FATF was established in 1989 as a one-year fact-finding body to research money laundering trends and develop recommendations and policies to combat illicit activities. However, it outlived its original purpose to become a 40-member global organization with a mandate to set international standards of anti-money laundering (AML) legislation, which are translated into domestic policies, monitor the effectiveness of law enforcement, and assess countries’ compliance.

Since the 2000s, FATF has maintained two lists of countries with strategic deficiencies in policies against money laundering, terrorism financing, and proliferation: a black list of high-risk jurisdictions and a grey list of jurisdictions under increased monitoring. 

### The blacklist is plain and simple

The FATF blacklist includes countries that refuse to cooperate with FATF on improving their AML and CTF (counter-terrorism financing) legislation and measures or systematically fail to do so. By 2024, the list included three countries: isolationist North Korea; Iran, which is a natural state-sponsor of terrorism; and Myanmar, which was previously blacklisted from 2001 to 2006, and from 2011 to 2016, and was included in the grey list from 2020 to 2022. 

Countries on the blacklist are essentially economically pariah jurisdictions cut off from the global finance. Private and state-owned enterprises de-risk and avoid dealing with them, while international financial institutions, such as the International Financial Corporation (IFC), and the World Bank (WB), hesitate to provide loans to countries that fail to ensure the funds won’t be laundered and spent on illicit activities.

### The grey list is a different story

The grey list includes countries with strategic deficiencies in AML and CTF policies that cooperate with FATF and make time-bound political commitments to address these deficiencies and follow an approved action plan. Once the issues are identified during the mutual assessment (the countries independently assess each others’ compliance with AML and CTF standards using FATF methodology), the country is provided one year to address them. If no significant progress is achieved, the country can be included in the grey list.

FATF considers greylisting a motivation for improvement rather than a punitive measure or signaling of external risks. The organization doesn’t oblige members to take any action, and its recommendations on the matter are non-binding. However, other financial actors see greylisting as a potential prelude to further sanctions, causing them to reconsider their risk profile, reduce investments, and strengthen compliance. Even though FATF doesn’t call for enhanced due diligence anymore, countries implement such measures as a part of national regulation. 

### Prepare for the foreseen consequences

Greylisting has severe effects on the national economy that form a negative cycle, further worsening the economic conditions in the targeted country. According to the International Monetary Fund (IMF) working paper published in 2021, the capital inflows (foreign investments and bank transfers) declined by 7.6% of GDP on average, following the counties’ introduction to the grey list.

While there’s no single scenario, the development of negative effects of greylisting may take the following order.
- Both the greylisted country and the other countries increase regulatory pressure, sometimes making regulatory risks unmanageable or inadequate for business.
- The companies based in the greylisted countries or maintaining business relations with them have to amend internal policies and operating practices in response to greylisting. Increased compliance costs lead to increased supplier costs.
- The other countries implement enhanced due diligence audits if their supply chains pass through greylisted countries. In some EU member states, the firms even have to identify and eliminate risks to human rights and environmental damage along their supply chains—which skyrockets compliance costs.
- Enhanced due diligence measures lead to problems with correspondent banking. Foreign banks must introduce more checks and investigate every suspicious transaction within their risk calculation models. Legit transactions are often rejected, or more paperwork is needed.
- The aforementioned costs and risks lead to reduced profits, which causes companies to reduce the range of goods and services provided or exit the market. Lowered supply and profitability reduce tax payments and limit debt capacity.
- Investments decline following the decline of profits and uncertainty of investors, leading to the reduction of capitalization of local markets.
- Increased national budgetary pressure meets the decreased ability to attract funds from donors, such as IMF, IFC, or development banks. The country's credit rating declines.
- International bond and loan markets react, putting pressure on both government borrowing levels and corporate spreads, further reducing access to the capital

The combination of such factors launches a negative spiral, which causes more harm to the national economy with each interaction, as the damaging factors take cumulative effect. The whole process is triggered by overreaction and over-compliance, with stakeholders becoming less responsive to positive news emanating from the greylisted countries. Additionally, greylisting may attract individuals and groups interested in exploiting identified regulatory loopholes.

### Not all countries are equal

In the worst possible scenario, a country with a small or weak economy will struggle to overcome the consequences of greylisting. It may cause long-term adverse effects, such as poverty, inequality, negative impacts on education and healthcare, brain drain, and political instability.

The effects are considerably smaller or barely noticeable for strong economies. For instance, the UAE was greylisted from 2022 to 2024 but enjoyed stable economic growth fueled by the high energy prices. Despite the inconveniences in correspondent banking, the country continued to attract foreign investors, and its credit rating (AA- stable, according to Moody’s) wasn’t affected.

### What can be done to stop all this?

To get off the grey list, the country has to follow the FATF-approved action plan to strengthen its AML and CTF legal framework. The steps may include: improving laws and regulations, bolstering international cooperation in fighting money laundering, addressing risks related to shell companies, ensuring effective implementation of targeted financial sanctions, and empowering the Financial Intelligence Unit (FIU) in investigating and persecuting crime.

Delisting has a massive positive impact, significantly increasing the confidence of foreign investors, firms, credit rating agencies, and international financial institutions. Supported by the positive expectations, the economy recovers and returns to growth.

One should remember that even if the formal requirements for delisting are met, the poor implementation of new regulations may undermine the country’s efforts to manage money laundering risks. For instance, Kenya, which was previously greylisted from 2010 to 2014, failed to combat suspicious financial activity, regulate shell companies, and implement other measures. It remained a major money laundering hub and was eventually readded to the grey list in 2024.

Explore actionable insights and tools to stay ahead of compliance challenges with  [Dataspike](https://dataspike.io/), your trusted partner in AML and KYC solutions.


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What Is the FATF Grey List? Understanding Its Role in Global AML Policies

### Binance: USD 4.3 billion for non-compliance

One year ago, US regulators blasted the world’s largest cryptocurrency exchange, Binance, with a record-setting penalty of USD 4.3 billion for outrageous compliance failures. In November 2023, the company and its founder Changpeng Zhao pleaded guilty to non-compliance, poorly executed anti-money laundering instruments, and deliberate disregard for illegal funds being routed through the platform. We covered this story in our December newsletter in 2023.

The [charges](https://www.reuters.com/technology/highlights-us-charges-against-crypto-exchange-binance-2023-11-21/) against the platforms included but weren’t limited to facilitating transactions worth billions of dollars without proper KYC checks, inadequate compliance measures, proceeding transactions that were clearly the proceeds from darknet market operations (which included transactions to Hydra, a notorious Russian marketplace used by criminals for nearly everything for drug sales to [contract murders](https://www.bbc.com/russian/features-50167318)), enabling sanctions evasion, and financing of terrorist organizations, such as Hamas or Hezbollah. These “shortcomings” in compliance were hardly a secret for the management and the employees, but no steps were made to prevent Binance from being used for illegal operations.

Other changes were related to “[Tai Chi](https://www.forbes.com/sites/michaeldelcastillo/2020/10/29/leaked-tai-chi-document-reveals-binances-elaborate-scheme-to-evade-bitcoin-regulators/),” the scheme Binance used to deceive US regulators by setting up a separate compliant US entity yet providing crypto services to US citizens on its main platform. Forbes originally revealed this scheme in 2020. In June 2023, the US Securities and Exchange Commission sued Binance and Zhao for illegal operations in the US and defrauding crypto investors.

Both the platform and its founder pleaded guilty. The court obliged both to pay massive non-compliance fines. Zhao was also sentenced to several months in prison. That verdict dramatically differed from other cases, when cryptocurrency platform operators faced decades of imprisonment for facilitating money laundering, which was widely considered a result of Binance’s full cooperation with the US authorities.

### TD Bank: guilty plead for money laundering

In November 2024, for the first time in US history, TD Bank pleaded guilty to money laundering charges. The Toronto-headquartered bank (its name stands for Toronto Dominion) is the second largest in Canada and the sixth largest in North America. Following two decades of successful expansion in the US market, the bank now faces a USD 3 billion fine and a little-used asset cap for compliance failures.

TD Bank’s history of regulatory failings in Canada and the US is continuous enough to be separated into several paragraphs.

The bank often failed to report suspicious activity and take the necessary actions. In 2008 and 2009, TD Bank did not file suspicious activity reports regarding the notorious [Scott Rothstein Ponzi scheme](https://www.forbes.com/sites/jordanmaglich/2014/10/31/on-fifth-anniversary-of-rothsteins-1-2-billion-ponzi-scheme-questions-remain/) while its regional vice-president was [found](https://www.sec.gov/newsroom/press-releases/2013-192) involved in fraud and misleading investors. TD Bank’s P2P payments app Zelle, launched in 2017, raised numerous red flags for the lack of transaction monitoring with investigations identifying alleged money laundering and payments for illicit activities, such as human trafficking. From 2018 to 2021, the bank failed to terminate hundreds of accounts marked for closure by its compliance team, allowing a total of USD 5 billion to be transferred to suspicious accounts.

Over the years, TD Bank’s compliance department lacked personnel and underperformed. According to reports, the bank systematically failed to detect and report alerts and respond to subpoenas. The bank’s management was aware of the issues but took no measures. The loopholes in the bank’s systems allowed corrupt bank employees to bypass compliance controls and execute transactions on behalf of customers potentially related to criminal groups in Colombia.

On another note, TD Bank failed to implement Regtech software effectively and had deficient Know Your Customer (KYC) procedures, which permitted high-risk clients to open accounts and perform transactions. In 2017, the internal assessment of the interbank transaction monitoring system found naturally no active system to monitor domestic transactions. In 2023, it became public that due to coverage gaps, transactions worth several trillion dollars weren’t screened by the bank’s software. The bank had spent several years changing its Regtech services provider but only made things worse. The new system seemingly failed to identify questionable operations, leading to a 123% increase in transactions that passed the AML checks. Still, the bank didn’t notify regulators or apply any mitigation control.

Under the penalty agreement, The Office of the Comptroller of the Currency (OCC), a US bank regulator, also implemented a USD 434 billion asset cap: a limit on TD Bank’s business functions and consolidated assets. Alongside the fines, such restrictions are meant to ensure that the bank focuses on implementing proper controls and risk management. The asset cap will remain indefinitely unless the OCC removes it. It will likely take years to satisfy the regulators: for instance, Wells Fargo still deals with its USD 1,95 trillion asset cap enforced in 2019.

### It’s time for a change: AML regulation in Canada

TD Bank’s problems in the US had an unusual outcome: the Canadian Bankers Association requested the House of Commons to reform the national AML procedures. The regulatory burden in Canada is terrible: banks and other businesses (such as brokers, agents, real estate firms, sales representatives, money services, casinos, etc)  have to submit 12,5 times more reports than those in the US and 96 times more reports than those in the UK. However, the performance of the Financial Transactions and Reporting Analysis Centre of Canada (FINTRAC), the national supervisory and financial intelligence organization, is subject to debate.

At the same time, Canada is considered a money laundering haven due to legislation that shields the real owners of companies, effectively allowing criminals or sanctions evaders to establish shell companies in Canada, make transactions, and purchase assets. Many forms of business organizations only require the key managers to be publicly disclosed, and some exempt people from filing taxes in Canada if they are living elsewhere. The practices of tax evasion, sanction evasion, and money laundering using Canadian firms are sometimes referred to as “snow washing”. TD Bank’s woes resulted from a combination of many factors. However, one can’t write off the regulatory requirements and enforcement practices that shaped the bank’s business strategy. 

### Our simple advice to the banks: invest in Regtech

We are not strictly neutral on the matter. Dataspike.io is an advanced regulatory technology platform that automates KYC and legal screening operations for banks, money services, and other firms in regulated markets. TD Bank’s problems resulted from poor managerial decisions, vastly underperforming Regtech software and a short-staffed compliance department.

[Dataspike.io](http://dataspike.io) focuses on a particular component of AML compliance, but we get your back no matter your Regtech stack. Our [platform](https://dataspike.io/pricing) scales well and works for both newborn startups and established banks. It integrates well with other compliance technology as a component of a robust compliance system that minimizes the chance for mistakes and noncompliance penalties. 





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Binance and TD Bank: Compliance Failures and the Rising Importance of Regtech

### Introduction

In recent decades, sanctions have become a standard instrument in international politics and a headache for compliance teams. Sanctions take different forms, from restrictions to additional rules on specific services and trade deals (such as price caps), and can be applied to individuals, entities, or jurisdictions.

Sanctions compliance is easily the most risky regulatory environment. Sanction evaders create complex schemes to access financial instruments and employ new technologies to launder and transfer money. 

Compliance officers should understand how such actors operate to abide by national and international sanctions without over-compliance, establish an effective Regtech stack to analyze transaction patterns and cooperate with national financial intelligence units to secure themselves from potential noncompliance fines.

Here, we review several cases elaborating on old-school and novel sanctions evasion approaches and provide advice on how to react to them.

### Case one: Tracking Cryptocurrencies 

The generous promise of anonymity and privacy made cryptocurrencies widespread among illegal actors. Still, it took stablecoins (pegged to traditional currencies) to make them genuinely convenient for money laundering, illegal trade, and sanctions evasion.

Just like bank accounts, cryptocurrency wallets can store significant wealth. A non-custodial wallet not operated by a platform like Binance or Coindesk requires no KYC checks to create and manage and can’t usually be traced back to its beneficial owner. A single person can control a virtually unlimited number of wallets, which means assets belonging to a single person can be distributed across numerous accounts, making them hard to trace.

However, blockchain is transparent by design, meaning that a high-net-worth sanction evader won’t be able to quietly transfer all his wealth through crypto. The lack of liquidity in the cryptocurrency market means that crypto isn’t reliable as a replacement for traditional financial instruments on a large scale. However, crypto is suitable for smaller-scale transactions, such as payments for smuggled goods between private entities working on procurement for sanctioned organizations. There are many crypto exchanges with lax or lacking AML controls that are specifically suited for such clientele.

### Case Two: Informal Connections

One crucial challenge in sanctions compliance is identifying ties between designated entities and proxies that facilitate sanctions evasion. One year ago, we emphasized this problem in the context of PEP screening in jurisdictions with widespread government corruption. Sanctioned individuals may conceal ownership of assets and actual beneficiaries of transfers behind distant relatives, close associates, and other trusted individuals, such as lawyers or consultants.

### Case Three: Revealing Schemes

Large-scale sanctions evasion, such as international trade with a sanctioned country or corporation, often requires complex schemes, including numerous legal entities in different jurisdictions (including off-shore and mid-shore financial centers), multiple international bank transactions to conceal the beneficiaries, and shady practices related to movement of the goods. 

Compliance-wise, such activities pose a significant risk to financial intermediaries. 
On the one hand, they can’t see the whole picture and may be convinced that the transactions between parties are legit. Even though sometimes the people behind sanction evasion schemes are well-known wealthy individuals with a lobbying potential, often the global sanction evasion and money laundering networks are orchestrated in secrecy and managed by seemingly quiet people living simple lives, which can’t usually be connected to high-risk activities.

On the other hand, once an illegal scheme is revealed, the regulators may consider that a financial intermediary (such as a bank) failed to take sufficient measures to prevent money laundering and sanction evasion, resulting in noncompliance fines. The well-known cases include Standards Chartered, Deutsche Bank, HSBC, Danske Bank, Societe Generale, Commerzbank, Credit Suisse, and BNP Paribas, with cumulative fines totaling 20 billion dollars in the last two decades.

### Conclusions

In 2024, compliance teams should go far beyond standard anti-money laundering frameworks to ensure full enforcement of international sanctions. There are several key recommendations:

* Fintech companies that enable the convenient use of traditional money and cryptocurrencies for everyday payments should employ on-chain analysis and risk assessment for crypto assets to prevent illicit transactions and ensure compliance. Such an approach is essential to avoid over-compliance and enable legitimate money transfers over the blockchain.
* Advanced Regtech solutions automate complex compliance tasks, such as adverse media searches, which allow the identification of informal ties between designated individuals and legal entities and facilitators of sanctions evasion. 
* Financial intermediaries lack instruments to identify complex sanctions evasion schemes, so related noncompliance risks can’t be mitigated. That’s why it’s crucial to cooperate with the national financial intelligence units and independent financial crimes investigators (such as [OCCRP](https://www.occrp.org/en)) and be proactive in reporting potential sanction evasion cases.

Dataspike platform leverages compliance teams’ ability to identify high-risk individuals and entities and prevent sanctions evasion, money laundering, and other illicit activities. It aggregates information from various sources, assesses risks, and helps compliance officers make informed, evidence-based decisions.


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Lessons on sanctions evasion

### Introduction

In summary, money laundering is the obfuscation of sources of funds obtained through illicit means. According to the Oxford English Dictionary, the first well-documented use of this term dates back to the 1970s and was widely adopted in the 1980s. However, money laundering has likely existed since the emergence of money and laws that prohibited certain activities.

Money laundering follows the predicate offense from which the funds are acquired. This legal concept also originates from the late 20th century; however, it’s essential for understanding the history of money laundering. While crimes such as piracy or robbery have been common throughout human history, there are more opportunities to explore the history of money laundering following white-collar offenses, such as bribery, financial fraud, or tax evasion.

This publication will review how money laundering has changed over time. This article will use modern concepts and terms to discuss events that took place years and centuries ago. 

### Three stages of money laundering

Money laundering consists of three steps: placement, layering, and integration. Placement is where questionable funds are reintroduced to the financial system. For that purpose, money can be deposited into accounts at financial intermediaries. To circumvent the reporting threshold, criminals split money and employ so-called “money mules” to open accounts and make transfers. Sometimes, money is sent abroad via remittance services or physically smuggled to jurisdictions with lax financial controls.

At the Layering stage, the funds are transferred through shell companies, often established in different jurisdictions, to hide the origin of the deposited cash. A professional facilitator usually orchestrates the process. Money launderers use fictitious contracts for trade or lending to provide plausible supporting documentation. Investigations based on massive leaks of documents related to offshore companies, such as Panama Papers or Pandora Papers, can illustrate the complexity of such schemes.

Finally, money Integration is performed through ostensibly legal economic transactions, such as acquiring various assets, purchasing goods, investments, etc. At this point, the criminals re-acquire control over the funds.

### Money laundering cases over the history

#### China

In the publication “Lords of the Rim,” American historian Sterling Seagrave provided details on tax evasion and money laundering practices employed by Imperial China-era merchants. One notable example is Fei-Chien, or Flying Money, an informal value transfer system based on exchange notes. This system enabled traders to avoid taxation when transferring funds abroad and leave a minimal money trail record. It also enabled trade in goods forbidden in particular parts of the ancient world, as sales and acquisitions usually remained invisible to authorities. 

Fei-Chien was highly formalized and regulated because it was the backbone of Chinese expatriate economics. In modern terms, it’s one of the early documented examples of placement and layering in the context of money laundering. The system is still widely used in South Asia and remains part of money laundering schemes for Chinese crime syndicates.

#### Ancient Greece

The polises of Ancient Greece introduced two components of the modern financial system: an agora, which served as a centralized location for financial activities overseen by the authorities, and a strong institute of reputation in economic relations (which remotely resembles modern KYC). Lenders operating across Greek cities separated by the Aegean Sea established networks for sharing information about maritime merchants to exclude fraudulent and dishonest ones from future transactions.

The tax system of ancient Greece, specifically classic Athens that ditched indirect taxes) provided many opportunities for tax evasion. Direct taxes were mainly applied to wealthy individuals. For instance, liturgies obliged rich citizens to fund specific public activities or maintenance of state property, such as triremes, during the war. 

Wealthy Athenians often concealed their wealth by assigning their assets to nominal owners or putting money in the bank. There was little or no bank regulation, and private financial intermediaries could ensure the complete confidentiality of their clients and even provide the latter with secret returns with no tax obligations. The same could be done with other illegal proceeds (such as bribes), making banks a one-step, one-stop laundromat for Greek citizens.

#### Modern times Switzerland

As money laundering requires secrecy, criminals benefit from corporate and bank secrecy practices. For centuries, Switzerland established its reputation for extreme financial secrecy and became a European hub for high-profile money launderers. Even in the 2020s, it was blamed for a passive stance towards money laundering, sanction evasion, and other illegal practices.

Swiss bank secrecy regulations date back to the 1713 Council of Geneva decision that forbade bankers from disclosing information about their clients to safeguard the interests of the Catholic French monarchy, which wanted to obscure its dealings with banks in a Protestant Switzerland during the European wars of religion. Financial secrecy became a tradition and was later supported by legislation, making obtaining information about accounts and their owners borderline impossible for authorities.

In the early 20th century, Swiss banks promoted the country as a tax haven and a safe place to store assets, which resulted in an unprecedented influx of money during the turbulent times of the Word War and the following succession of political, financial, and monetary crises in Europe and ashore. 

Money launderers, such as the renowned “Banker for the American Mafia” Meyer Lenski, took advantage of the protection provided by the Swiss banking system. He purchased an actual Swiss bank and built a network of shell companies and offshore accounts to launder proceedings from illegal activities in the United States. He employed nearly anonymous numbered bank accounts that provided an additional layer of privacy to deposit questionable money that passed through his international laundromat and used them to secure legal loans in the bank. Despite all the efforts of the U.S. authorities, he was never convicted. Lenski died in 1983, aged 81, free.

#### The Prohibition in the USA

In the early 20th century, money laundering wasn’t even considered a separate activity but a component of other crimes. No legislation obliged banks to identify their clients or question the source of funds thoughtfully. However, tax evasion was already a thing, and criminals needed a way to convert illegal proceedings into legal profits.

There’s a widespread belief that the term “money laundering” originates from the activities of a renowned Prohibition-era gangster, Al Capone, who allegedly used laundrettes with cash-only laundromats as cover firms to launder revenues from prostitution or bootlegged liquor sales, and other illegal businesses. The amount of cash flowing into laundromat operations was hard for law enforcement authorities to monitor, generating vast amounts of seemingly legitimate funds.

#### Watergate Scandal

The first well-documented use of the term “money laundering” in the general media was in the Guardian’s 1972 coverage of the Watergate scandal. It was the first time the U.S. authorities emphasized the need to interpret money laundering as a separate offense. 

The Watergate scandal is commonly associated with a break-in at the Democratic National Committee headquarters in the Watergate Office Building in Washington, D.C. Still, the most essential part was a scheme that concealed funding sources for Richard Nixon’s 1972 election campaign. After being elected President of the United States in 1968, Nixon established a “Committee to Re-elect the President” to aggregate donations for his next campaign. However, the contributions didn’t go directly to the fund; they were rerouted to Mexico and then back to a Miami-based front company, allowing the concealment of the origin of money and donors’ identities.

The Department of Justice of the United States linked the cash found on the Watergate burglars to the “Committee to Re-elect the President” funding, and the connections were later confirmed during the burglars’ trials. The finding convinced the House of Representatives to grant its judiciary committee more authority to investigate money laundering matters. Despite winning the 1972 elections, Nixon resigned in 1974 due to the threat of impeachment for this and other misdemeanors.

#### War on Drugs

During the global war on drugs in the 1980s, law enforcement authorities required more instruments to chase money and freeze and seize assets belonging to organized crime. The United States pioneered the development of anti-money laundering legislation, which finally led to the formalization and globalization of control over money laundering.

During Ronald Reagan’s presidency, the U.S. authorities escalated their control over illegal drugs. The 1985 Money Laundering Control Act declared money laundering a separate federal crime for the first time. It enabled law enforcement and intelligence services to impose restrictive measures on proceedings from the drug trade. This strategy proved effective and was soon followed by similar laws in other countries.

In 1988, the United Nations implemented the Vienna Convention Against Illicit Traffic in Narcotic Drugs and Psychotropic Substances, which declared money laundering illegal, specifically in regard to the drug trade. In 1989, the G7 established the Financial Action Task Force (FATF) to analyze money laundering trends and develop standards and best practices to prevent it nationally and globally.

#### War on Terror

The 9/11 terrorist attack on the World Trade Center in New York had a profound effect on the development of anti-money laundering regulations in the U.S. and the world. For instance, the Patriot Act of 2001 directed financial institutions to expand anti-money laundering programs and improve due diligence reporting on foreign bank transactions. It also introduced new communication standards between law enforcement authorities and financial intermediaries. The stricter anti-money laundering regime implemented by the U.S. to fight the threat of international terrorism soon became the benchmark for the rest of the world.

### Conclusions

We made a short glimpse into the evolution of money laundering and financial regulation from the ancient times to the beginning of the 21st century. Get ready for our next historical review on the modern ways of money laundering and contemporary AML tactics.



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The brief history of money laundering (from ancient times to the early 21th century)

### Trust, but Verify: GenAI in AML Compliance
Generative artificial intelligence (GenAI) is the most significant tech hype nowadays. Large language models became standard productivity tools, and new startups promise to revolutionize industries with GenAI. However, most existing GenAI-based products help in simple everyday tasks rather than redefining how people work. Can GenAI become a life-changer instead of a time-saver in a complex workflow, such as anti-money laundering (AML) compliance? Let us explore further. 

### Old-School Machine Learning vs. GenAI

Machine learning, the application of artificial intelligence (AI) that allows machines to learn and improve, gained traction in the 1990s and became mainstream in the 2010s. Machine learning instruments work under the hood of popular websites, operating systems, business applications, etc. These applications are valuable due to their ability to make accountable predictions (predictive analytics).

GenAI is a particular implementation of machine learning that aims to generate new content (text, images, video and so on) based on its training dataset. The GenAI boom of the recent decade was manifested by the launch of numerous consumer AI applications, as opposed to industrial, such as chatbots or image generators, which facilitated the adoption of AI tools as part of daily routines and workflows. 

### The GenAI Hype
The hype surrounding GenAI exploded in the late 2010s, bringing substantial investor interest to the industry and fueling the growth of numerous startups, creating their own AI or building specialized solutions on top of existing ones. 
Financial institutions (FIs) have long employed various AI/machine learning instruments to offer digital-first onboarding, run adverse media screening of their customers, improve false positive detection in transaction monitoring and identify unusual patterns in user activity. Does GenAI have unique potential for changing a current regulatory technology stack, or can it make things worse by empowering cybercriminals and fraudsters?

### The Challenges
The ability to create plausible-looking content makes GenAI shine in the hands of cybercriminals, who use it to bypass know your customer (KYC) procedures in banks, financial technology and crypto apps with fake identities. GenAI applications create fake IDs, proofs of address and other documents, generate photos and even pass the liveness check with deepfakes.

A recent example of a potentially mean GenAI was the network behind the currently inoperative OnlyFake website, which was supposedly able to create realistic-looking IDs. People claimed to be able to pass through document verification at PayPal and cryptocurrency exchanges. 

Later, GenAI applications could also be tasked to create realistic-looking invoices and records for money laundering purposes. Technologies enabled malevolent actors to single-handedly manage multiple synthetic users’ identities and accounts. Over recent years, numerous tech enthusiasts and security experts have pointed out vulnerabilities in existing KYC procedures; however, no large-scale industry research has been published.

### The Opportunities

AML compliance is standardized and directed by legal requirements. A properly trained conversational assistant can help compliance officers with everyday duties and provide a fast search across legal documents and cases. There is little space for AI “creativity,” but GenAI can solve one of the long-standing issues associated with machine learning, the 

### AI bias.

Bias is the fundamental problem of machine learning. Bias may originate from the training data that overrepresents or underrepresents specific groups based on race, gender or other characteristics. Bias can be rooted in the very design of a machine learning model, reproduced through self-training and amplified over time. In this case, GenAI can be tasked to generate vast amounts of synthetic data, such as customer profiles with certain qualities, which will be further used to train other machine learning models, tweak their performance and reach better results. 

### The Considerations for GenAI

There are several matters to consider when discussing the implementation of AI in financial compliance. 
The matter of AI accountability became important with the adoption of machine learning models in business. A quote attributed to a 1970s IBM presentation states: “A computer can never be held accountable; therefore, a computer must never make a management decision.” However, when it comes to GenAI applications, such as chatbots, people use and overuse them in decision-making and even brag about it on X and other social media platforms. In compliance, AI-induced mistakes may come with a high price for both the company and the customer, which means using AI recommendations should be limited.

FIs must be able to explain their decisions to internal and external parties, including regulatory bodies. However, the complexity of modern AI models results in a situation when the input signals might not be easily identifiable or even known. This problem is even worse for generative AIs, which often trade explainability for flexibility and are even more prone to AI hallucination.

Another matter is data privacy, which is even more crucial for companies in the regulated financial market. Machine learning models are prone to data leakages from the training datasets, the capacity to unmask anonymized data through interferences, and “remembering” information about individuals in the training datasets even after the data is discarded. That is a known issue, and some generative AI developers explicitly state that they cannot ensure the security and confidentiality of the information that users provide.

### Conclusion

For decades, machine learning revolutionized business in the financial services and regulatory technology markets. One should not be mistaken by the hype surrounding GenAI: Machine learning models already optimize dozens of crucial processes in AML compliance workflow. 

GenAI became a massive problem for AML compliance, as cybercriminals employed the technology to create synthetic identities of nonexistent people with forged documents and deepfake-powered faces. GenAI has the potential for a positive change in the industry; however, it is not as apparent as its ability to cause chaos for compliance officers.

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Generative AI and Compliance

Know Your Customer (KYC) is essential to the modern anti-money laundering framework. KYC guidelines are designed to prevent banks and other regulated companies from being used for illicit activities and serve as a starting point for other anti-money laundering measures. Yet, even though most people understand the underlying rationale behind KYC, it often becomes the worst customer experience in finance.

A poorly automated KYC can take several days, including office appointments and additional paperwork. Compliance officers' decisions are subject to individual racial, gender, or other biases. Some customers consider KYC a violation of privacy, while others are reasonably concerned about data safety. Finally, there are conspiracy theorists and crypto-anarchists afraid of mass surveillance. People also disapprove of implementing KYC over emerging financial instruments like cryptocurrencies (however, this is not the case).

Modern KYC isn’t that bad, but it’s not great either. Here, we will consider what can be done to make KYC faster, less error-prone, and more comfortable for users in the future.

### One-step KYC solution with eID.

Electronic identification cards store basic personal identification information, sometimes biometric data (such as photos for automated visual identification or fingerprint data), and special credentials that allow for reliable corporate or state services authentication. An eID card with an integrated NFC chip can be read using a smartphone or specialized reader, allowing in-person or remote identification.

National legislation for eID evolves, allowing for streamlined KYC. For instance, the comprehensive legal basis for the remote use of digital identities for KYC in Europe is included in the eIDAS 2.0 standards, the European Banking Authority’s (EBA) guidelines, and the 5th AML Directive. Soon, the KYC process in the EU and other parts of the world will become much easier, less annoying, and generally better (just like the identity check in the airport).

### Risk score aggregation and sharing.

Discomfort caused by KYC is partially rooted in its repetitive nature. An individual has to pass KYC checks again and again in different banks, fintech platforms, crypto exchanges, luxury goods points of sale, and so on. Even worse, the decisions vary due to different compliance policies and compliance officers' interpretations of such policies. That leads to stress, dissatisfaction with negative responses, and general customer dissatisfaction.

The industry can overcome this problem by introducing aggregated risk scores that reflect the results of many KYC checks and AML screenings. Such a score should not include full information about the client but rather a tokenized piece of data. A compliance officer will still be able to request additional documents or explanations. Still, for most people, a KYC will become a fast, easy, and error-prone procedure relevant to their previous experience.

### The true potential of AI is still unknown.

Machine learning already serves as a backbone of many compliance procedures. Computer vision models recognize ID data and run liveness checks. Sophisticate algorithms analyze thousands of transactions in real time to identify unusual patterns potentially related to illicit activities. AI also enables perpetual KYC, an approach that constantly improves KYC profiles based on new knowledge about a customer. 

Some people in the industry research the potential of generative AI in compliance; however, it’s limited by the very nature of such ML models. We commented on the matter in our blog: [Trust, but Verify, GenAI in AML Compliance](https://dataspike.io/blog/generative-ai-and-compliance?lang=en). We believe that generative AI can help identify existing software's drawbacks and tweak their behavior by creating digital identities with specified properties to train and improve ML algorithms.

The accelerated development of AI technologies brings both potential and risks. We still need to overcome various problems, including privacy and security threats, the lack of transparency and explainability of AI actions, and align the potential implementation of novel technologies with laws and regulations designed to protect humans.


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