AI-Washing in AML and Compliance: A Growing Challenge for Financial Institutions

Artificial Intelligence (AI) is widely regarded as a game-changer across various industries, including finance, compliance, and Anti-Money Laundering (AML). It has the potential to transform how financial institutions detect suspicious activities, automate compliance processes, and enhance operational efficiency. However, as AI solutions flood the market, a growing concern known as AI-washing has emerged — where companies exaggerate the capabilities of their AI tools, portraying them as more advanced than they truly are. This practice is especially concerning in the compliance and AML sectors, where accuracy, reliability, and adherence to regulations are crucial.

In this post, we will explore what AI-washing is, how it impacts the AML industry, and the implications it has for name screening, transaction screening, and transaction monitoring, which are vital components of compliance. Additionally, we'll examine what financial institutions and compliance professionals can do to navigate this growing issue.

What is AI-Washing?

AI-washing refers to the practice of exaggerating or falsely claiming that a product or service uses AI or machine learning (ML) when, in reality, it may be using traditional, rule-based systems or basic algorithms. Companies often use the term "AI" as a buzzword to attract attention, increase sales, and gain a competitive advantage without delivering the real, transformative power that genuine AI offers.

AI-washing is analogous to "green-washing," where companies misrepresent their products as environmentally friendly to benefit from market trends. In the case of AI-washing, firms mislead customers into thinking they are adopting cutting-edge technology, when, in fact, they are using outdated or subpar systems.

The Promise and Reality of AI in AML

The Promise:

In the realm of Anti-Money Laundering, AI offers significant potential to transform how suspicious activities are detected, reported, and investigated. AI and machine learning algorithms are capable of processing vast amounts of transactional data in real-time, identifying complex patterns that human analysts may miss. By leveraging advanced analytics, AI can:

• Enhance detection of money laundering activities by identifying unusual patterns and trends that deviate from established norms.

• Streamline compliance operations by automating tasks such as customer due diligence (CDD), Know Your Customer (KYC) checks, and transaction monitoring.

• Reduce false positives by providing more precise and context-specific alerts.

• Adapt over time by learning from new data and evolving typologies of financial crime.

The Reality:

Despite the promise, many financial institutions are still struggling with the practical limitations of AI in compliance. Not all AI-driven solutions are created equal, and many vendors offer products that are either partially or heavily reliant on Robotic Process Automation (RPA). This disconnect often leads to the issue of AI-washing in compliance, creating a range of challenges for the industry.

Name Screening, Transaction Screening, and Monitoring in the Context of AI-Washing

1. Name Screening: Ensuring Compliance with Sanctions Lists

Name screening is a crucial part of the AML framework. Financial institutions must screen customer names and counterparties against various global sanctions lists (e.g., OFAC, UN, EU lists) to ensure they are not engaging with prohibited entities. Many "AI" vendors claim to offer highly effective name screening tools that use machine learning to detect name variations, aliases, and potential matches with sanctions lists.

However, in practice, many of these tools rely on basic fuzzy matching algorithms or outdated data, which can result in either false positives (legitimate customers flagged as suspicious) or false negatives (suspicious individuals or entities not detected). This poses a serious risk of non-compliance, as financial institutions may unknowingly conduct transactions with sanctioned entities or fail to report suspicious activities.

AI-washed tools may also lack the sophistication required to continuously update their matching algorithms in line with evolving typologies or global sanctions lists. Financial institutions need systems that adapt to new patterns of evasion and are capable of flagging high-risk individuals accurately.

2. Transaction Screening: Identifying Sanctions Risks in Payments

Transaction screening is a critical compliance control that ensures financial institutions do not process payments involving sanctioned individuals, entities, or jurisdictions. This process involves real-time or near-real-time comparison of payment details against sanctions lists, such as those maintained by the Office of Foreign Assets Control (OFAC), the United Nations, the European Union, and other regulatory bodies.

Many vendors claim to offer AI-powered transaction screening solutions, often highlighting their ability to reduce false positives and detect evasive patterns used by sanctioned entities. However, in reality, some of these tools rely primarily on basic name-matching algorithms or static rule-based approaches that fail to adapt to increasingly sophisticated sanctions evasion techniques.

3. Transaction Monitoring: Real-Time Alerts and Investigations

Transaction monitoring is the process of continuously assessing transactions to identify suspicious patterns in real-time. This is a central element of any AML programme. True AI-powered systems can automate this process by learning from historical data to build a model of typical customer behaviour, flagging transactions that deviate from the norm.

Unfortunately, many transaction monitoring systems marketed as AI-based may still rely on static thresholds or basic algorithms that are easily bypassed by criminals using increasingly sophisticated methods. AI-washed tools may struggle to detect emerging forms of money laundering, such as layering or the use of digital assets, leaving institutions vulnerable to compliance risks.

For institutions to truly benefit from AI in transaction monitoring, they need systems that not only flag suspicious activities but also prioritise alerts based on risk and automate investigation workflows, freeing up compliance teams to focus on high-risk.

How AI-Washing Affects the Compliance and AML Industry

1. Misleading Claims and False Assurance

AI-washing creates a false sense of security, leading financial institutions and regulators to believe they are deploying advanced AI-powered solutions that will significantly enhance their compliance efforts. However, if the tools in place are not truly AI-driven or fail to offer the sophistication claimed, these institutions may be misled into a false sense of confidence.

Financial criminals continuously refine their techniques, using increasingly sophisticated methods to launder money and evade detection. If the technology in use isn’t as advanced as claimed, or lacks the ability to adapt to new typologies, financial institutions risk missing key signs of illicit activity.

For instance, many "AI" solutions might still rely on basic rule-based systems (e.g., transaction thresholds or pre-set patterns), which can be easily manipulated by criminals. This is particularly concerning within the context of AML, where even small lapses in detection could result in undetected financial crime and significant reputational damage.

2. Wasted Resources and Increased Costs

Another significant risk is that institutions may invest heavily in AI tools that prove to be ineffective. AML compliance is already costly, and firms often look to AI to reduce operational expenses and increase efficiency. However, when these systems don’t deliver the promised results or fail to meet regulatory requirements, financial institutions end up spending more on remediation, revalidation, audits, and, potentially, fines.

For example, a bank may purchase an AI-powered transaction monitoring investigation solution that, in reality, only performs rudimentary analysis. The organisation could end up spending a considerable amount of time and money attempting to address gaps in investigation outcomes or fulfil compliance requirements.

3. Lack of Transparency and Accountability

A key issue with many so-called "AI" systems is the lack of transparency in how decisions are made. True AI-driven solutions, particularly those based on deep learning and advanced machine learning, may function as "black boxes," making it difficult even for developers to fully understand or explain how decisions are arrived at.

In the context of AML compliance, this lack of transparency is especially problematic. Regulatory bodies require clear audit trails and justifications for why certain alerts are triggered or adjudicated. If AI-washed tools cannot provide this level of transparency, it becomes challenging for financial institutions to explain their actions during investigations or regulatory reviews.

For example, regulators may demand explanations for why a certain transaction was flagged as suspicious or why a particular individual’s name triggered a sanctions screening alert. Without transparency, it can be difficult to demonstrate that the system is functioning as intended.

4. Increased Risk of Non-Compliance

AI-washing can directly lead to non-compliance with local and international regulations. Anti-money laundering regulations, such as the Financial Action Task Force (FATF) recommendations, the Fifth Anti-Money Laundering Directive (5AMLD), and the Bank Secrecy Act (BSA) in the U.S., impose strict requirements on financial institutions. If AI tools are not truly effective — for instance, if they are based on outdated methods or lack critical features like real-time monitoring — institutions risk failing to meet these obligations.

Regulatory authorities are increasingly scrutinising the effectiveness of AI in AML practices. If the technology in place is not meeting expectations, financial institutions could face significant fines, sanctions, and reputational damage.

5. Erosion of Trust in AI Solutions

AI-washing harms not only individual organisations but also damages the broader reputation of AI solutions in compliance and AML. If institutions and regulators experience poor results due to AI-washing, they may become sceptical of AI-powered tools in general, which could stall innovation and delay the adoption of genuinely effective AI technologies.

As AI continues to develop and become a central feature in AML compliance, it is essential that the industry builds trust through transparency, clear benchmarks, and demonstrated results. AI-washing jeopardises this progress and hinders the sector from fully realising AI's potential.

6. Transaction Screening Compliance Issues

AI-washing in transaction screening can lead to serious compliance gaps, such as:

• Failure to detect subtle variations in sanctioned names, such as transliterations, abbreviations, or minor spelling changes.

• Inability to recognize indirect sanctions risks, such as payments routed through intermediary banks in non-sanctioned jurisdictions.

• Over-reliance on outdated data sources, leading to missed detections or excessive false positives that overwhelm compliance teams.

How to Combat AI-Washing in Compliance and AML

1. Education 

Before engaging vendors who are offering AI based solutions, organisations should educate themselves in what AI is, what it isn’t and how AI works. Compliance specialists should do this in conjunction with their technology partners so they can understand from a model and technology perspective what is being offered.

2. Demand Transparency and Validation

When selecting AI tools, financial institutions must demand full transparency from vendors. This means a clear understanding of how the system operates, the data it uses, and the methodology behind its outputs. Vendors should be able to demonstrate that their AI solutions are capable of detecting money laundering patterns effectively and can be fully audited for compliance with regulatory standards.

3. Focus on Tangible Outcomes, Not Just Buzzwords

Compliance officers and AML specialists should prioritise the practical, real-world results of AI tools rather than getting caught up in industry buzzwords. Does the system actually reduce false positives, improve detection rates, or speed up the investigation process? If not, the tool may be suffering from AI-washing. Institutions should look for measurable improvements in compliance efficiency, not just marketing claims.

4. Independent Testing and Auditing

Before fully implementing any AI system, organisations should engage in independent testing and auditing. Third-party audits are essential to assess the effectiveness of the solution and to ensure it meets both regulatory requirements and the institution's specific compliance needs. This process can provide reassurance that the AI tool performs as promised and adheres to legal and operational standards.

5. Ensure Continuous Improvement and Adaptation

AML tools powered by AI must be adaptive and capable of learning from new data over time. Financial institutions should seek vendors that provide ongoing updates to their systems based on emerging threats, regulatory changes, and feedback from real-world use cases. This ensures the tool remains relevant and effective in a constantly evolving financial crime landscape.

6. Specific Sanctions Screening Requirements

To ensure effective sanctions screening, financial institutions should seek AI solutions that provide:

• Advanced linguistic algorithms to handle complex name variations across different languages and character sets.

• Context-aware screening, which considers additional attributes (e.g., country, entity type) rather than relying solely on name matches.

• Real-time updating and adaptation, enabling the system to incorporate new sanctions typologies and emerging evasion tactics.

• Without genuine AI-driven enhancements, transaction screening solutions marketed as "AI-powered" may not provide the level of protection required to prevent regulatory breaches and financial crime exposure.

AI holds immense potential to transform the AML industry by making it more efficient, accurate, and adaptive. However, AI-washing remains a growing concern that could derail this transformation. By remaining vigilant and demanding transparency, financial institutions can avoid falling victim to misleading claims and ensure they are investing in genuine AI solutions that enhance their compliance programmes and strengthen their ability to detect financial crime.

As the regulatory environment becomes ever more stringent and criminals continue to evolve their methods, the need for truly effective AI-driven AML tools will only grow. It is crucial for compliance professionals to not only choose the best available tools but to also ensure that those tools are genuinely delivering the results and value they promise.