Political instability is a significant catalyst for financial crimes, particularly in the context of Anti-Money Laundering (AML) and Counter-Terrorism Financing (CTF) efforts. As political instability undermines governance, it enables money laundering and terrorist financing by weakening institutional oversight and enforcement of financial regulations. In regions marked by turmoil, the erosion of governance structures and rampant corruption create fertile ground for illicit financial activities to thrive. Historical and contemporary analyses reveal a troubling pattern: countries experiencing political instability often witness a surge in money laundering and terrorist financing activities. Understanding the relationship between political instability and AML/CTF risks is crucial for developing effective global strategies to combat financial crimes.
Understanding political instability
The World Bank defines political instability as “the propensity of a government collapse either because of conflicts or rampant competition between various political parties.” There are several factors that can lead to political instability. These can be categorised into internal factors—those contained within the nation itself—and external factors arising from the international context. Internal factors include economic inequality, poverty, corruption, weak governmental institutions, ethnic tensions, regime changes, military coups, and even terrorism. External factors may include foreign interventions, covert operations, global economic shocks, and the forces of globalisation.
Politically unstable environments are characterised by frequent governmental changes, weakened regulatory frameworks, and increased reliance on informal financial systems. These conditions provide favourable opportunities for criminals and terrorist organisations to move, conceal, and utilise illicit funds with minimal detection.
Several real-world examples illustrate how political instability contributes to the rise in money laundering activities. This article focuses on jurisdictions added to the FATF’s Grey List (those under increased monitoring) and the political context surrounding their listing.
Case Studies
Burkina Faso (2021)
Around the time of its grey listing, Burkina Faso had been grappling with a series of terrorist attacks and massacres committed by jihadist groups in the North. After the ousting of Blaise Compaoré in 2014, the country experienced a succession of military coups in January and September 2022, along with a rise in insurgency and terrorist violence. This political instability weakened the state’s capacity to govern effectively, particularly in the security sector. The absence of strong governance left large areas unprotected, allowing jihadist groups to expand their influence and exploit the country’s porous borders for illicit activities. The weak institutional framework struggled to monitor financial transactions, leading to a significant rise in money laundering (ML) and terrorist financing (TF), ultimately resulting in Burkina Faso’s grey listing by the FATF in 2021.
Nigeria (2023)
From 2019 onward, Nigeria grappled with increasing political tensions and instability, especially after the 2019 general elections, where Muhammadu Buhari was re-elected amid allegations of vote-rigging and electoral violence. These tensions intensified leading up to the 2023 presidential elections, which saw Bola Tinubu declared the winner amidst widespread disputes. At the same time, Nigeria faced a worsening economic crisis characterised by high inflation and a growing debt burden. Proposals to remove fuel subsidies inflamed public discontent, exacerbating the hardships faced by ordinary Nigerians. Throughout this tumultuous period, pervasive corruption and financial mismanagement severely undermined effective governance, contributing to Nigeria’s grey listing by the FATF in 2023.
Syria (2011)
In 2011, Syria was plunged into widespread political instability as protests erupted against President Bashar al-Assad’s regime, inspired by the broader Arab Spring movement. What began as peaceful demonstrations quickly escalated into violent crackdowns, leading to a full-scale civil war. The Syrian government faced international condemnation for human rights violations, and the country’s economy deteriorated rapidly due to the unrest and subsequent sanctions. Amid this chaos, Syria was grey-listed by the FATF in 2011 for failing to implement effective measures against terrorist financing, as weak financial controls allowed illicit financial flows linked to terrorism and organised crime to flourish.
Haiti (2021)
Following the devastating 2010 earthquake, Haiti struggled to recover despite international aid efforts. Successive governments faced allegations of corruption and mismanagement of recovery funds, deepening public distrust. Politically, the country experienced cycles of instability, culminating in the assassination of President Jovenel Moïse in July 2021. This event created a power vacuum and intensified the already dire security situation, ultimately leading to Haiti’s addition to the FATF’s Grey List. The lack of effective governance and rampant gang violence allowed illicit activities to proliferate, undermining efforts to combat financial crime.
Lebanon (2023)
In the years leading up to 2023, Lebanon faced an escalating crisis driven by economic collapse and political instability. The economic situation began deteriorating in 2019, with mass protests erupting against the ruling elite, who were blamed for rampant corruption and failing infrastructure. This culminated in the resignation of Prime Minister Saad Hariri, leaving the government paralysed. The devastating 2020 Beirut port explosion exacerbated the situation, further weakening Lebanon’s economy. The prolonged presidential vacuum following the end of President Michel Aoun’s term in 2022 left the country without effective leadership, ultimately resulting in Lebanon’s grey listing by the FATF in 2023. The lack of oversight and regulatory weakness hindered efforts to combat money laundering and terrorist financing.
While these cases vary in context and scale, they demonstrate that a decline in political stability often signals future governance issues, which could, in turn, predict higher ML/TF risks. Acknowledging that not all politically unstable regions experience the same level of financial crime risk, there are instances where effective governance has led to successful AML measures. Early warning systems that track political and economic instability might offer valuable insights into jurisdictions at higher risk of financial crime, allowing governments and international bodies to take pre-emptive action. By strengthening regulatory support and institutional resilience in unstable regions, it may be possible to mitigate the potential surge in money laundering and terrorist financing activities.to mitigate the potential surge in money laundering and terrorist financing activities.
Can AI and Machine Learning Help Predict AML/CTF Risks?
Given the strong correlation between political instability and the rise of money laundering and terrorist financing, predicting these risks has become a critical focus for global financial crime prevention efforts. Emerging technologies, particularly AI and machine learning, offer powerful tools for addressing these challenges. AI and machine learning can process vast datasets and identify complex patterns that human analysts might miss.
Machine learning models can be trained on a combination of historical data—such as political instability, corruption indices, economic trends, and AML/CTF case data—to predict future risks in specific regions. By continuously learning from new data, these models improve their accuracy over time, allowing financial institutions and regulators to anticipate where AML/CTF risks may escalate.
In regions with weak governance, where traditional oversight mechanisms may be compromised, AI-powered systems can monitor financial transactions in real time. These systems use advanced algorithms to flag unusual activity and detect hidden networks of illicit financial flows. AI can also assist regulators in prioritising oversight efforts by generating risk-based alerts and focusing on areas most susceptible to financial crime.
While these technologies are not a complete solution and present several limitations—including ethical concerns and data protection issues—they significantly enhance the ability to predict and mitigate AML/CTF risks in politically unstable environments by providing proactive, data-driven insights into potential threats. However, the implementation of AI and machine learning can be costly, often requiring significant financial investment that politically unstable countries may lack. Furthermore, infrastructure challenges, such as unreliable electricity and internet access—as seen in Lebanon—can hinder the effective deployment and sustainability of these technologies. Without adequate resources and infrastructure, even the most advanced tools may fail to provide the intended support in combating financial crime.
TenIntelligence Thoughts
As political instability continues to shape the global financial landscape, financial institutions must not only recognise the risks but also equip themselves with advanced tools to combat them. Strengthening internal capacities and leveraging innovative technologies will be crucial in navigating these challenges and effectively combating financial crime in regions where governance is fragile.
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Written by
Riwa Haidar