Given the growing scale and scope of organized crime, the topic featured as a core theme in our State of Financial Crime 2025 report, spotlighting its prevalence across various markets and the specific anti-money laundering (AML) tools firms are using to identify OCG-related threats. 

This article explores some of the nuances of organized crime’s transformation as well as the new risks it presents and how you can effectively prepare to protect your organization.

How is organized crime changing?

Based on the findings and investigations of researchers and law enforcement agencies, OCGs continue to find new ways to make money, including:

1. The diversification of criminal activities

OCGs have significantly expanded their scope beyond traditional crimes like drug trafficking and extortion. These groups are now heavily involved in environmental crimes such as illegal logging, mining, and wildlife trafficking. These activities cause widespread environmental damage and introduce financial and reputational risks for businesses, particularly those with complex, global supply chains. For instance, illegal gold mining in South America is not only a source of substantial revenue for OCGs but also involves forced labor and contributes to severe environmental degradation, making it a regulatory and ethical concern for companies indirectly connected to these operations.

2. The rise of cybercrime and digital threats

The digital age has opened new avenues for OCGs to exploit, with cybercrime emerging as a major focus. OCGs are increasingly engaging in ransomware attacks, data breaches, and various forms of online financial fraud. They leverage advanced technologies, including the use of cryptocurrencies, to facilitate money laundering, making their activities harder to trace. Additionally, the use of drones for smuggling contraband demonstrates how these groups are adopting modern technologies to enhance their operations and evade law enforcement. 

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3. Infiltration into legitimate industries

OCGs are also making inroads into legitimate industries, using these platforms to launder money and extend their influence under the guise of lawful business operations. Industries like agriculture and retail are particularly vulnerable. For example, in the avocado trade, some cartels in Latin America are exploiting this legitimate market to launder illicit gains. Similarly, the construction and real estate sectors have been targeted by OCGs as a means to integrate their criminal activities into the formal economy. This infiltration complicates the task for businesses trying to ensure their operations remain compliant and free from criminal exploitation.

How can I help my firm?

According to our survey of 600 senior compliance professionals, businesses are extremely aware of the risks of exposure to organized crime. 71 percent of organizations said they already undertake a detailed analysis of exposure to organized crime in their financial crime risk assessments, and 26 percent said they plan to undertake one in the next 12 months. 

However, our survey also showed that firms’ detection of real-time exposure appears to rely on diverse methods, with 38 percent of respondents emphasizing transaction monitoring, 31 percent payment screening, 16 percent human-led investigations, and 15 percent name screening. This suggests that firms do not see any one platform as sufficient on its own and are using multi-pronged approaches as they seek to identify and mitigate the risks.

While multi-pronged approaches demonstrate a commitment to combating organized crime, our survey highlighted several key challenges that hinder firms’ effectiveness. To overcome these obstacles and improve their defenses, you should focus on the following:

1. Prioritize data integration and analytics capabilities

While many compliance teams recognize the need to break down data silos to combat organized crime, the reality often falls short. Our survey revealed that 45 percent of respondents cited siloed datasets as a significant hurdle, hindering their ability to connect the dots and identify suspicious activity. Given the increasing sophistication and complexity of organized crime groups, this presents a unique opportunity for compliance leaders: to leverage this threat as a catalyst, educating internal stakeholders on the critical need for data consolidation and investment in advanced analytics. By building a comprehensive risk profile through integrated data, you can not only improve detection but also proactively anticipate and mitigate emerging threats.

Technology will play a major role in helping generate insights about potential OCG exposure, especially by leveraging integrated platforms and comprehensive risk data sets.”

Iain Armstrong, Regulatory Affairs Practice Lead at ComplyAdvantage

2. Enhance third-party due diligence processes

Given the infiltration of OCGs into legitimate industries, your team must rigorously scrutinize its third-party relationships. Continuous monitoring of suppliers, contractors, and partners is crucial. Enhanced due diligence (EDD) should include assessing financial stability, ownership structures, and compliance history. Automating parts of the due diligence process can help firms manage these complex relationships more efficiently and accurately, reducing their exposure to potential criminal activity.

3. Develop a dynamic risk assessment framework

A static risk management approach is inadequate in the face of evolving organized crime tactics. Rather, your team should adopt a dynamic risk assessment framework that incorporates real-time data and predictive analytics. By regularly updating risk models and conducting scenario analysis, you can better anticipate and respond to threats. 

Your team’s approach should go beyond tick-box compliance to thinking in detail about your company’s potential exposure to particular types of crime and crime typologies. Policies, procedures, and controls need to be agile, flexible, and open to recalibration.”

Iain Armstrong, Regulatory Affairs Practice Lead at ComplyAdvantage

4. Cultivate strategic partnerships and industry collaboration

Tackling organized crime requires a collaborative effort. Building strategic partnerships with industry peers, regulatory bodies, and law enforcement agencies can help you and your team stay ahead of criminal networks. Sharing information and best practices helps address the interconnected challenges of data quality, visibility, and integration identified in our report. In fact, in our survey, respondents were asked where the tightening of AML regulation would have the greatest impact on the fight against financial crime. The lead category was stronger public-private cooperation and data-sharing protocols (47 percent).

Participation in industry forums and information-sharing initiatives can help you remain informed about the latest trends and strategies, enhancing their collective ability to detect and combat organized crime.

The post How is organized crime changing – and what can I do to protect my firm? appeared first on ComplyAdvantage.

In AML, explainable AI is crucial for understanding why certain alerts are generated, the rationale behind blocking or allowing a transaction, or the factors contributing to identifying a high-risk client. This transparency helps financial institutions (FIs) meet regulatory expectations and build trust by allowing clients, auditors, and regulators to see how and why their AI-driven decisions are made, reducing the risk of misrepresentation and improving accountability.

However, in our 2025 State of Financial Crime survey, an interesting contradiction was revealed: while firms expressed confidence in understanding regulatory expectations around AI, 91 percent of respondents still felt comfortable trading off explainability for greater automation and efficiency, which could contradict the transparency increasingly expected by regulators. 

In light of this, compliance leaders need to find ways to ensure their AI tools meet both efficiency goals and regulatory requirements. This article explores:

• What explainable AI (XAI) is and why it matters in financial compliance;
• Regulatory expectations;
• Key benefits of XAI for improving transparency and accountability in AML;
• Challenges and limitations; and
• How ComplyAdvantage approaches explainable AI.

What is explainable AI (XAI)?

Explainable AI (XAI) refers to AI systems that make decisions and articulate the how and why behind those decisions. It is also a subset of responsible AI (RAI). While RAI covers the full spectrum of ethical and trustworthy AI practices, XAI focuses specifically on making AI systems transparent and their decisions understandable. For compliance teams, XAI is essential to ensure AI-driven outcomes are interpretable for stakeholders who need to validate and trust these systems.

To achieve this level of transparency, compliance teams often make use of specific XAI techniques, including:

• Rule-based models: Also known as “white-box machine learning models,” straightforward rule-based models apply predefined rules that make them naturally interpretable. For instance, flagging high-value transactions is a simple rule that compliance professionals can easily audit. However, due to a lack of nuance and flexibility, these models’ false positive rate is estimated at over 98 percent.
• Post-hoc explainability: While white-box models offer more transparency upfront, more complex “black-box models” tend to have higher predictive accuracy. These models often require post-hoc methods to allow compliance staff to trace risk scores back to specific input features like transaction type or customer history. 
• Hybrid approaches: Combining rules-based and AI-driven techniques, hybrid models leverage both static rules and adaptable algorithms to improve detection accuracy while maintaining transparency. This balance helps compliance teams capture more nuanced suspicious activity while keeping explanations clear and accessible.

How can XAI improve AML systems?

One of XAI’s primary benefits is its ability to improve alert quality and reduce false positives. Traditional AML models often generate large volumes of alerts, many of which are benign, overloading compliance teams and diverting resources from genuine risks. XAI provides more nuanced insights into each alert’s underlying factors, allowing FIs to refine their detection models and better prioritize cases that warrant deeper investigation.

XAI also significantly strengthens a firm’s ability to audit and justify decisions. For example, if a model identifies a suspicious transaction, explainable AI can articulate the factors driving the alert – such as transaction patterns or the customer’s historical risk profile. This detailed reasoning behind each alert enhances the institution’s readiness for regulatory scrutiny, as it enables compliance teams to confidently demonstrate why an alert was triggered and how their AML systems mitigate the risk of oversight or error.

One of our most practical uses of XAI is through our Advanced Fraud Detectors. Designed with client-facing transparency, the system goes beyond identifying suspicious behavior; it provides clear explanations for each alert, detailing the factors influencing the decision. This not only helps our clients justify actions but also enhances readiness for audits, as they can transparently address regulatory inquiries about specific alerts.

Iain Armstong, Regulatory Affairs Practice Lead at ComplyAdvantage

Moreover, explainability in AI models can help train and upskill compliance teams. When compliance staff understand the logic behind alerts, they can better analyze patterns and develop a deeper understanding of high-risk indicators. This helps build expertise across the team, enabling a more informed approach to AML beyond simple model outputs.

However, XAI doesn’t just enhance compliance and regulatory functions – it plays a crucial role in building better products. 

The relationship between XAI and product excellence is mutually reinforcing, and it’s difficult to achieve a great product without focusing on these elements. At ComplyAdvantage, our model risk management approach not only helps us control risk and deliver XAI to clients but also plays a key role in building high-quality AI systems that underpin best-in-class products and services.

Chris Elliot, Director of Data Governance at ComplyAdvantage

Regulatory expectations surrounding XAI

The regulatory landscape surrounding AI and machine learning is continually evolving, which can create uncertainty for organizations seeking to adopt XAI. As AI technologies become more integral to AML and broader compliance practices, regulators worldwide are sharpening their focus on transparency, fairness, and accountability in AI-driven systems. FIs adopting these technologies must stay attuned to emerging legal standards and best practices, which increasingly emphasize XAI. 

While jurisdictions like the European Union, United Kingdom, and United States have different regulatory frameworks, they share a common concern: ensuring AI applications are transparent and fair, especially in high-stakes sectors like AML.

European Union

Considered “the world’s first comprehensive AI law.” the EU AI Act establishes expectations for transparency, governance, and accountability in AI-powered AML solutions. Having entered into force in August 2024, the Act requires FIs to ensure transparency in their decision-making processes, including clarifying why certain transactions are flagged as suspicious. Additionally, the Act emphasizes the importance of robust data governance frameworks that mandate high-quality data to minimize inaccuracies and bias.

The EU’s General Data Protection Regulation (GDPR) complements these AI-specific standards by enforcing stringent data privacy controls that intersect with XAI goals. GDPR’s “right to explanation” gives individuals insight into automated decisions that affect them, a standard that dovetails with the transparency requirements of the EU AI Act.

United Kingdom

In the UK, the regulatory approach to AI in financial services has been shaped by institutions like the Financial Conduct Authority (FCA) and the Bank of England, which have jointly addressed the need for responsible AI. While the UK has not yet enacted an AI law on par with the EU’s AI Act (as of November 2024), these regulators have issued guidelines highlighting the importance of explainability, particularly for high-risk applications like AML.

The FCA has emphasized in its guidance that AI and machine learning applications must be interpretable, especially when they affect consumer rights or regulatory compliance. The FCA and Bank of England’s Machine Learning in UK Financial Services report underscores that FIs should implement governance frameworks that oversee AI models, including transparency around decision-making processes. They recommend that larger institutions lead by example in adopting advanced analytics, recognizing that smaller firms may face challenges in implementing these standards due to resource constraints.

Moreover, the UK’s proposed AI regulatory framework, introduced in the government’s National AI Strategy, encourages a “pro-innovation” approach that balances AI’s potential with ethical considerations like fairness and transparency. To do this effectively, the UK government has indicated plans to avoid “over-regulation” but will likely monitor AI developments and may impose stricter controls over time.

United States

No federal law specifically regulates AI in financial services in the United States. However, several agencies, including the Federal Reserve, the Office of the Comptroller of the Currency (OCC), and the New York Department of Financial Services (NYDFS), have issued guidance and policy statements that touch on AI transparency and fairness in FIs.

In 2024, the US Treasury Department also introduced a National Strategy for Combatting Terrorist and Other Illicit Financing, highlighting AI technologies’ transformative potential in enhancing AML compliance. This strategy underscores how AI can analyze vast amounts of data to uncover patterns related to illicit financing. Additionally, the Treasury issued a Request for Information (RFI) in June 2024, aimed at gathering insights on the use of AI in the financial sector, particularly concerning compliance with AML regulations. The RFI focuses on the opportunities AI presents and the associated risks, such as bias and data privacy concerns. This proactive stance indicates a commitment to shaping future regulations and guidance that prioritize transparency and fairness in AI applications within financial services​

Benefits of using explainable AI for AML

As FIs look to strengthen their AML capabilities, XAI presents a range of unique benefits that enhance operational efficiency and regulatory compliance, including:

• Reduced operational burden: By improving the quality of alerts and decreasing false positives, XAI helps compliance teams focus their resources on genuine risks rather than sifting through a high volume of alerts that require minimal action. This streamlining can lead to more efficient allocation of human resources.
• Stakeholder engagement: XAI can enhance communication with external stakeholders, such as regulators and clients. Clear explanations of the rationale behind decisions allow FIs to engage more effectively in discussions around compliance practices and risk management strategies.
• Support for continuous improvement: The insights gained from XAI can inform ongoing enhancements to AML strategies. By analyzing the reasons behind alerts and outcomes, organizations can refine their detection models and policies, fostering a culture of continuous improvement in risk management.
• Boosted client trust: FIs can foster greater trust in their AML practices by utilizing XAI. Transparency in decision-making reassures clients that their FI is diligent and fair in its monitoring processes.

Challenges and limitations of XAI

While XAI offers numerous benefits, its implementation is not without challenges. Organizations must navigate a complex landscape of technical, regulatory, and operational hurdles to integrate XAI into their compliance frameworks effectively. Understanding these limitations is essential for FIs as they seek to balance transparency with efficiency and effectiveness. Some primary challenges include:

• Data privacy: Ensuring privacy while maintaining transparency can be a delicate balance. For instance, GDPR regulations in Europe mandate strict data protection measures. Organizations must navigate these regulations while providing sufficient transparency in their XAI outputs, which can be challenging when personal data is involved.
• Bias: If the data used to train XAI models is biased or unrepresentative, the explanations generated may perpetuate existing biases, leading to inaccurate or unfair outcomes. In January 2024, the New York Department of Financial Services (NYDFS) raised concerns about algorithmic bias in financial technologies, emphasizing the need for fairness and accuracy in automated decision-making processes.
• Dependence on quality data: XAI’s effectiveness relies heavily on the quality and accuracy of the data it processes. Poor-quality data can lead to misleading explanations and decisions, undermining the very purpose of using XAI. Organizations need to invest in data governance and management practices to ensure their systems function correctly. 

How does XAI work in an AML system?

In AML, explainability can be integrated in multiple ways to create a transparent and auditable system, including:

1. Data ingestion and entity resolution: By consolidating information from multiple sources, AML systems can provide a comprehensive view of entities. XAI can play a critical role here, clarifying how entities are identified and linked to relevant data points, such as transactions, relationships, or risk indicators. For example, suppose an individual is flagged as high-risk due to adverse media. In that case, XAI systems can clearly break down these triggers, helping compliance teams trace data origins and make informed decisions.
   
   
2. Risk assessment and scoring: Risk assessments are a critical component of every AML program, involving the evaluation of data to determine the potential risk associated with specific entities or transactions. XAI facilitates this process by using explainable models that allow users to trace the factors influencing risk scores. As shown in the image below, these models provide a risk breakdown into how specific attributes contribute to an overall risk assessment. This level of detail is vital for compliance officers, as it empowers them to make decisions based on transparent criteria.
   
   
3. Alert generation with explainability: When an AML system generates alerts for potentially suspicious activities, XAI plays a crucial role in ensuring these alerts are accurate and accompanied by clear explanations. Each alert can include details about the specific risk indicators that triggered the alert, providing compliance teams with context and facilitating efficient follow-up actions. This transparency is essential for regulatory reporting and justifying decisions made in response to alerts.
   
   

Tips on how to tell if an AML vendor uses XAI

By asking the right questions, organizations can better assess the transparency and accountability of the AI solutions they are scrutinizing. The following questions, in particular, can serve as a good starting point for this discussion: 

• What methodologies does your AI system employ for decision-making?
  Understanding the underlying algorithms and models the vendor uses can provide insight into whether their AI is explainable. Ask if they utilize rule-based systems, interpretable models, or hybrid approaches that combine multiple techniques. A commitment to transparency in methodology is a good indicator of XAI practices.
• How do you provide explanations for risk assessments and alerts?
  Vendors should be able to articulate how their system generates explanations for risk scores and alerts. Ask for examples of how these explanations are presented to compliance teams, including the specific factors considered in the decision-making process. 
• Can your system demonstrate audit trails for decisions made by the AI?
  A robust XAI system should offer traceability for its decisions, allowing compliance professionals to review the rationale behind alerts and risk assessments. Inquire whether the vendor maintains detailed logs of the decision-making process, which can be critical during regulatory audits and compliance reviews.
• What measures are in place to ensure the fairness and accuracy of your AI models?
  Understanding how vendors address bias and accuracy in their AI systems is crucial. Ask about the data sources used for training their models, how they ensure data quality, and the steps taken to mitigate bias in decision-making. This is especially relevant in light of regulatory expectations for fairness in automated systems.
• How do you handle model updates and validation?
  Continuous monitoring and validation of AI models are essential to maintain their accuracy and reliability over time. Compliance professionals should ask how the vendor ensures their models are regularly tested and updated and whether they explain model performance changes.

How does ComplyAdvantage approach explainable AI?

At ComplyAdvantage, our approach to responsible AI, and by extension XAI, is based on and aligned with the Organisation for Economic Co-operation and Development (OECD)’s AI Principles and the UK’s “A pro-innovation approach to AI regulation: government response” whitepaper. Of the five core themes that lie at the heart of our understanding and implementation of responsible AI, two relate explicitly to XAI: 

• Transparency and explainability: This means we record and make information available about our use of AI systems, including purpose and methodologies.
• Safety, security, and robustness: This means decisions made by our AI systems are explainable, and explanations can be accessed and understood by relevant stakeholders (e.g., users or regulators).

This approach underpins the development of solutions that not only meet regulatory expectations but also deliver meaningful insights.

ComplyAdvantage believes that responsibly developing and managing AI is not only the right thing to do but also leads to better products that engage AI. Responsible AI is best when viewed as part of a best practice and thereby improves outcomes for our clients and their customers. In this way, it is aligned with business needs and not an external force acting on existing processes and competing with priorities.

Chris Elliot, Director of Data Governance at ComplyAdvantage

For more information on ComplyAdvantage’s approach to model risk management, read the full statement here. 

The post Enhancing AML efforts with explainable AI appeared first on ComplyAdvantage.

What is corruption?

Corruption is the misuse of power or position for personal benefit, usually involving acts like bribery, fraud, or the manipulation of resources. The World Bank describes corruption as “the abuse of public office for private gain,” emphasizing the misuse of authority for illicit purposes. Similarly, the Organisation for Economic Co-operation and Development (OECD) characterizes corruption as “a widespread phenomenon in international business transactions,” encompassing a wide range of unlawful activities, including embezzlement and influence peddling. While these definitions capture key aspects of corruption, they remain broad and somewhat vague, leaving room for interpretation and adaptation across different contexts.

This ambiguity is similar to the challenges seen with defining politically exposed persons (PEPs), where regulators and global standard-setters provide flexible but non-exclusive guidelines. The United Nations Office on Drugs and Crime (UNODC) has noted that the lack of a standardized, global definition of corruption stems from the “legal, criminological, and political problems” that arise when trying to harmonize approaches across various countries. This flexibility allows for a wider application of anti-corruption measures but also complicates efforts to create uniform legal frameworks across different jurisdictions.

The main causes of corruption

Just as there is no universal definition, corruption has no single root cause. The factors that drive corrupt activity vary widely depending on the context, shaped by political, economic, and social environments. This complexity makes addressing corruption challenging, as it often stems from a combination of influences rather than one isolated source. 

However, anti-corruption agencies and global standard setters like the Financial Action Task Force (FATF) and Transparency International have identified several recurring causes that tend to lead to corrupt practices. A non-exhaustive list of these causes includes:

• Poor governance: According to the FATF, a lack of accountability and transparency in public institutions can foster an environment conducive to corrupt practices. 
• Economic factors: Transparency International highlights that high levels of poverty and unemployment can compel individuals to engage in corrupt practices, particularly in regions with limited economic opportunities. 
• Weak legal frameworks: In its 2023 annual report, the same organization also identified inadequate laws and enforcement as critical factors facilitating corruption. 
• Political influence: Various anti-corruption organizations discuss how political influence can exacerbate corruption, especially when political figures engage in corrupt practices to maintain power and control. The United Nations Office on Drugs and Crime (UNODC) elaborates on this aspect in their academic resource, “Corruption and Comparative Politics.”

Types of corruption 

Understanding the different ways corruption can manifest is essential for effective governance and compliance frameworks. Several common types of corruption include:

• Bribery: The offering, giving, receiving, or soliciting of something of value to influence the actions of an official or other person in a position of authority.
• Embezzlement: The misappropriation of funds or property entrusted to an individual’s care, often involving public officials or employees.
• Fraud: Deceptive practices intended to secure unfair or unlawful gain, including falsifying documents or financial statements.
• Nepotism: Favoritism shown to relatives or close friends in hiring or promotion practices, undermining fairness in the workplace.
• Kickbacks: Payments made to someone in return for facilitating a transaction, often involving procurement processes or government contracts.

Despite the increasing sophistication of financial crime techniques, ‘old school’ corruption cases still appear. One example involved former US Senator Menendez, who was found guilty in June 2024 of accepting gifts from foreign governments that included literal gold bars. This underlines the need for firms to continue monitoring for both ‘traditional’ forms of corruption alongside newer methods driven by anonymized technologies and currencies. 

In a 2024 State of Compliance webinar, panelist David Hamilton noted how corruption and the regulations surrounding it are adapting to new challenges.

The relationship between corruption and money laundering

When public officials or individuals engage in corrupt activities, they are paid using money or assets they can’t easily use openly. This is where money laundering comes in – it allows criminals to clean and conceal these ill-gotten gains, making it harder for authorities to trace the funds back to their illegal origin. The FATF highlights that cracking down on money laundering is essential in fighting corruption, as it strips criminals of their profits and enables the prosecution of those involved in concealing dirty money.

Corruption is also officially recognized as a predicate offense to money laundering under the 6th Anti-Money Laundering Directive (6AMLD), meaning it’s one of the core crimes that generate illicit funds which then need to be laundered.

AML and counter-terrorist financing (AM/CTF) measures are crucial in identifying and recovering proceeds from corruption. In its assessments, the FATF evaluates countries’ ability to combat corruption through their AML/CTF frameworks, focusing on investigations, prosecutions, and the confiscation of assets linked to corruption-related offenses. 

This is highlighted in Recommendation 32, which calls for countries to demonstrate robust AML/CTF efforts in relation to corruption. A strong emphasis is placed on transparency, good governance, and efficient judicial systems to ensure corrupt officials cannot launder their illicit funds. As the global watchdog notes, “significant weaknesses or shortcomings in these areas may impede effective implementation of anti-corruption measures.”

High-profile examples of corruption

The FATF highlights several well-known examples of corruption in its 2011 Laundering the Proceeds of Corruption report. Although the report was published over a decade ago, many of the tactics used in these examples – from creating shell companies with complex ownership structures to bribing lawyers and other gatekeeper professionals – are still being used by corrupt officials today.

Augusto Pinochet: The former Chilean dictator is infamous for his authoritarian regime between 1971 and 1990, during which he was accused of serious human rights violations. In 2004, the US Senate investigated Pinochet’s financial activities and discovered that he, with the assistance of a US-based bank, diverted millions of public funds into personal accounts by creating offshore entities in tax havens like the Bahamas to conceal their origins. 

Vladimiro Montesinos: As the head of Peru’s intelligence service under President Alberto Fujimori (1990-2000), Montesinos orchestrated various corrupt schemes, particularly through defense contracts. According to a report issued by the Office of Transnational Issues, Montesinos allegedly received millions in kickbacks for setting up the purchase of 22 military aircraft from Belarus in 1996. According to Montesinos’ accomplice, Peru spent $20 million for each plane, when in reality they each cost $5 million. Montesinos pocketed the extra $15 million for each plane and set up shell corporations to launder the funds.

Jean-Claude Duvalier: Often referred to as “Baby Doc,” Duvalier ruled Haiti from 1971 to 1986. He embezzled millions of dollars from the Haitian government and funneled them into foreign bank accounts and investments. Lawyers played a critical role in disguising his wealth by holding accounts on behalf of the Duvalier family.

Frederick Chiluba: The former President of Zambia was accused of misappropriating substantial government funds during his time in office from 1991 to 2002. He utilized various law firms and set up complex corporate structures to obscure the flow of embezzled money. Courts noted that lawyers facilitated the movement of these funds into personal accounts and other investments, effectively laundering money that originated from government coffers. 

When is corruption not corruption?

Tune in as our expert panel explores how different jurisdictions interpret and handle corruption, unraveling the complexities that beg the question: when is corruption not corruption?

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Best practices to mitigate the risks of corruption

Due to the various forms corruption can take, there’s no single blueprint for combating it. However, firms can still take significant steps to mitigate risks by adopting a risk-based approach. By tailoring their strategies to specific threats and vulnerabilities, organizations can effectively guard against corruption. To mitigate exposure to corrupt activities, compliance teams can implement the following best practices:

• Screen clients for political exposure and sector risk to mitigate exposure to high-risk individuals and industries. PEPs are particularly vulnerable to engaging in corrupt activities, and comprehensive screening is crucial to help organizations manage these risks effectively.
• Use advanced risk-scoring models that dynamically assess customers and transactions based on factors such as political exposure, geographic risk, and industry vulnerabilities. Regular updates to these models ensure high-risk activities are flagged and mitigated in real-time.
• Implement enhanced due diligence (EDD) for high-risk clients and transactions, especially those linked to politically unstable regions or sectors prone to corruption. Thorough investigations into beneficial ownership and third-party relationships are crucial for identifying hidden risks.
• Tailor anti-corruption strategies based on a thorough understanding of regional political instability, regulatory environments, and local sector-specific risks. Regular assessments enable compliance teams to stay ahead of evolving corruption threats and adjust their frameworks accordingly.
• Adjust anti-corruption strategies by staying updated on global trends, including tightening oversight of key gatekeeper professions such as lawyers, accountants, and real estate agents who may facilitate illicit transactions. This aligns with guidelines from organizations like the FATF, which emphasize the role of intermediaries in enabling corruption and money laundering.
• Ensure anti-bribery and corruption (ABC) policies are embedded across the entire organization, from senior management to entry-level employees. This creates a unified responsibility for preventing corruption.
• Regularly train employees on ethical behavior and anti-corruption policies, ensuring they can identify and report suspicious activities. As the FATF recommends, a strong ethical culture is a crucial defense against corruption across all organizational levels.

To learn more about mitigating corruption risks, read our blog, How to strengthen anti-corruption frameworks: 4 actionable insights.

Mitigate corruption risks with advanced solutions

At ComplyAdvantage, we provide firms with market-leading global risk and compliance intelligence to help compliance teams effectively monitor illicit financial activities, including corruption. Our AI-powered data and intelligence layers include:

• Adverse Media: Our platform enables compliance teams to filter adverse media using categories aligned with the FATF’s predicate crime taxonomy, ensuring a focused approach to relevant corruption risks. With advanced natural language processing (NLP), our solution also helps firms minimize false positives, delivering accurate and relevant results.
• PEPs: We use a programmatic approach to update PEP data and election changes daily. This enables our customers to assess PEP risks accurately while utilizing our comprehensive knowledge graph, which maps relationships to relatives and close associates (RCAs) – crucial for identifying potential corruption links.
• Sanctions: Our solution enhances financial crime strategies with global sanctions data, focusing on entities controlled by sanctioned individuals. Thanks to our deep-tech approach, firms receive real-time updates, helping them stay ahead of compliance challenges related to corruption.

The post Understanding corruption and its relationship with AML appeared first on ComplyAdvantage.

But what makes managing corruption risk challenging for financial institutions (FIs)? With inconsistent definitions and diverging regulatory expectations, many firms have been left asking the question: when is corruption, not corruption?

While there is no universal blueprint for fighting corruption, a webinar in ComplyAdvantage’s 2024 State of Compliance series addressed this question, featuring experts from our Regulatory Affairs team, the Royal United Services Institute (RUSI), and law firm Howard Kennedy. This article highlights some of their key takeaways for firms looking to enhance their anti-corruption strategies.

1. Conduct regular risk assessments 

Regular and thorough risk assessments are essential to preemptively address corruption risks in different jurisdictions and across the various operations FIs manage. These assessments should focus on understanding the local regulatory environments and sector-specific risks that could affect firms’ operations.

Each region may present unique corruption risks depending on political instability, weak legal frameworks, or high bureaucratic complexity. For example, some countries may have less stringent anti-corruption laws or enforcement, making them higher risk for firms operating within those jurisdictions.

Risk assessments are the absolute bedrock of any effective financial crime system. How can you know what risks to deal with if you haven’t identified and properly assessed them?

David Hamilton, Partner – Business Crime and Regulatory at Howard Kennedy

By regularly assessing these risks, FIs can better tailor their anti-corruption strategies to address vulnerabilities specific to their business and avoid pitfalls associated with high-risk factors.

2. Adjust strategies based on global corruption trends

While risk assessments help identify vulnerabilities, adapting risk management strategies to reflect the latest global corruption trends is equally crucial. A key longstanding area of concern is the regulation of “gatekeeper professions” – including lawyers, accountants, and real estate agents – who play a vital role in preventing money laundering and corruption by acting as intermediaries in high-risk transactions. While their importance in preventing these illicit activities is not new, the context in which they operate continues to change year on year. The increasing complexity and globalization of financial transactions have expanded opportunities for corruption, making vigilant oversight of these professions more essential than ever. 

In June 2024, the Financial Action Task Force (FATF) highlighted gaps in the application of anti-money laundering (AML) regulations for these professions. The report revealed that while an average of 74 percent of member countries have implemented the FATF’s recommendations, major economies like Australia, the United States, and China scored zero percent. 

Corruption can happen in what you might think are the least likely places… It’s important to be cognizant of the environment that you’re operating in.

Tom Keatinge, Director, Centre for Finance and Security at RUSI

For FIs, these findings highlight the importance of understanding and mitigating corruption risks tied to different regions and professions. To stay ahead, firms should:

• Regularly assess global reports, including those from the FATF, and adjust country risk scores accordingly.
• Provide ongoing AML training for internal teams working with gatekeepers. 
• Conduct third-party due diligence on partners and consultants, especially in high-risk regions.

When is corruption not corruption?

Watch our expert panelists discuss the complexities of defining and managing corruption, focusing on regulatory differences and their impact on compliance and anti-corruption efforts worldwide.

Watch on-demand

3. Screen customers for political exposure and industry risk

In a year marked by 40+ global elections, there is an intense focus on managing risk around politically exposed persons (PEPs). This is predominantly due to the heightened risk of corruption PEPs pose due to their influence, access to public funds, and potential susceptibility to kickbacks and bribery. 

Screening customers for political exposure, both at the onboarding stage and continuously throughout the business relationship, is therefore essential for firms to mitigate the risk of being linked to corrupt activities. This screening helps firms create a more detailed and dynamic understanding of who they’re doing business with, allowing them to assess whether these individuals fall within their established risk appetite.

You should be screening both prospective and existing customers for political exposure… acquiring all of that information at the right time allows you to do more sophisticated monitoring.

Iain Armstrong, Regulatory Affairs Practice Lead at ComplyAdvantage

But PEPs are only one risk factor. Some industries are inherently more prone to corruption than others. For example, in Transparency International’s report on diagnosing the challenges of corruption in the pharmaceutical sector, the organization highlighted various risks presented by the industry, from companies bribing doctors to prescribe their medicines to government employees and introducing substandard medicines into the distribution system. Similarly, extractive industries, such as mining and oil, can introduce significant corruption risks related to granting licenses or concessions. 

Given these heightened risks, firms should ensure industry-specific factors are thoroughly assessed and appropriately weighted to influence their customer’s overall risk score.

4. Embed ABC policies across the entire organization

Due to the speed at which regulations are changing, it is no longer enough for firms to view anti-corruption and consumer protection as merely legal matters to be handled at the top levels of an organization. Historically, legal and compliance teams were tasked with guiding businesses through the complexities of regulatory obligations. However, there has been a significant shift in how firms are expected to manage these risks and this approach is now considered inadequate.

To build a robust anti-corruption framework, responsibility must be spread across the entire organization, from senior management to those who manage customer relations daily.  

Having ownership of anti-bribery and corruption (ABC) measures throughout the business, and not just leaving it to the board or Senior Management, is absolutely fundamental to ensuring you’ve got an effective risk management framework.

David Hamilton, Partner – Business Crime and Regulatory at Howard Kennedy

By ensuring all employees are accountable for upholding the firm’s ABC principles, businesses can better detect and prevent corrupt practices that might otherwise have gone unnoticed at higher levels of oversight.

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