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.

One of the leading providers of innovative solutions in this field is KYC2020. With our expertise in AI and KYC AML platforms, we offer advanced software solutions that streamline AML processes, enabling organizations to stay ahead of money laundering risks.

Automation can significantly improve the accuracy and efficiency of AML compliance processes, reducing manual errors and minimizing false positives. This blog post will explore the benefits and advantages of AI-driven automation in AML compliance. Also, we will explore the transformative impact of AI-driven automation on AML compliance and how KYC2020’s solutions can help businesses navigate the complex landscape of financial crime prevention.

Understanding AML Compliance

Anti-Money Laundering compliance refers to the policies, procedures, and systems implemented by financial institutions to prevent money laundering and prevent criminal activities. AML Compliance is crucial in the global fight against financial crimes by detecting and deterring unlawful activities like money laundering, terrorist financing, and fraud.

Organizations face numerous challenges in ensuring KYC/AML compliance. The increasing volume of data poses a significant obstacle, as manually processing vast amounts of information becomes time-consuming and prone to errors. Additionally, regulatory complexities add another layer of difficulty as financial institutions must navigate through a web of ever-changing regulations and compliance requirements. Moreover, timely and accurate risk assessments are crucial, as identifying high-risk customers and transactions is essential for effective AML compliance.

In the face of these challenges, leveraging AI-driven automation can provide significant advantages in streamlining AML compliance processes, enhancing efficiency, and improving the accuracy of risk assessments.

Rise of AI in AML Compliance

AI has emerged as a powerful tool in transforming AML compliance processes, revolutionizing how financial institutions detect and prevent money laundering. AML integrated with AI is well suited to control advanced capabilities such as natural language processing, machine learning, and pattern recognition

By analyzing vast amounts of data quickly and accurately, AI can identify complex patterns and anomalies that may indicate suspicious activities. It can efficiently monitor customer behavior, transactional patterns, and financial networks, providing real-time insights into potential risks. Furthermore, AI-powered systems can adapt and improve over time, learning from new data and evolving money laundering techniques, ensuring enhanced detection capabilities.

KYC2020 has been at the forefront of leveraging AI technology for AML compliance solutions. Our innovative KYC compliance software harnesses AI’s capabilities to streamline compliance processes, enabling organizations to efficiently manage risk, reduce false positives, and stay compliant with regulations. Our pioneering efforts demonstrate the transformative impact of AI in combating financial crimes and revolutionizing AML compliance.

Benefits of Automating AML Compliance with AI

The benefits of automating AML compliance with AI are manifold. By embracing AI-driven automation, organizations can strengthen their AML programs, stay ahead of evolving financial crimes, and protect their reputation and integrity.

Below are some benefits of integrating AI with AML compliance for automation:

Enhanced Accuracy and Efficiency:

Automating AML compliance with AI brings a significant boost in accuracy and efficiency. AI-powered systems can process large amounts of data in real time, reducing human error and false positives. By leveraging machine learning algorithms, AI can continuously learn and improve its detection capabilities, adapting to evolving money laundering techniques and patterns. This led to more accurate identification of suspicious activities and reduced manual effort, enabling organizations to focus their resources on high-value tasks.

Improved Risk Assessment and Detection:

AI-driven automation enhances risk assessment and detection capabilities. With advanced analytics and pattern recognition, AI can identify complex relationships, network connections, and behavioral anomalies that may indicate money laundering or other illicit activities. By analyzing customer profiles, transactional data, and external sources, AI can generate risk scores and alerts, allowing institutions to prioritize investigations and allocate resources effectively. Real-time monitoring and alerts enable timely intervention, minimizing potential risks and facilitating proactive compliance measures.

Cost and Resource Optimization:

Automating AML compliance with AI leads to cost and resource optimization. By reducing manual processes, organizations can allocate their workforce to higher-value tasks, such as conducting in-depth investigations and strategic decision-making. Additionally, AI-powered systems can efficiently handle large volumes of data, eliminating the need for additional staffing and reducing operational costs. Automated processes also ensure faster response times, enabling organizations to meet regulatory deadlines and minimize compliance-related penalties.

Overcoming Challenges and Implementation Considerations

Implementing AI-driven AML compliance solutions comes with its own challenges for organizations. Ensuring data quality and integrity is crucial, as AI models heavily rely on accurate and reliable data. Addressing concerns related to data privacy and maintaining explainability in AI algorithms is also essential to gain trust and regulatory compliance.

To achieve successful implementation, organizations should focus on the following best practices:

• Data preparation plays a vital role in ensuring the quality and relevance of input data for AI models.
• Staff training is key to equipping employees with the necessary skills to understand and work effectively with AI technologies.
• Establishing transparent AI governance frameworks promotes AI’s responsible and ethical use in compliance processes.

Moreover, organizations should consider implementing AI as a part of a broader AML compliance strategy, integrating it seamlessly with existing systems and workflows. Regular monitoring and validation of AI models are essential to ensure ongoing accuracy and performance. Collaborating with experts and industry leaders at KYC2020 can provide valuable guidance and support throughout the implementation process.

Conclusion

Automating AML compliance with AI brings numerous advantages to financial institutions in the global fight against financial crimes. This blog has highlighted the importance of AI-driven automation, emphasizing its enhanced accuracy, efficiency, and risk assessment. Organizations can streamline compliance processes, improve detection capabilities, and optimize resource allocation by adopting AI-powered solutions.

KYC2020, a leading provider in this field, offers innovative AI solutions tailored for KYC/AML compliance needs. It is crucial for organizations to explore the transformative potential of automation and leverage the capabilities of AI to stay ahead of money laundering risks. Embrace the power of AI-driven automation and partner with KYC2020 to strengthen your AML compliance efforts. Implementing AI technology today is the first step towards efficient and effective AML compliance.

We will uncover ongoing monitoring enhances the detection of hidden risks, leverages technology, and promotes collaboration, ultimately ensuring a more robust and resilient AML compliance framework.

Enhancing AML Compliance Efforts through Continuous Vigilance

The AML compliance program is an ongoing process requiring continuous vigilance to combat money laundering activities effectively. Ongoing monitoring is a critical tool in this regard, enabling financial institutions to detect and mitigate potential risks. Its ongoing monitoring casts a wider net, primarily on individual data, ensuring that suspicious activities and patterns are identified over time.

Uncovering Hidden Risks: The Power of Ongoing Monitoring

Ongoing monitoring is pivotal in uncovering hidden risks that may otherwise go undetected. Financial institutions can gain deeper insights into potential money laundering activities by analyzing customer behavior and account activity over an extended period. This proactive approach helps identify anomalies, unusual trends, and suspicious relationships that might indicate illicit financial behavior. It empowers institutions to take timely action, preventing the further proliferation of money laundering schemes.

Leveraging Technology for Effective Ongoing Monitoring

The effective implementation of ongoing monitoring relies heavily on technology-driven solutions. Advanced analytics, artificial intelligence, and machine learning algorithms can help financial institutions sift through vast amounts of data, identifying patterns and anomalies that might signal money laundering activities. These technologies enable the automation of monitoring processes, enhancing efficiency and reducing the risk of human error. By harnessing the power of technology, institutions can stay ahead of increasingly sophisticated money laundering techniques.

The Importance of Collaboration and Information Sharing

Ongoing monitoring is not limited to an individual institution’s efforts but extends to collaboration and information sharing among financial institutions and regulatory bodies. Through sharing information on suspicious activities, emerging risks, and new typologies, institutions can collectively strengthen their AML compliance efforts. Collaboration facilitates the identification of broader trends and helps develop more robust risk mitigation strategies, ultimately contributing to a stronger and more resilient financial system.

Strengthening Compliance Programs: The Benefits of Ongoing Monitoring

Implementing robust ongoing monitoring practices not only ensures regulatory compliance but also brings several benefits to financial institutions. Below we are mentioning some of the benefits:

• It enables institutions to protect their reputation by minimizing the risk of involvement in money laundering activities. By actively monitoring customer behavior, institutions can identify and address potential risks, thereby safeguarding their integrity and preserving the trust of their customers and stakeholders.
• Ongoing monitoring contributes to cost-effectiveness in AML compliance efforts. Detecting and mitigating risks early can prevent financial losses, regulatory penalties, and reputational damage associated with non-compliance. By proactively identifying and addressing potential money laundering activities, institutions can avoid the financial implications of fines and legal consequences.
• Ongoing monitoring enhances the overall effectiveness of AML compliance programs. It enables institutions to refine their risk assessments, adjust monitoring strategies, and improve the accuracy of their suspicious activity reporting. The insights gained from ongoing monitoring help institutions stay one step ahead of money launderers, adapting to new and evolving techniques.

The Regulatory Landscape: Ongoing Monitoring as a Requirement

Regulatory bodies worldwide are placing increased emphasis on the importance of ongoing monitoring in AML compliance. Financial institutions are obligated to implement robust monitoring programs. These regulatory requirements acknowledge the necessity of continuous vigilance in the fight against money laundering.

Regulators expect financial institutions to demonstrate a proactive and risk-based approach to ongoing monitoring. Integrating ongoing monitoring into compliance programs is essential to meet regulatory expectations and ensure adherence to AML laws and regulations.

Non-compliance with regulatory requirements can have severe consequences for financial institutions. Apart from financial penalties, institutions may face reputational damage, loss of customer trust, and even legal repercussions. Therefore, incorporating ongoing monitoring practices into AML compliance programs is not just a regulatory obligation but a fundamental requirement for financial institutions’ long-term sustainability and success.

How KYC2020 Can Help Implementing Ongoing Monitoring of AML

KYC2020 is a trusted partner that helps financial institutions achieve and maintain compliance through its comprehensive ongoing monitoring solutions.

We provide financial institutions with robust and scalable technology enabling real-time activity monitoring. By leveraging SanctionWatch, an advanced KYC AML compliance tool, the institutions can quickly analyze large volumes of data, identify suspicious patterns, and detect potential money laundering activities in real-time. This proactive approach ensures that financial institutions can promptly respond to and mitigate potential risks, helping them comply with evolving AML regulations.

SanctionWatch is designed to minimize false positives, which can burden compliance teams and hinder operational efficiency. It also offers robust data integration capabilities. It aggregates data from multiple sources, including international sanctions lists and adverse media, providing a comprehensive view of customer risk profiles. By harnessing this broad range of data, financial institutions can enhance their monitoring efforts, ensuring they capture all relevant information and minimize the risk of missing critical red flags.

Furthermore, Staying current with regulatory requirements is crucial to maintaining compliance and avoiding penalties or reputational damage. SanctionWatch is continuously updated with the latest regulatory changes and additions to sanctions lists, ensuring that financial institutions are always up to date with the evolving compliance landscape. 

Hence, by partnering with KYC2020, financial institutions can benefit from continuous support and ensure their ongoing monitoring practices align with changing regulatory landscapes.

Conclusion

Ongoing monitoring plays a vital role in AML compliance, serving as a proactive tool to identify and mitigate risks associated with money laundering. By continuously monitoring customer behavior, transactional patterns, and account activity, financial institutions can uncover hidden risks and take timely action. Leveraging technology and promoting collaboration among institutions are crucial factors in maximizing the effectiveness of ongoing monitoring. As financial systems continue to evolve, ongoing monitoring will remain an indispensable component of AML compliance, safeguarding the integrity of the global financial system.

In an effort to increase loans and deposits and expand their customer base, smaller financial institutions and credit unions have been considering whether or not to work with cannabis-related businesses. It’s a decision that should not be taken lightly. Servicing this customer pool will in some cases greatly increase your institution’s risk profile. It also entails significantly more scrutiny around Anti-Money Laundering/Bank Secrecy Act (AML/BSA) compliance.

Whether or not you decide to bank a cannabis-related entity, before your institution can even open its doors to this emerging market, there are two elements of your AML compliance regime in particular that you need to focus on:

1. A thorough risk assessment
2. A rock-solid onboarding process from start to finish

Much regulatory, legal and reputational damage can be averted right from the beginning when you have access to intelligent data, can automate screening processes, and can combine this with good KYC policies and systems.

A Brief Overview of the Cannabis Business Industry

One of the biggest challenges of cannabis business banking is that the Cannabis industry itself as well as the rules that guide it are incredibly diverse and extensive. For starters, cannabis products actually fall into one of three categories: hemp, Cannabidiol (CBD) oil, and marijuana. Each one of these categories is treated differently from a legal and regulatory standpoint. Complicating matters, there are also differences in the rules and regulations from one state to another.

Here is a brief rundown of each type of Cannabis product, including its current legal and regulatory standing:

Hemp

Hemp is a variety of the Cannabis sativa plant species that is grown specifically for commercial use. Hemp produces a broad range of cannabinoids, including tetrahydrocannabinol (THC), the intoxicating chemical in marijuana. Hemp, however, does not contain enough THC to produce intoxicating effects.

Many countries differentiate hemp from marijuana by the amount of THC produced by the plant. In the US, industrial hemp must not contain more than 0.3% THC.

Hemp has a variety of uses. Hemp fibers can be used to make an assortment of products including paper, clothing, textiles, rope, building materials and compost. Hemp seeds and the oil produced from them, on the other hand, can be consumed as healthy, nutrient-rich, super food.

CBD Oil

CBD oil comes from the flower and leaves of the hemp plant. It is not the same as hemp seed oil, which comes from the seed and contains little to no CBD. Over the past few years, there has been a growing demand for CBD oil as a wellness supplement to such an extent it is a sub-industry all to itself.

Both hemp and products containing CBD oil are treated the same from a legal and regulatory standpoint. CBD is also subject to Food and Drug Administration (FDA) oversight with respect to how products containing CBD can be advertised.

According to the 2018 Farm Bill, growing, processing and selling hemp and hemp derivatives is legal at the federal level, and so is transporting hemp across state lines.

There are, however, many restrictions placed on such activities. For instance, all growers, processors and dealers must be approved, registered and/or licensed by the U.S. Department of Agriculture (USDA). Hemp that is not produced in accordance with the 2018 Farm Bill is designated as illegal marijuana.

For financial institutions in particular, the Financial Crimes Enforcement Network (FinCEN) recently issued their guidance regarding due diligence requirements under the Bank Secrecy Act (BSA) for hemp-related business clients.

Marijuana

Marijuana is the dried leaves and flowering tops of either the cannabis sativa or cannabis indica plant. Since marijuana contains a significant amount of the psychoactive substance THC, it is still considered a Schedule 1 substance under federal law. Many states, however, have legalized the cultivation and selling of medical and/or recreational marijuana.

This odd contradiction between federal and state law has brought about two indirect, yet consequential results:

1. Marijuana is primarily a cash-based industry since most payment networks will not process marijuana-related transactions.
2. Many big marijuana companies rely on the investments of foreign individuals and entities.

Marijuana-related businesses (MRBs) can also be classified into one of two tiers:

1. Tier I MRB- A company that is directly involved in cultivating or dispensing marijuana
2. Tier II MRB- A company that supports Tier I MRBs, such as hydroponic suppliers, packaging suppliers, licensing consultants, or marijuana software providers

Although cannabis business banking may be challenging in the current environment, it represents a potentially lucrative emerging industry for financial institutions. With total sales of cannabis in the states where it is legal expected to reach nearly $30 billion by 2025, a growing number of cannabis-related businesses are seeking access to mainstream financial products.

Moreover, cannabis products derived from Hemp and CBD are both federally legal and considerably less taboo. This means that institutions looking for untapped markets, could get their foot in the door with less risk and uncertainty.

With MRBs, currently 36 states have legalized the distribution of medicinal and/or recreational marijuana. The largest draw for both banks and credit unions in this high-risk market is the vast untapped potential. Like any business, MRBs need access to financial services in order to survive and expand. The MRB industry, however, is expected to reach a staggering $57 billion by 2027, yet a majority of MRBs (70 percent in total) remain unbanked or under-banked.

Understanding Cannabis Business Banking Risk

Whether or not your financial institution chooses to bank hemp, CBD, or MRB customers, your will be making a risk-based business decision. There are three main areas of risk to consider right from the start:

The Risk Posed by the Cannabis Business

As mentioned above, the risks associated with banking hemp companies versus CBD companies versus MRBs all differ tremendously. Not only would you potentially need to stay updated with the current legal and regulatory status of hemp, CBD and marijuana businesses across several different regions, but there are serious know your customer (KYC) risks, too.

The big risk in banking hemp or CBD companies is that either:

• The hemp involved is actually marijuana
• The hemp or CBD company has not followed all legal requirements, such as having the correct licenses and registrations, or it is growing the hemp in a tract of land that has not been approved.

Support services provided by third parties to cannabis businesses, such as hemp testing facilities, may also put financial institutions at risk for money laundering schemes. These types of companies, however, generally do not pose the same amount of risk as direct cannabis businesses, such as farmers, processors, and dealers.

With cash-oriented MRBs, financial institutions face the real risk of legal action for money laundering and aiding and abetting in drug trafficking. But, that’s not all. Some legal MRBs are committing fraud or opening up shell companies just to gain access to the services of a financial institution. For example, an MRB may list itself as a “pharmacy”, “nursery” or “wellness center” in order to get through the onboarding process.

Risk Posed by the Beneficial Owner

Aside from the typical risk associated with politically exposed persons (PEP) or those with a criminal background, one of the biggest risks surrounding the beneficial owners of a cannabis-related business is figuring out who they are in the first place. The challenge is even greater when determining the true beneficial ownership of marijuana companies due to the prevalence of foreign investment and the prolific use of management agreements and shell companies in the industry.

Risk Posed by the Financial Product

The final area of risk to consider is that posed by the desired financial product(s). The risk to offer a deposit account differs greatly from the risk of providing a loan. Once you’ve determined the risk of the cannabis business itself and of the beneficial owner, making this decision depends on your institution’s risk appetite.

Mitigating the High Risk of Cannabis Banking from the Beginning

A great deal of mitigating the risk of cannabis business banking happens at the beginning, before a client has completed the onboarding process. There are two key steps and areas of focus your financial institution should be concerned about:

Conducting a Risk Assessment

After becoming familiar with the relevant laws and regulations across your institution’s entire footprint, you should conduct a cannabis-risk assessment and set your institution’s risk appetite for the industry. The risk assessment process with the aid of AML Compliance tools and consulting will provide an opportunity for your financial institution to see if cannabis business banking products and services truly fall within the scope of its strategy and risk appetite.

A significant part of your institution’s risk management starts with good KYC practices and systems. Thus, the risk assessment process should confirm whether there is adequate infrastructure and support in place in your AML compliance program. For instance, how is your institution scanning watchlist data, which alternative data sources will you be consulting, and how will matches be sorted and verified?

When evaluating any higher risk activities, the legal and regulatory impact should also be considered in terms of both AML/BSA compliance and potential exposure to litigation.

Developing a Thorough Onboarding Process

The Onboarding process starts with the onboarding request, followed by data capture and initial CDD/CIP, and then enhanced due diligence (EDD) for KYC. This is where much of your risk management and AML compliance happens in practice.

After conducting a risk assessment, you then need to set the risk-rating criteria and processes for risk-rating new customers. Here are a few important considerations:

• Use a comprehensive application and onboarding questionnaire to understand the nature of the business and its financial activity and to secure the necessary documentation, such as the business’ registration and license information. Because banking hemp or CBD is different from marijuana businesses, it’s good practice to have different onboarding questionnaires for each of the three types of cannabis products.

• Invest in automated onboarding technology powered by artificial intelligence and machine learning. These technologies can help your institution conduct enhanced due diligence and deep web searches for automating EDD. You’ll have better insight into the background of each business and its beneficial owners. Moreover, automated platforms such as our own DecisionIQ will not only screen for sanctions, PEP, criminal records, negative news media and more, but create an intelligent scoring engine that reduces manual processes and False Positives.

• Developing an onboarding process that reflects your risk appetite should always be considered a work in progress that you periodically return to and adjust where needed. This reevaluation process will also help your institution conduct its ongoing monitoring for suspicious activity.

In short, banking cannabis-related companies raises the level of risk for any financial institution. But your institution can mitigate the high risk in cannabis business banking by getting your onboarding process, systems and procedures in line with your risk appetite and financial goals.

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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.

Sometimes it can feel like AML compliance procedures and regulation are getting more complex by the day. Just trying to keep up with the alphabet soup of AML terms and acronyms, not to mention the assortment of relevant regulatory bodies and organizations, is a dizzying endeavor.

So if you are confused by all the AML jargon, we have created a glossary of the most common acronyms in the world of AML/CFT compliance. This list is primarily for financial institutions and MSBs in the U.S. and Canada. However, we’ll also, give a brief rundown of some of the major regulatory bodies both within North America and abroad.

AML Terms and Abbreviations

A

Adverse Media Screening- Adverse Media screening, also known as Negative News Screening, is a process to detect and prevent sanction and reputation risk of dealing with persons or entities with associations with financial crime or other relevant negative news. It is conducted via smart tools built on AI and NLP to scan through thousands of global news sources.

AML- Anti-Money Laundering

AML/CTF compliance program- Anti-Money Laundering/Counter-Terrorism Financing compliance program.

A system designed to assist financial institutions, securities dealers and money services businesses in their fight against money laundering, terrorist financing, and other financial crimes. This system generally includes:

• Written internal policies, procedures and controls
• A designated AML compliance officer or team
• Third-party audits
• Comprehensive employee training

AML Compliance Officer- Responsible for the implementation and maintenance of an organization’s AML/CFT compliance program.

AMLA- Anti-Money Laundering Act of 2020

AMLID- Anti-Money Laundering International Database

A compendium of analyses of anti-money laundering laws and regulations, including both domestic laws and international cooperation and information on national contacts and authorities.

ARS- Alternative Remittance System

Any system used for transferring money from one location to another while operating outside of traditional banking channels.

As Soon as Practicable (Canada)- The expected time within which a suspicious transaction report (STR) can be submitted to FINTRAC taking into account the facts and circumstances of the situation.

ASTR (Canada)- Attempted Suspicious Transaction Report

A report that is filed when an individual initiates and then abandons a financial transaction in a way that looks suspicious.

AST- Automated Screening Tool

A sanctions, PEP, and adverse media screening tool powered by AI, natural language processing and machine learning with automated search functionality. ASTs, like our very own SmartScan, offers intelligent search and a scoring engine that works to minimize manual intervention while significantly reducing False Positives.

ATF- Anti-Terrorist Financing

The same thing as Counter Terrorist Financing (CFT) and is often used interchangeably

B

Batch Processing- A type of data processing where related transactions or entries are grouped together and transmitted for structuring and analysis.

Batch Screening- The process of screening an institution’s customer base and other associated entities on a periodic basis. Batch processing, along with API based screening are necessary requirements for modern sanction and PEP screening tools for AML compliance.

Beneficiary- The individual or entity who will ultimately benefit from a transaction and be the final recipient of the funds.

BO- Beneficial Ownership

Beneficial ownership is a term in commercial law which refers to the natural person or persons “who ultimately own or control a legal entity or arrangement, such as a company, a trust, or a foundation”.

BSA- The Bank Secrecy Act

U.S. legislation aimed toward preventing criminals from using financial institutions to hide or launder money. The law requires financial institutions to provide documentation to regulators whenever their clients deal with suspicious cash transactions involving sums over $10,000.

Business Relationship (Canada)- The connection between a reporting entity and a client to conduct financial transactions or provide services related to those transactions. This relationship can be account-based, where the client holds an account with the reporting institution, or non-account-based, but recurring.

C

CAMLO (Canada)- Chief Anti-Money Laundering Officer

Responsible for maintaining and managing records of high risk customers and reporting suspicious activities to the authorities as well as organizing inspections and audits from third-party entities.

CAMS- Certified Anti-Money Laundering Specialist Certification designation from ACAMS.

Cash-Intensive Business- Any business in which customers usually pay with cash for the products or services provided, such as food delivery services and coin-operated machines.

CDD- Customer Due Diligence

The process of assessing customers’ background to determine their identity and the level of risk they possess.

CFT- Counter-Terrorism Financing

CFCS- Certified Financial Crime Specialist designation from ACFCS.

Check Kiting- A form of check fraud that involves taking advantage of the float to make use of non-existent funds in a checking or other bank account. In this way, instead of being used as a negotiable instrument, checks are misused as a form of unauthorized credit.

CIP- Customer Identification Program

Where financial institutions need to verify the identity of individuals wishing to conduct financial transactions with them and is a provision of the USA Patriot Act.

Clarification Request (Canada)- A method used to communicate with money services businesses when FINTRAC needs more information about their registration form. This request is usually sent by email.

Compliance Program (Canada)- All elements a reporting entity is legally required to have under the PCMLTFA and associated regulations, to ensure that the organization meets all reporting, record keeping, client identification, and know-your-client requirements.

CRO- Chief Risk Officer

An executive in charge of managing risks to the company that can be categorized into three groups: technical, regulatory, and competitive.

CRS- Common Reporting Standard

The global standard for the automatic exchange of financial account information

CTR- Currency Transaction Reporting

A bank form used in the US to help prevent instances of money laundering. This is a report that U.S. financial institutions are required to file with FinCEN for each deposit, withdrawal, exchange of currency, or other payment or transfer, by, through, or to the financial institution which involves a transaction in currency of more than $10,000.

Currency Smuggling- The illicit movement of large quantities of cash across borders, often into countries without strict banking secrecy, poor exchange controls or poor anti-money laundering legislation.

CVC- Convertible Virtual Currencies

An unregulated digital currency that can be used as a substitute for real and legally recognized currency as defined by the US regulator FinCen in a guidance release May 9, 2020

D

Delisting- When a sanctions target is removed from a sanctions list after the restrictions imposed on them have been lifted

Designated Categories of Offense- Crimes the FATF considers to be money-laundering predicate offenses.

DNFBP- Designated Non-Financial Businesses & Professions

Non-financial businesses and professions designated by the FATF that require extra vigilance, including: casinos, real estate agents, dealers in precious metals, dealers in precious stones, lawyers, notaries, other independent legal professionals and accountants, trusts and company service providers.

DPL- Denied Persons List

A list, published by the Bureau of Industry and Security (BIS), of individuals, entities, or companies that have been denied export privileges, generally due to Export Administration Act violations.

E

EAR- Export Administration Regulations

A set of regulations administered and enforced by the Bureau of Industry and Security, that specifically apply to physical goods or commodities subject to export controls.

EDD- Enhanced Due Diligence

A KYC process that involves the enhanced review of high-risk individuals or companies.

Effectiveness Review- An evaluation conducted at a minimum of every 24 months to test the effectiveness of an organization’s AML compliance program: policies and procedures, risk assessment and ongoing training program. KYC2020 AML Compliance Toolkit can help you manage your Effectiveness Review for FREE.

EIDV- Electronic Identification and Verification

The verification of a person’s identity remotely through digital means

Electronic Funds Transfer Report (Canada)- A report submitted to FINTRAC upon a transmission of instructions for the transfer of CAD 10,000 or more out of or into Canada in a single transaction or in two or more transactions totaling CAD 10,000 or more made within 24 consecutive hours by or on behalf of the same individual or entity.

Embezzlement- The act of taking or misappropriating funds entrusted by an employer or organization for illegal personal use.

Event-Triggered Monitoring- An internal control used to mitigate sanctions risks. Whenever relevant information about an existing customer changes, an interim review of information is conducted prior to a full scheduled review.

Exclusions List- A list of names or entries that are to be excluded from the sanctions screening process.

F

F2R- A secure application accessed over the internet that lets reporting entities manually enter and correct reports, such as STRs and LCTRs, individually (Canada)

False Negative- A hit that is flagged during the screening process as a possible alert, but is subsequently dismissed, yet there is actually a missed match on a sanctions list, or screening activity that is inappropriately calibrated in such a way that the suspicious activity itself is missed.  AI based decision systems can help eliminate false positives and speed up customer onboarding and ongoing monitoring.

False Positive- A hit flagged during the screening process as a possible alert, but upon review is found to not be a match to a target named on a sanctions list.

FATCA- The Foreign Account Tax Compliance Act

Passed as part of the HIRE Act, generally requires that foreign financial Institutions and certain other non-financial foreign entities report on the foreign assets held by their U.S. account holders or be subject to payment withholding.

FBAR- Foreign Bank and Financial Accounts

Under the Bank Secrecy Act, foreign financial accounts, such as bank accounts, brokerage accounts and mutual funds, must be reported to the Treasury Department and reporting institutions must keep certain records of those accounts.

FCCR- Financial Crime Country Risk

FFI- Foreign Financial Institution

A Foreign Financial Institution that has registered and agreed to adhere to the requirements set forth by FATCA.

FMSB- Foreign money services business (Canada)

A money services business, that operates outside of Canada, that does not operate a physical location in Canada, yet directs MSB services to persons or entities in Canada.

G

GRC- Governance, Risk & Compliance

An organization’s approach across these three practices: governance, risk management, and compliance

Gatekeepers- Professionals such as lawyers, notaries, accountants, investment advisors, and trust and company service providers who assist in transactions involving the movement of money, and are deemed to have a particular role in identifying, preventing and reporting money laundering.

Greylist- A list of entities that present a higher risk for causing a negative impact to an organization.

H

HIO (Canada)- Head of an International Organization

HNWI- High-net-worth individual

A person with more than US$1 million in liquid assets

I

IBC- International Business Company

An offshore company that is registered in a tax neutral setting

ID&V- Identification & Verification

Inherent Risk- The level of sanctions risks along four categories: customers, products and services, countries, and delivery channels. Inherent risk examines the likelihood and impact of noncompliance prior to considering any controls within the risk management processes.

Internal Evasion- When an organization’s employee commit or facilitate a sanctions violation.

IVTS- Informal Value Transfer System

Any system, mechanism, or network of individuals that receives money for the purpose of making the funds or an equivalent value payable to a third party in another geographic location.

K

KYC- Know Your Customer

AML policies and procedures a financial services institution or MSB must act on to verify the identity, suitability, and risks involved with maintaining a business relationship with a particular customer.

KYE- Know Your Employee

AML policies and procedures for acquiring a better knowledge and understanding of an organization’s employees for the purpose of detecting conflicts of interests, money laundering, past criminal activity and suspicious activity.

KYM- Know Your Member

Common in the Credit Union space where customers are members of the Credit Union

L

LCT- Large Cash Transaction (Canada)

Any cash transactions valued at CAD 10,000 or more (or the equivalent in a foreign currency) that take place within the same 24 hour period for the same customer.

LCTR- Large Cash Transaction Report (Canada)

A report that is filed with FINTRAC when a large cash transaction has taken place.

Listed Person (Canada)- Anyone on a list published in the Regulations Implementing the United Nations Resolutions on the Suppression of Terrorism issued under the United Nations Act. You can consult that list of names via the Office of the Superintendent of Financial Institutions.

A listed person includes an individual, a corporation, a trust, a partnership or fund or an unincorporated association or organization that is believed to:

• have carried out, attempted to carry out, participated in or facilitated a terrorist activity; or
• be controlled directly or indirectly by, be acting on behalf of, at the direction of, or in association with any individual or entity conducting any of the above activities.

M

Mandatory Sanctions Lists- International sanctions lists, such as those including targets designated by the United Nations Security Council Resolutions (UNSCR), which must be screened against.

Ministerial Directives (Canada)- The authority to issue directives that require reporting entities to apply measures (which this document calls “countermeasures”) to transactions originating from or destined to designated foreign jurisdictions and entities; and the authority to recommend that the Governor-in-Council issue regulations limiting or prohibiting reporting entities from entering into a financial transaction originating from or destined to designated foreign jurisdictions and entities.

MLRO- Money Laundering Reporting Officer (Canada)

The individual who manages an organization’s AML systems and acts as a focal point for related inquiries.

ML- Money Laundering

The process of taking money obtained by committing a crime and disguising the source to make it appear legitimate

Money Mule- A person who transfers money acquired illegally on behalf of others.

MSB- Money Service Business

A business that transmits or converts money, foreign exchange, remittance and/or that issues or redeems monetary instruments

N

Name Screening- The process of matching an internal record or batch of records against sanctioned list records, either manually or through an automated screening tool.

O

OFC- An offshore Financial Center

A country or jurisdiction that provides financial services to nonresidents on a scale that is incommensurate with the size and the financing of its domestic economy

Ongoing Monitoring- An AML control process that involves monitoring high-risk customers on a periodic basis based on their risk profile.

Operational Risk– The risk of direct or indirect loss of operations due to inadequate or failed internal processes, people or systems, or as a result of external events.

P

Partial Match- When an entity being screened is similar to a sanctioned entity and potentially shares identifying factors, such as date of birth. Partial matches require further human intervention to determine whether the match is a true match.

PEP- Politically Exposed Person

An individual who, through their prominent position or influence, is more susceptible to bribery or corruption and thus presents a higher risk as a customer

PEDP- Politically Exposed Domestic Person (Canada)

PEFP- Politically Exposed Foreign Person (Canada)

PIN- Purpose and Intended Nature [of a Business Relationship] (Canada)

A record that documents the purpose and intended nature of a business relationship, and includes information that would help an organization anticipate the types of transactions and activities the client may conduct.

R

Reasonable Measures (Canada)- Steps organizations must take to collect certain information but it is not mandatory, such as, asking the client, conducting open source searches, or consulting commercially available information.

Red Flag- A warning signal that should bring attention to a potentially suspicious situation, transaction or activity.

Reputational Risk- The potential that adverse publicity involving a financial institution’s business practices and associations, whether accurate or not, will cause a loss of confidence in the integrity of the institution.

Risk Appetite- The amount of risk that an organization is comfortable accepting in the pursuit of value or opportunity

Risk Assessment- A tool that allows an organization to identify and assess its potential risks and vulnerabilities.  Try the KYC2020 Risk Assessment tool for FREE

RBA- Risk-Based Approach

The process of assessing of the various risks associated with different types of businesses, clients, accounts and transactions in order to maximize the effectiveness of an AML/CFT regime.

RCA- Relative or Close Associate

Sometimes referred to as “PEPs by association,” RCAs are individuals related to or who have a close connection to a PEP, such as spouses, friends and business partners.

RGS- Reasonable Grounds to Suspect (Canada)

A suspicion of corruption that is not biased or prejudiced, but is based on an assessment of facts, context, and AML/CTF indicators

Reporting Entity- Entities regulated by the PCMLTFA that must report certain transactions to the FINTRAC, such as accountants, life insurance companies, brokers and agents, MSBs, real estate brokers and securities dealers (Canada)

S

Safe Harbor-  A legal provision to reduce or eliminate legal or regulatory liability for financial institutions, and their directors, officers and employees, if they report their suspicions in good faith to the Financial Investigation Unit (FIU).

Sanctions- Restrictive measures taken by individual countries, regimes, or coalitions with the goal of provoking a change in behavior or policy. Sanctions can restrict trade, financial transactions, diplomatic relations, and movement.

Sanctions List- A document or database listing individuals, legal entities, and countries with whom it is illegal to do business.

Sanctions Screening- Sanctions screening is a process employed within financial institutions to detect, prevent and manage sanctions risk. It is conducted via automated screening tools and manual processes.

SAR- Suspicious Activity Report

A report made by a financial institution about suspicious activity.

SCO- Sanctions Compliance Officer

The individual responsible for oversight of the sanctions compliance program. The SCO conducts ongoing monitoring for sanctions compliance, including sample testing and a review of exception reports, to enable the escalation of identified noncompliance or other issues to senior management.

SCP- Sanctions Compliance Program

A system designed to assist financial institutions in their sanctions compliance and to manage their sanctions risk.

SDD- Sanctions Due Diligence

A process that focuses on the risks specific to sanctions, while taking into account governance and risk assessment

SDN List- The Specially Designated Nationals and Blocked Persons List

A United States government sanctions/embargo measure targeting U.S.-designated terrorists, officials and beneficiaries of certain authoritarian regimes, and international criminals

SIE- Special Interest Entity

SIEs are high-risk organizations that are involved in criminal activities or are suspected of being involved in criminal activities

SIP- Special Interest Person

High risk individuals similar to PEPs, who have a history of involvement or alleged involvement in crime

Shell Bank- A foreign financial institution that does not have a physical presence in a foreign country.

Smurfing- A common money laundering method involving multiple individuals and/or multiple transactions for making cash deposits, buying monetary instruments or bank drafts in amounts under the reporting threshold

SRB or SRO- Self-Regulatory Body or Organisation (Canada)

A body that represents a profession (e.g. lawyers, notaries, other independent legal professionals or accountants), and which is made up of members from the profession and has the role of performing certain supervisory/monitoring functions of standards that are maintained by the professions.

Structuring- Illegal act of splitting cash deposits or withdrawals into smaller amounts, or purchasing monetary instruments, to stay under a currency reporting threshold.

Suspicious Activity– Unusual or questionable customer activity that may be related to a money laundering or other criminal offense, or to the financing of a terrorist activity

T

Target Match- Identification of an individual or entity as matching one named on a sanctions list, also referred to as a true match

Tax Haven- Countries that offer special tax incentives or tax avoidance to foreign investors and depositors

Terrorist Financing- The manner by which terrorists fund their operations in order to perform acts of terrorism

TMP- Transaction Monitoring and Filtering Program

A system required of financial institutions to monitor transactions after their execution for compliance with the Bank Secrecy Act and AML laws and regulations.

TBML- Trade-Based Money Laundering

A financial crime that involves hiding the proceeds of a crime by moving the funds to a specific destination via trade transactions

Third-Party Database- A database made up of data collected by an entity that does not have a direct relationship with the user. Examples of third-party databases include rating agencies, stock exchanges, and legal databases.

U

UBO- Ultimate Beneficial Owner

The person or entity that is the ultimate beneficiary when an institution initiates a transaction.

USA PATRIOT Act- The Uniting and Strengthening America by Providing Appropriate Tools Required to Intercept and Obstruct Terrorism Act of 2001. The historic legislation brought about significant changes to the field of AML, including more than 50 amendments to the Bank Secrecy Act.

UTR- Unusual Transaction Report (Canada)

An internal form that is used to record the details of any transactions (attempted or completed) that is suspected of being related to money laundering or terrorist financing.

V

VIR- Voluntary Information Report

A report that is filed, by non-reporting entities, when there is suspicion of money laundering or terrorist financing.

W

Whitelist- A list of individuals and entities that trigger an alert by an AST, but who are later cleared as not to being a match.

Willful Blindness- A legal principle defined as the deliberate avoidance of knowledge of the facts or purposeful indifference to a money laundering transaction.

AML Organizations, Associations and Regulatory Bodies

ACAMS- Association of Certified Anti-Money Laundering Specialists

The largest international membership organization dedicated to advancing the professional knowledge, skills and experience of those dedicated to the detection and prevention of money laundering around the world, and to promote the development and implementation of sound anti-money laundering policies and procedures

ACFCS- Association of Certified Financial Crime Specialists

A leading association and provider of practical tools and knowledge to help professionals improve results in financial crime detection and prevention.

BCBS- Basel Committee on Banking Supervision

The Basel Committee on Banking Supervision is the primary global standard setter for the prudential regulation of banks and provides a forum for regular cooperation on banking supervisory matters.

BIS- The Bureau of Industry and Security

An agency of the United States Department of Commerce that deals with issues involving national security and high technology

CAFC- Canadian Anti-Fraud Centre

The Canadian Anti-Fraud Centre collects information on fraud and identity theft. They provide information on past and current scams affecting Canadians.

EGFIU- Egmont Group of Financial Intelligence Units

An international organization that facilitates cooperation and intelligence sharing between national financial intelligence units to investigate and prevent money laundering and terrorist financing.

FATF- The Financial Action Task Force on Money Laundering

An intergovernmental organization founded on the initiative of the G7 to develop policies to combat money laundering.

FinCEN- The Financial Crimes Enforcement Network

A bureau of the United States Department of the Treasury that collects and analyzes information about financial transactions in order to combat domestic and international money laundering, terrorist financing, and other financial crimes

FIRA- Financial Industry Regulatory Authority

Dedicated to protecting investors and safeguarding market integrity in a manner that facilitates vibrant capital markets.

FIU- Financial Intelligence Unit

A central agency that is responsible for receiving, analyzing, and transmitting disclosures on suspicious transactions to the relevant authorities.

FINTRAC- Financial Transactions and Reports Analysis Centre of Canada

Canada’s financial intelligence unit and regulator for AML and CTF

IIROC- Investment Industry Regulatory Organization of Canada

A non-profit, national self-regulatory organization. Established through the merger of the Investment Dealers Association of Canada and Market Regulation Services Inc. on June 1, 2008, IIROC oversees all investment dealers and trading activity on debt and equity markets in Canada.

OFAC- Office of Foreign Asset Control

Part of the U.S. Department of the Treasury, OFAC administers and enforces economic and trade sanctions based on US foreign policy and national security goals.

OSFI- Office of the Superintendent of Financial Institutions

The primary agency regulating financial institutions in Canada

Wolfsberg Group- An association of thirteen global financial institutions that have worked to develop global anti-money laundering guidelines for international private banks

This glossary of AML terms and acronyms as well as regulatory bodies and organizations, like the field of AML/CTF, will evolve over the coming months and years, so be sure to check back for updates.

Alternatively, if you are looking for an even more comprehensive glossary of AML acronyms, definitions and industry jargon, head over here.

Adverse media screening, also known as negative news screening, is an integral part of your KYC and AML regime. In its most basic form, AMS is the process by which a current or prospective customer is screened for negative news mentions and other unfavorable information. AMS allows your organization to flag a person or business as a potential risk and thus avoid future reputational and compliance damages.

Getting AMS right, however, comes with many challenges, including:

• AML compliance teams are often already bogged down by time consuming, manual processes. Manually searching news sources via search engines just adds to the complexity.
• The sheer volume and complexity of the news data that must be scanned and sorted is staggering.
• Dealing with news feeds versus public watchlists is exponentially more complex. It goes beyond filtering false positives and identifying the correct person or business. One has to resolve the news article for sentiment, context, and other aspects of relevancy to determine if it is truly adverse media.
• AML compliance reporting requirements are becoming more stringent among smaller financial institutions and MSBs. Credit unions in particular are currently facing many changes to their AML compliance requirements. This leads to either under-reporting or over-engineering, either of which can lead to costly mistakes.

Institutions need to develop AML compliance systems that can successfully and efficiently identify high-risk customers. In particular they should define:

• When to perform adverse media scans?
• What types of data to search for and from which sources?
• What type of systems and procedures to use in order to balance manual and automated interventions?

Adverse Media Screening: Finding the Right Balance

Luckily, AMS is not rocket science. However, developing an effective and efficient AMS does require a structured approach as well as the integration and implementation of some core technologies. It also requires striking a balance between taking a risk-based approach to AMS while maintaining high-level of customer service and flexibility to scale operations for future growth. Done right, and done with the right technologies, such as AI and NLP, AMS should not result in a slow down of your customer onboarding and ongoing monitoring.

That said, here are four best practices that your organization can use to get your AMS off to the right start:

1. Determine Your Risk Tolerance and Perform a Risk Assessment

Setting the parameters for what constitutes an acceptable level risk when deciding whether or not to work with a particular customer, is a critical first step. This process involves answering a number of questions, such as:

• Which high-risk groups of individuals or businesses is your institution comfortable servicing?
• Which offenses would bar a potential customer from being accepted? What offenses would not?
• How much weight do charges and allegations get versus an actual conviction by a court or an admission of guilt?
• Which types of adverse media could be considered lower-risk over time?
• What systems and processes will be in place to take action and perform Enhanced Due Diligence (EDD) when the results of adverse media screening return matches?

Every financial institution and MSB has its own unique exposure to potential financial crime based on the kinds of customers it serves, the locations it operates in, it’s product and service mix, and the channels it uses to service customers. Every financial institution and MSB also has its own unique risk tolerance.

Before you can even begin to design an effective AMS, it is crucial that you set clear risk categorization thresholds and outline the relevant actions and details required for each.

To help with you with that, try our FREE AML Risk Assessment Tool!

2. Decide When is the Right Time to Screen

Though most financial institutions and MSBs engage in customer screening during the onboarding process, many do not have a clear and consistent screening strategy, particularly when it comes to ongoing monitoring for adverse media. When standardized procedures and decisioning are lacking, both routine monitoring as well as monitoring triggered by an event, such as a news item, can result in discrepant responses.

Adding fuel to the fire are increases in regulatory expectations, expanding sanctions data bases and watchlists, and an increasing number of categories that must be screened against. For example, in recent years AML transaction monitoring has grown to include crimes dealing with human trafficking and smuggling, as well as new business customer types that fall in ‘High Risk’ category, such as cannabis-based businesses and a wide range of innovative fintechs, such as crypto-currency.

Some of the best practices to help ensure that your organization screens customers at the right time without increasing the rate of false positives includes:

• Decide which events and activities warrant further examination
• Include AMS in the standard onboarding procedures for all new customers and for any new accounts from current customers
• Establish automated AMS for all customers on a routine basis
• Screen current higher-risk customers based on your organization’s risk-based approach.

3. Use Credible and Current News Sources

These days, anyone with internet access can become a publisher of “news.” Although this helps to ensure that critical information becomes available to the public, at the same time it creates several challenges for your AML compliance system and procedures:

• First, how can you verify the credibility of an original adverse media source?
• How should you handle alternative news sources, such as social media?
• Which of the many tens of thousands of verifiable sources should you choose to monitor for a particular customer– especially since the sources themselves may change?
• How do you ensure that you are working with current news items?
• How far back in time to go for screening adverse media?
• Finally, how can you sift through all of this information in an efficient and effective way?

Given the dynamic nature of sanctions and screening data, you need a smart watchlist database that can actively weed out unnecessary information while focusing in on items of true value. This data can then be coupled with a dynamic, automated system to help you match, sort and analyze the results. You must get this step right before you even venture into the minefield of negative news screening.

That brings us to final best practice in adverse media screening…

4. Use Smart AMS Technologies

Automated AMS technology can help your organization address the deluge of information and alerts by focusing in on the most relevant matches. Sophisticated AMS tools also significantly reduce false positives and help you screen customers according to their risk categories.

Consider our Adverse Media Check ^®. We harness the power of AI, NLP, and machine learning to continuously monitor thousands of quality relevant global news sources. Our sophisticated algorithms get to work identifying sentiment, context, and the prime actors (i.e. personas) in each article. This information is then structured and fed into a persona database of individuals, companies, and politically exposed persons (PEPs) to identify cross-links between news and government and public watchlists. Each Persona is also tagged with FATF supported categorization of risk of financial crime.

Adverse Media Check also feeds into the KYC2020 DecisionIQ decisioning engine to render Pass/Fail/Verify decisions on AMS so that you can speed up your customer onboarding with the help of AI-based decision systems.

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.