. When this happens, it is called a false positive.

Such manual reviews are simply “baked” into the AML compliance system of many traditional and established financial institutions. The time and resources required to conduct this oversight is then written off as the cost of doing business.

The added piece of mind comes with a big price tag, however. The industry average rate of false positives currently hovers around 42%. But for larger institutions it can reach a whopping 95%. This translates into millions and even billions of dollars of lost revenue each year.

Another indirect cost of a high false positive rate is the negative impact it has on the customer experience. In recent years, the financial services industry has been required to pour more resources into the experience users have as they move through the buyer’s journey. According to the 2020 Digital Banking Report, customer experience is a key element in maintaining a competitive edge. Customers increasingly want financial institutions to provide personalized services. This is putting increasing pressure on financial institutions to perform to rapid, frictionless transactions and offer personalized customer communications.

A large number of false positives can slow down the on-boarding process as well as customer transaction times. They are thus an obvious stumbling block. However, compliance teams must find a way to reduce their rate of false positives without compromising the accuracy and effectiveness of AML transaction monitoring and screening.

What are Your False Positives Telling You?

The solution to the false positives conundrum is to fundamentally change the way your organization perceives false positives in the first place. False positives are a necessary and unavoidable occurrence. When your rate of false positives stays within a healthy range appropriate for your user base and risk profile, then it is a sign that your institution is on the right track.

When the number of false positives steps outside of that range, then you’ll want to pay attention. You are getting valuable information about your AML compliance efforts. Usually, it means that something within your system has to be tweaked or reworked.

Let’s consider a few examples…

Scanning sanctions watchlists and associated data, such as adverse media and PEP lists, coupled with ongoing transaction monitoring require firms to process and analyze a vast amount of information from a variety of sources which are themselves evolving. With sanctions screening in particular, these sources often vary wildly in quality and completeness, and they are bloated with redundant or useless information. On top of this, they generally come in an unstructured format.

A high rate of false positives could simply be the result of relying on poor data pools full of outdated or irrelevant information that has been ineffectively culled, organized and structured. Of course, the more confusing the data, the more challenging it becomes to discern false positives from true positive matches.

For this reason if your institution is experiencing an unusually high rate of false positives, you may want to reevaluate your data capture. You need to ensure it truly fits the needs of your organization along with the reality of the market base you are servicing.

For example, we developed VisionIQ, a comprehensive watchlist database with access to over 1000 watchlists and automatically updated news sources from more than 200 countries. VisionIQ is powered by advanced technologies, such as artificial intelligence (AI) and natural language processing (NLP). This smart data system is structured and organized to make it easy to selectively group watchlists for different risk profiles and screening criteria. Data is de-duplicated and cross-linked across watchlists to better resolve the data into personas.

Smart data by itself may help reduce the rate of false positives, however, it may not be enough. Part of the challenge is being able to manage the sheer volume and velocity of data creation today, as well as the constantly growing number of data sources. Even the best compilation of data and news is useless without an intelligent search and scoring engine built on AI algorithms and machine learning. Such an automated screening solution can significantly reduce labor-intensive manual interventions. In the process it will significantly minimize False Positives, while helping to ensure that True Positives are discovered.

Utilizing an automated screening solution solves yet another factor in a high false positives rate: the need for real-time data updates. It’s common for those involved in financial crimes to change their names or move to another region in order to avoid prosecution. It’s also common for innocent individuals to make such life changes.

Your organization should thus ensure that the customer data you are relying on is both relevant and up-to-date. A false positive might be created, for example, if a firm neglects to update a user’s change of name or list their new home address– particularly if the new region presents less of a risk than that of the previous residence.

When Even a Little Becomes A Lot

When it comes to false positives, some organizations, for example, a financial services business, may discover that their ability to conduct manual reviews on even a moderate rate of false positives is a struggle. Despite all the advances in technology, sanction screening still requires a significant amount of manual intervention that can dramatically slow down and disrupt the customer verification and on-boarding processes. Many sanction screening solutions on the market are designed to filter a set of watchlists. The search results must then be manually exported for review.

For nimble organizations with limited human resources, there are stand-alone decisioning systems that yield screening decisions in real-time without requiring manual review. This of course includes the automatic clearing of false positives using AI.

A smart, AI and NLP based sanction and PEP screening decision system, like our very own DecisionIQ, can render 95% of all verification decisions automatically within a 60-second response time and a low decision error rate. For records that still require manual screening, they can be sent within minutes to compliance officers, who should have all the info they need to take immediate action.  The screening decisions are then setup for ongoing monitoring based on your risk-based approach.

Slow customer on-boarding and the constant drumbeat of false positives will quickly eat up your profitability and your competitive advantage. On the other hand, failing to identify criminal financial activity can lead to unwanted regulatory attention and action.

The use of smart automated technologies can help to alleviate the challenge of false positives in sanction screening more effectively, more rapidly, and more affordably than ever before.

In the previous article of the KYC 2020 Guide to PEP Screening, we learned that engaging with PEPs and RCAs who are involved in financial crime can be damaging to your financial institution’s reputation. Although you may already recognize the risk and have tried to address it with legacy screening systems, your current program may not provide enough protection and oversight.

AML/CFT systems and procedures need to protect your financial institution against corrupt PEPs and RCAs. However, these days they must do so while accurately verifying legitimate transactional activity and creating a positive customer experience.

Slow customer onboarding and the constant drumbeat of false positives will eat up your profitability and your competitive advantage. On the other hand, failing to identify corrupt PEPs or RCAs can lead to unwanted regulatory attention and action.

Thanks to AI and advancements in regulation technology (RegTech), screening for PEPs in global watchlists, government sources, and in adverse media is more effective, fast, and affordable than ever before.

Over the next four years, the global RegTech industry is expected to reach $16 billion. Much of this growth is driven by a wave of up-and-coming fintech innovators. These companies are harnessing advanced technologies to make AML compliance more doable and budget-friendly for smaller organizations and FinTech. In the process, they are leveling the playing field for financial innovators against their bigger, more established competitors.

Considerations Before Choosing PEP Data and Screening Solutions

One of the most important things to understand in your search for PEP data and screening solutions is that not all solutions (or vendors) are created equal. Not all solutions will be a perfect fit for your organization, either.

To give your institution a good chance of finding the most suitable tools for the identification and prevention of PEP financial corruption, keep in mind the following three considerations:

1. What are your needs?

Which kinds of PEPs or RCAs do you work with? What types of products or services does your organization offer that would be of interest to them? What is your workflow, and what AML/CFT compliance tools or technologies do you already employ?

Another important point to consider is how much direction and assistance you need to set up and run these tools. At KYC2020, for example, our CAMS and Vskills certified specialists help clients define a risk-based approach to compliance that fits their risk profile. We can also tailor our products to fit established workflows and assist with the integration.

2. What are the specifics of the PEP data and screening solution?

What is the scope of the solution? What methodologies does the vendor use? Does the AML/CFT compliance database go beyond sanction lists and other standard databases? Does the vendor also include other sources of data for PEP identification, such as negative news databases? How much of this data is truly necessary and how well is the data structured and de-duplicated? Does the vendor use advanced technologies such as AI and NLP for identity recognition and resolution? What search technologies does the software use to filter matches? How often is the data updated?

3. What kind of relationship does the vendor offer?

Who is the vendor and how do they work with their clients? Do they have experience working with your type of financial institution, credit union, Fintech company or MSB? Do they offer direct assistance and support to get started or integrate, as mentioned above?

Elements of a Good PEP and RCA Screening Tools

The most reliable, efficient, and modern data screening systems for PEPs and RCAs will include the following three core functions:

Smart Screening and Verification

Your institution is required by law to conduct reasonable procedures to identify PEPs either before a relationship is established or as part of the onboarding process. The quality of such screening, however, depends on the quality of the data you use. PEP and RCA sanctions screening always starts with traditional sanctions lists and Regulatory Authority databases. However, these watchlists are aggregated from thousands of independent sources, are highly unstructured, and are constantly changing. Simultaneously, sanctions are becoming more complex, while the number of regulatory bodies are growing.

In short, maintaining quality data is a resource-intensive challenge.

Given the nature of watchlist data, you must start with a smart watchlist database that is well structured, normalized, and regularly updated to catch true positives while minimizing false positives. This data should be coupled with an intelligent screening engine to help filter, sort and analyze search results.

In the event a firm wants to eliminate the manual effort of clearing false positives and speed up onboarding, they must move from screening to decisioning systems. These smart systems use AI to automatically clear false positives and deliver pass/fail decisions so your organization can speed up its customer onboarding.

The Ability to Conduct Enhanced Due Diligence

Once you have successfully identified a PEP or RCA and have defined the level of risk the individual brings, additional research and analysis may be appropriate. You may want to validate, for example, the individual’s source of funds and/or wealth. The bulk of this added investigation will often include non-watchlist data, such as adverse media screening.

Accounts with a PEP relationship are also subject to enhanced monitoring and periodic review. This helps to ensure that due diligence information remains current and the risk assessment and associated controls remain appropriate.

Automated Processes

Sanction screening still requires a significant amount of manual intervention that can dramatically slow down and disrupt the PEP verification and onboarding processes. False positives in particular will hold your workflow hostage. Most sanction screening systems still yield 5-7% false positives that must be cleared manually by a compliance officer.

Advanced sanction screening solutions leverage latest technologies, such as AI, machine learning, and NLP to significantly reduce such manual intervention. They also go beyond the matching, sorting, and analysis of watchlist data results. Smart solutions, like our DecisionIQ^TM, can render 95% of all verification decisions automatically in under a minute with a low decision error rate.

Each screening decision is accompanied with a transparent ‘Proof of Decision’ that is electronically stored. The Proof of Decision can be electronically reviewed and annotated by a compliance officer. When a PEP or RCA is identified, they can be assigned to different customer lists based on the risk level. These customer lists can then be set up for scheduled or active ongoing monitoring and alerts. All this can be done via an easy to access online user interface, or via an easy to integrate API library.

Just imagine what your institution could do with all the time, money and other resources that would be freed up under such a system! Decision-making would be a lot more accurate, too.

The point is PEP data and screening solutions are evolving, just as the regulatory environment and even the world itself shifts and transforms. When you are in search of AML compliance tools of any kind, you need to partner with an AML compliance vendor that understands the winds of change. This knowledge should be worked into their collection of solutions. In the financial services sector, such partnerships are what it takes to stay in the game.

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In the previous article of the KYC 2020 Guide to PEP Screening, we learned about the many uncertainties surrounding PEPs, including how to even define them. In this article, we are going to take a closer look at PEP risk.

The Challenge of Serving PEPs

In Ontario, Sanjay Madan, a Ministry of Education computer specialist was recently fired after an alleged fraud involving $11.6 million in COVID-19 funds. Madan is also being accused of running an elaborate kickback scheme that stole at least $30 million more from the Canadian government.

Meanwhile, in the US, Tennessee state senator Katrina Robinson was recently charged with theft and embezzlement. According to court records, Robinson stole more than $600,000 in federal funds while serving as the director of a health care company. She then used this money to pay for her wedding, a new vehicle for her daughter, entertainment and various other personal expenses.

In the US and Canada and throughout the world, there are countless high-profile examples of PEPs using financial institutions as conduits for their illegal activities.

For this reason, PEPs and their relatives and close associates (RCAs) generally present a higher level of risk to your financial institution. When people are in positions of influence and power and have access to insider information, there is a greater chance that they will abuse their privileges. PEPs can end up leveraging their status for personal gain through bribery, money laundering, and corruption instead of benefiting those they are meant to serve.

PEP corruption undermines the rule of law. If left unchecked, corrupt PEP behavior can be detrimental to the overall health of the economy. It can drain a state or a country’s assets, compromise judicial and governmental systems, hurt fair commerce, and extinguish open competition.

It also can spell the end of your financial institution. Banks, credit unions and money services businesses (MSBs) that conduct business with dishonest PEPs face substantial reputational risk, additional regulatory scrutiny, and potential regulatory action.

Mitigating PEP Risk Through Red Flags

In the context of KYC and BSA compliance, identifying a client as a PEP or RCA is not the goal in and of itself. A PEP designation also does not mean the individual is necessarily involved in corruption or other forms of criminal activity. It is merely a call for extra vigilance. PEP screening and monitoring is thus one piece of a bigger process to discover and assess client risk.

The truth is that not all PEPs present the same level of risk. This risk will vary depending on numerous factors, including: the PEP’s geographic location, industry, sector, and position as well as the level or nature of the authority invested to this individual. Other factors, such as the type of account and its purpose, the transactional activity, and the complexity of the account, can all influence the risk to your institution when servicing a particular PEP.

PEP screening is a standard part of a risk-based approach to AML compliance. Though this process is usually performed as part of KYC and on-boarding procedures, account monitoring should continue periodically throughout the customer relationship. After determining that a customer is a PEP, your institution is responsible for conducting ongoing due diligence that is specifically tied to the individual’s current PEP status.

If your organization needs to assess PEP risk, then there are several red flags and indicators you can use to detect suspicious behavior and potential financial abuse.

PEPs that exhibit the following behavior or characteristics, should be approached with caution:

• Attempting to shield identity and obscure ownership through the use of corporate vehicles, intermediaries, or family members/associates as legal owners
• Exhibiting odd or suspicious behavior, for example: showing difficulty discussing the source of funding or wealth, providing incomplete or inaccurate information, asking questions about your institution’s AML/CFT policy
• Being involved with an industry or sector that is high-risk, such as arms trade and defense, banking and finance, businesses that sell or work with the government, healthcare, and mining
• Having a position in or an involvement with a business or organization that is operating in a high-risk sector
• Having a high-ranking position with significant authority, such as control over or access to state assets and funds, policies, and operations, as well as control over regulatory permissions like being able to approve an operating license
• Conducting suspicious transactional activities, such as payments and transactions that are anonymous

When on-boarding a client who possesses one or more red flags such as these, you need to proceed with caution. Your AML/CFT systems and procedures should kick in to protect your institution against unscrupulous individuals while verifying legitimate transactional activity and creating a positive customer experience.

To make this possible, you first need to be able to detect and identify PEPs and RCAs. This has traditionally been an expensive and time-consuming undertaking that greatly slows down customer onboarding. As a result, compliance for PEP screening has been lax and irregular across the financial markets. Now, thanks to AI and other advancements in regulation technology (RegTech), screening for PEPs in global watchlists, government sources, and in adverse media is not only effective and fast, but also affordable.

We’ll address the solutions helping to mitigate PEP risk in the next article.

As credit unions continue to grapple with a growing mountain of costly and complex financial regulations as well as an evolving digital ecosystem, they have been ramping up their investment in regulatory technology (RegTech) solutions.

Determining which AML compliance solutions make the most sense for a specific credit union, however, can be a challenge. Over the next four years, the global RegTech industry is expected to reach $16 billion. While there are many established players already in the market, this growth is in large part driven by a rising wave of new RegTech innovators. These companies are harnessing advanced technologies to address a wide range of issues in addition to AML compliance, such as data and network security, user experience, the increasing demand for faster digital transactions, affordability and ease of integration.

RegTech has become mission critical among credit unions seeking support in their AML/CFT compliance efforts. Earlier in this series, we examined the ways in which modern Credit Unions need to rethink their approach to AML/CFT compliance as well as the unique challenges they will face in the process. The distinct path awaiting Credit Unions towards an accurate, cost-effective, and nimble AML compliance regime requires that they look beyond many of the established vendors.

Credit unions do not operate in the same way as banks do. They have their own unique structure, mission, and set of needs. RegTech platforms that prioritize affordability, accuracy, customization, support, and ease of use are better suited to these financial institutions.

Heading into the future, partnerships with RegTech vendors can help to make credit unions more adaptable and competitive in a changing financial services landscape. Many small and mid-sized credit unions today face two options: either adapt to changes in regulation, technology, and financial market trends or be absorbed by a bigger financial institution. Though the Covid-19 pandemic initially slowed down M&A activity among credit unions, all signs indicate that M&As will increase in 2021.

Having smart, customized AML compliance solutions in place is a necessary growth strategy for today’s credit unions. In fact, it could end up being one of the most significant factors in the fundamental viability of the credit union model going forward.

In this final article of our series on AML compliance among credit unions, we will look at three core RegTech solutions that are putting these financial institutions back on the financial services playing field.

3 Smart AML Compliance Solutions for Today’s Credit Unions

A solid AML/CFT credit union compliance program will depend on some combination of three core services:

Moving from Big Data to Smart Data

AML compliance begins with screening clients as part of customer due diligence (CDD). A good sanction, PEP, and adverse media screening program begins with great watchlist data.

Most AML compliance data vendors build and promote BIG DATA. However, when it comes to a given database, bigger isn’t necessarily better. The problem with big data is that it is often full of junk and bloated with duplicates. This unnecessary, incomplete, or unwanted information creates added work for AML compliance staff who must manually reconcile and clear the excessive false positives. Financial institutions typically end up over-engineering their screening systems to deal with junk data to strike a balance between an “acceptable” number of false positives with their efforts to avoid missing true positives.

The beginning of modern KYC and sanctions screenings still starts with sanctions lists, but now extends to other watchlist data, including PEP (politically exposed persons), RCA (relatives and close associates of politicians), HIO, Regulatory Authority, Negative News, and more. These lists are constantly evolving, while sanctions are becoming more complex and the number of regulatory bodies are growing. For credit unions working with limited resources, this big and constantly changing data pool must be augmented and transformed by SMART data systems prior to consumption for screening. SMART DATA leads to INTELLIGENT SCREENING. Solving the data problem is the most effective way to enhance a credit union’s AML compliance program.

For this reason, KYC2020 developed VisionIQ, the most comprehensive, advanced, and affordable watchlist database on the market with access to over 1000 watchlists and over 10,000 automatically updated news sources from more than 200 countries. The VisionIQ data advantage is not just that the data is comprehensive so that a credit union never misses a relevant true positive, but that we spend a great deal of time to make it SMART so that our clients are not dealing with endless false positives.

For a credit union to be served the right data to suit their risk profile, VisionIQ is powered by advanced technologies, such as artificial intelligence (AI) and natural language processing (NLP).

Data is structured and organized to make it easy to selectively group watchlists for different risk profiles and screening criteria. Data is de-duplicated and cross-linked across watchlists to better resolve the data into personas. Data, especially PEP and RCA data, is further augmented by name and entity recognition in relevant adverse media. Personas are also tagged with aliases, common name variants and misspellings to reduce fuzzy search errors and false positives. Plus, we have added machine learning (ML) to the mix which makes the data, and by extension the AI-based compliance system smarter over time.

Best of all, we have priced VisionIQ with smaller financial institutions in mind. Data does not have to be so expensive, and credit unions do not have to compromise quality for good data.

Smart Sanction Screening Solutions

Sanction screening is an integral part of KYC and AML/CFT compliance. Sanction screening systems are designed to protect financial institutions from high-risk customers and help to ensure the integrity of the financial services sector. But it does not stop with just sanction screening. Today, financial institutions must screen for PEP, criminal, and even negative news.

Aside from the risk of regulatory fines, credit unions must think about their reputation. Nothing will ruin a credit union’s reputation faster than being tied to a rogue member or an employee who has generated negative publicity. Where a financial crime was committed, the financial institution’s ties may be made public, creating a reputational crisis. The credit union may be deemed guilty by association, with members questioning the integrity of the credit union itself even if it has done nothing wrong.

For example, corruption among public employees, such as politicians, government officials and their associates is on the rise. As part of their customer due diligence obligations, banks and financial institutions must establish and report whether their clients are Politically Exposed Persons (PEP) and at risk of being involved in financial crimes, like money laundering, or are associated with a predicate offense as reported in the news.

A great KYC sanction screening solution for CDD begins with an exhaustive watchlists and negative news database. However, even the best compilation of data and news sources is useless without an intelligent search and scoring engine that works to minimize False Positives, while helping to ensure that True Positives are discovered.

KYC2020 SmartScan is the best in class sanction, PEP and negative news screening and scoring service that integrates automation, AI and NLP for big and small credit unions alike with flexible price plans to suite any budget.

SmartScan gives credit unions the smarts and sophistication of an enterprise sanction screening solution while avoiding all the cost and complexity. It is available as SaaS solution or OnPrem deployment. Plus, we have developed an extensive API Library for easy integration with in-house systems and workflows.

From Screening to Decisioning

Despite all the advances in technology, sanction screening still requires a significant amount of manual intervention that can dramatically slow down and disrupt the customer verification and on-boarding processes. There are generally four main hurdles to overcome:

1. False Positives. The best systems still yield 5-7% false positives that must be cleared manually by a compliance officer.
2. Record keeping. Compliance best practices require the storing and organizing of auditable documentation for minimum of 5 years. The administrative effort and resources needed to fulfill this requirement are high, costly, and can lead to expensive inaccuracies.
3. Long On-boarding Times. Sanction screening plagued by over-engineered systems, depends even more on manual interventions that are not only slower, but prone to mistakes and discrepancies. This results in a slow customer verification and on-boarding process.
4. Ongoing Monitoring. Constantly changing data means regular and recurring screening of customers. Depending on your risk-based approach, this could mean quarterly, weekly, or even daily screening. A poor screening system invariably leads to an inaccurate and ineffective batch monitoring program.

Traditional sanction screening solutions are designed to filter a set of watchlists. The search results are then manually exported for review. To stay relevant and competitive, credit unions must move from screening tools built around integrated manual workflows to stand-alone decisioning systems that automatically clear false positives to yield actionable screening decisions. All this in near real-time, with a human readable and audit friendly decision proof-of-work file, and without requiring manual effort.

Manual-centric AML compliance systems are by nature reactive. When processes are dependent on manually clearing a mountain of false positives and routinely tie up a large amount of resources, credit union leaders can feel like they are never in control.

On the other hand, a smart, automated sanction screening decisioning system can render 95% of all verification decisions automatically within a 60-second response time and a low decision error rate. For records that require manual screening, they can be sent within minutes to compliance officers, who should have all the info they need to take immediate action.

With DecisionIQ, we put credit union AML compliance teams back in the driver’s seat. We take a risk-based approach coupled with the best watchlist data and latest technologies in search, scoring, AI, NLP, and decision systems. Automated clearing dramatically speeds up on-boarding while reducing wasteful manual work. Each decision is accompanied by a rational proof-of-work file that is securely stored. All case management and AML officer reviews can be done electronically, and best of all, our highly intelligent monitoring system actively tracks watchlist data changes, automatically re-scans impacted customer records, and only send an alert when appropriate. This means no more manual redundant reprocessing of customer batch files in the name of ongoing monitoring!

No Need to Go it Alone

Credit unions do not have to figure out the best AML compliance system on their own, nor should they resort to requiring staff to multi-task between their regular roles and AML/CFT compliance efforts. Our qualified CAMS and VSkills certified specialists have experience working with credit unions and can help with a range of compliance needs, from revamping an AML system or tailoring our products to fit established workflows.

In Closing…

Smart data and automated decisioning promise to be a fundamental part of AML compliance among all financial institutions in the future. Credit unions, however, require smart AML compliance solutions that suit their unique structure, workflow, transactional needs and budget. Such systems are more than just a way for credit unions to stay AML compliant. They are necessary growth strategy that can keep them competitive in the years ahead.

Want to learn more about how KYC2020 solutions can help your financial institution? Contact us here or send us an email at sales@kyc2020.com.

KYC2020 is also not alone in the world of RegTech innovation. To find out more about other 2021 innovators, check out the RegTech100 list.

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Anti-money laundering (AML) compliance has always been a significant burden to credit unions. But as the new year gets underway, fulfilling the regulatory requirements against financial crime may be all the more daunting.

Over the past few years, consumers and a growing range of business clients have been seeking faster banking, more convenient access to their accounts and quicker funding decisions. But credit unions have been stuck treading water in the rising wave of new and existing financial regulations, while risks of default, fraud and other types of cybercrime continue to mount.

Historically, AML compliance and regulation did not apply to credit unions in the same way it has to larger banks and traditional financial institutions. However, as digital commerce goes mainstream among credit unions and they open the door to under-served, yet higher-risk businesses, recent changes to BSA/AML compliance have been shifting this dynamic.

Understanding the Challenges in Credit Union AML Compliance

In the first article of this series, we looked at the four critical pillars of AML/CFT compliance among credit unions. In this second article, we’ll now examine the five biggest obstacles that are standing in the way of an accurate and effective compliance program.

Balancing Accuracy and Speed

Regulatory authorities have been charging hefty fines to financial institutions that fail to meet all the compliance requirements of AML, KYC, and BSA. In fact in 2020 alone, financial institutions across the world have been penalized with a staggering $10.4 billion in fines and penalties.

The continual screening, monitoring, recording and reporting requirements is a resource-intensive business process for even the biggest financial institutions. Big banks, however, can afford to throw people and money at the process, and they can more easily absorb the financial “hiccups” along the way.

With smaller financial institutions there is a struggle to balance accuracy and speed while fulfilling ALM/CFT compliance regulations. Many credit unions still employ legacy systems that rely on a significant amount of manual activity and action.

Not only are these systems expensive to run, maintain and upgrade, but they are incredibly inefficient, too. The reliance on manual, repetitive procedures increases the chance of mistakes and omissions, and onerous reporting and auditing requirements frequently tie up employee time and attention. Moreover, senior leaders continue to invest significant time and money into cybersecurity and compliance rather than strategic initiatives and goals.

Making Informed, Smart Data-Driven Decisions

This leads us to a rather strange paradox: Many financial institutions already understand that in order to comply with anti-money laundering, fraud and data security regulations, they need the assistance of automated software. A number of credit unions have already done the research and plugged the best tools into their system. But instead of improving performance, their compliance efforts are impeded.

There are two main reasons for such an outcome. The first issue involves the synergy between automation, AI, and machine learning on one side with human decision-making, input and due diligence on the other side. Relying too heavily on manual operations opens the door to inefficiencies and inaccuracies. But, an over-reliance on the results of AML detection and risk assessment solutions, can lead to a disproportionate amount of false positives and negatives– the effects of which can be catastrophic for both the credit union and its members.

The second issue can be traced back to bad data. Global watchlist data, especially PEP, RCA, HIO, is unstructured, impractical and “ugly.” Though it’s a real challenge to aggregate and standardize, it is mis-information to think that it’s a “big data” problem. The race to bigger databases leads to lack of quality in the data itself. Credit unions would be wise to start with quality data such as VisionIQ Global Watchlist Database where the data philosophy is compliant, comprehensive, and smart versus big, getting bigger, and becoming dirtier.

For these reasons, it’s not uncommon for an AML system to generate a high proportion of false positives. We’re talking about 80 to 90 percent of alerts. If a credit union blindly acts on a suspicious activity alert and files a SAR on a client, yet the client’s transaction activity was actually legitimate, it can lead the financial institution down a rather destructive path.

The member client will now become subject to frequent review. Each review will scrutinize the customer’s financial activity. If the suspected suspicious activity happens again, the credit union could decide to close all associated accounts. Multiple SARs could also attract the interest of law enforcement and eventually lead to an investigation. An initial query by internal AML investigators can easily de-risk a client who is making legitimate transactions and save a tremendous amount of time, money and headache.

On the other hand, if credit unions are not diligent enough in monitoring transactional activity and paying attention to other red flags, they become complicit with the illegal activities of the clients in their watch. Many compliance and risk assessment monitoring systems are equipped to mitigate risk on behalf of the financial institution. Credit union leaders would do well to keep in mind that although these solutions are getting better with time, the techniques and strategies criminals are using to cover up fraudulent behavior are, too.

Working with MSBs and “High-Risk” Entities

With increasing regulatory scrutiny on large financial institutions, many high-risk clients, such as money services businesses (MSBs) and marijuana-related companies, are turning to credit unions for their banking needs.

Some of these high-risk organizations may indeed be involved in money laundering and other financial crimes. Small institutions are preferred targets for such operations since they tend to have less regulatory oversight and compliance processes in place.

Consider, for example, a large MSB. A money services client can generate significant transaction volumes that could overwhelm a small credit union. Yet to ensure compliance with the KYC and BSA regulations, a credit union must continue to assess the risks posed by this client, closely monitor activity, report any unusual behavior, and implement a range of added controls to manage any exposure to risk. This includes continuously monitoring media for relevant negative news on their customers and members.

Such AML compliance measures may often be at odds with providing a good customer experience to high-risk credit union members, but are necessary. Fast, sophisticated, affordable, and low-friction regulation technology solutions can be a boon to help balance both goals. For example, as it relates to negative news screening, our Adverse Media Check ®, relies on AI, Machine Learning, and Natural Language Processing to continuously monitor thousands of relevant global news sources to create a persona database of individuals, companies, and politically exposed persons (PEPs) for relevant negative news. But we don’t stop there, the results are then fed into a AI-based decisioning system to clear false positives and identify risk. More about this and other market solutions in our final article in this series.

Mitigating Data Security Threats

As incidents of cyberattacks continue to evolve and grow, small and mid-sized credit unions face a greater threat in a sense than the largest enterprises due to their tighter budgets and access to fewer resources. Compromised customer data typically leads to fraud, stolen identities, and more. Solving for AML compliance should not be at a cost of compromising customer data.

Only sustainable data security best practices will help credit unions stay BSA/AML compliant while keeping their data and networks safe. Specifically, credit unions must now have controls in place to protect the security and confidentiality of member records, respond to and report security breaches in a timely manner, and maintain vital data helpful in identifying the source of cyber criminal behavior.

Maintaining a Positive Customer Experience

Customer expectations for fast, seamless banking create two competing priorities for financial institutions: staying AML compliant and keeping customers happy. Slow on-boarding, false positives, and cyberattacks will certainly compromise that experience.

Credit unions are known for their customer service towards members and it is a big reason why clients would choose to bank there. According to a recent PwC report, speed, convenience, helpful employees and friendly service is important for more than 70% of banking consumers, and positive customer experiences influence 75% of customer decisions in banking. Satisfied credit union members are loyal customers who will increase the scope of their relationship and recommend their credit union to others.

In short, AML/CFT compliance has always been a complicated, resource-intensive process for credit unions, and all signs indicate that it will only get more challenging in the years ahead. But, credit unions don’t have to go through this process alone. A number of regulation technology innovators can help credit unions stay compliant, even with evolving threats, while maintaining the level of quality service they are known for. At KYC2020, we have a history of working within the credit union space. We understand the unique challenges credit unions face on a daily basis and offer local solutions.

In the last article of our AML for credit unions series, we’ll examine some of the many solutions that are giving credit unions the technological edge they need to both stay compliant and expand their operations.

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For more than 30 years, the Financial Action Task Force (FATF) has been the global standard by which many countries have wholeheartedly adopted the measure to combat Money Laundering and Terrorist Financing(ML/TF). Since the FATF first started in the late 1980’s, many countries have come a long way in adopting FATF regulations and limiting the spread of ML/TF. While the primary G37 members have strict adherences and enforcement to the FATF 40 “Recommendations”, other members follow the guidelines with more leniency. When FATF was first founded,  there were only 7 pages of the 40 “Recommendations”. A mere 7 pages!  On the other hand, there are now a staggering 127 pages printed In the recently published June 2019 version of the “Recommendations”. Let that sink in.

Similarly, the original version only mentioned “policies” once, while in the latest version these policies are mentioned 30 times. This means that new “policies” were added almost 10 times per decade, resulting in about 42 new pages per decade.  This progression is not lost on most Compliance Officers, nor is it unexpected whenever regulations come into play.  So, when one may I ask, “Why is an AML Policy so important?” The answer to the question comes down to, its required by law!

Seriously though, WHY! Apart from the legal aspect there are many more important reasons why AML policy is becoming so crucial.  To be able to combat ML/TF risk, one needs to understand the risk which can come from all types of criminal individuals and organizations.  From the lone wolf to the entrepreneurial criminal enterprises, criminals launder trillions of dollars in drugs, human trafficking, bribery and the like according to a UN study in 2011. The FATF states “The possible social and political costs of money laundering, if left unchecked or dealt with ineffectively, are serious. Organized crime can infiltrate financial institutions, acquire control of large sectors of the economy through investment, or offer bribes to public officials and indeed governments.”

For some companies it can be complicated to build and maintain an AML Policy. AML regulations are constantly being updated as seen by the FATF example over a 30+ year period. In addition, some of these businesses operate in more than one jurisdiction, and often there is more than one regulator. If you think you have what it takes to build your own AML Policy, take our CAMLO quiz and see if you have what it takes.

If not, our team at KYC2020 has set out to create a tool to help with the development and support of an AML policy.  It was built with templating in mind to have a base to start with and then customize for each entities specific lines of business.  The first version was focused on FINRA regulated entities in the US.  Now we have two more in production for Money Service Businesses (MSB) and Dealers in Virtual Currencies (DIVC) in Canada and the US. The AML Policy template mechanism is structured to allow for flexible sections and content to be tailored to the specific activities of the Regulated Entities (REs).

Once a template is chosen, the RE works through the various sections answering the questions related to the particular component of a standard AML Policy, while customizing the components and sections specifically related to their business.  Once done, the RE can request to have a certified CAMS (or equivalent) person review it and provide additional feedback or suggestions on updates and changes.

When the RE is satisfied with the results, the AML Policy can be frozen, and version control kicks in.

As with FATF and their 40+ recommendations, it is the same idea with the RE’s AML Policy.  It will be a living breathing document that expands and changes over time.   The constant transformation of the document is a good thing, it demonstrates that the REs AML regime is maturing over time to deal with new risk, regulations, and changes to the business to fight ML/TF.

Want to find out more about our AML Policy tool?  Click here and sign up for this and all the other offerings at KYC2020.

Spreadsheets for Everything

While an RA is the foundation for a good risk-based AML Regime, it is often marginalized by many non-bank financial firms.  It is done in a superficial manner, using tools that are convenient and free, but ill-suited for the job. Spreadsheets are generally the tool of choice to input, track, and manage the annual risk assessment compliance requirement. As one can imagine, this approach is rather ineffective. It lacks proper controls, is prone to human-errors, difficult to audit, share, or track revision history. Moreover, as banks enhance their KYC regimes, they require annually updated formal AML Risk Assessment documentation.  The inherent limitations of spreadsheets for RA reporting and management is obvious.

Alternatives to Spreadsheets

So then, what are the alternatives to spreadsheets? The challenge once again for most SMEs and FinTech innovators is Cost, Availability, Competence, and Convenience. In our search for alternatives, we could not find a competent RA tool that met our basic requirements, was available at low-cost, was easy to get started, and convenient to use.

When we introduced our FREE Watchlist Curator Tool, we made the following statement:

“At KYC2020, we could have invested in writing a blog and talked about why “over-screening” is an expensive problem…  Instead, we decided to have our CAMS AML specialists build a tool to help you identify what Sanction, PEP, and watchlists you should be screening based on your company’s risk profile. And we decided to make our watchlist builder available free of charge.”

KYC2020 Free AML Risk Assessment Tool

For our AML Risk Assessment Tool, we decided not to be so lazy. We not only built a tool that we could make available for FREE, but also forced our CAMLO, Joseph Iuso, CAMS, to write a Blog on the What, Why, and How’s of an AML Risk Assessment. Between the information and wisdom of Joseph’s blog and the competence and convenience of our RA tool, I believe we have got you covered. Of course, we have the standard disclaimers, and you should already know that a tool cannot replace the advice of your in-house Compliance Officer.

Get started by clicking below.

KYC2020 AML Risk Assessment Tool benefits heavily from the Federal Financial Institutions Examination Council’s (FFIEC) Risk Assessment guidelines, as well as the Conference of State Bank Supervisors (CSBS) BSA/AML Self Assessment tool.  The tool considers five (5) main categories to arrive at an aggregate risk score for your business. These are: 1) Products and Services, 2) Customers, 3) Geography, 4) Operations, and 5) Agents. For each category, you have the opportunity to specify the High, Medium, and Low Risk definitions inherent to the business. For the inherent risk definitions you then specify your controls and mitigation measures to arrive at the residual risk; the risk that still remains after application of your counter-measures. This residual risk is then scored and aggregated across all categories to complete your risk assessment.

Who said it would be Easy

Sounds complicated? That’s because it is. Even for AML specialists working on a low-risk profile business, this is a minimum 2+ hour investment of time. It is for this reason the tool has the ability to save work and complete the assessment over multiple sessions. With a little bit of study, watching our Get Started Video, and rolling up your sleeves you can get through with your risk assessment on your own and for FREE!

Need Help with your RA?

For those who need a little bit of help completing, we offer some reasonably priced paid options to speed up your risk assessment process and follow best practices. These include, access to business specific templates that can be customized to speed up the assessment, different consulting packages to help prepare, review, and/or audit your Risk Assessment by our CAMS or VSKILLS certified AML specialist.

Once again, there is no catch. The tool is no obligation and we do not take any payment information. We do hope you might take the time to see what awesome and affordable AML compliance solutions we offer, from Global Watchlist Data to Sanction Screening to AML compliance consulting services. Chat with us online now, email us, or just use the tool. Our aim is to help you reduce the cost and friction of AML compliance.