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Human trafficking is one of the most pervasive and insidious crimes in the modern world, and its lifeblood is financial. Yet, its operations are often made invisible through financial systems: cleverly disguised transactions, front companies, and the misuse of legitimate services.
While it is fundamentally a humanitarian crisis, human trafficking is also a financial crime. Its success depends on laundering illicit funds and making dirty money appear clean. From cross-border transactions to the creation of synthetic identities, traffickers depend on the global financial system to obscure and legitimise their profits.
Combatting human trafficking is not just a moral obligation, it is a regulatory and reputational priority. With the right technology, processes, and people in place, we have the power to stop illicit funds from fuelling this industry. And the most effective weapon in this fight is rapidly becoming artificial intelligence (AI).
Listen to podcast:Using AI to Combat Fraud used in Human Trafficking | Discussing New Approaches
The financial architecture of human exploitation
Contrary to outdated stereotypes of laundering through duffel bags of cash, today’s trafficking networks rely on complex and digital means to move illicit funds. Shell companies, prepaid debit cards, small-scale cross-account transfers, and the misuse of legitimate platforms all play a role.
Traffickers don’t just move money - they manipulate it. They use methods designed to blend into the financial background noise, making it difficult for traditional detection systems to spot.
Crucially, these funds often flow through accounts of unwitting participants, many of whom are victims themselves. In this context, the challenge for financial institutions is not just to detect crime, but to distinguish between criminal behaviour and vulnerable individuals coerced into financial roles.
Three ways AI helps identify hidden risks
Traditional transaction monitoring systems use rules to flag suspicious activity based on amount thresholds, geographies, or keywords. While these systems have value, they are increasingly ineffective in detecting modern laundering techniques, especially those linked to human trafficking.
AI allows us to go further.
By analysing vast volumes of transactional and behavioural data, AI models can detect subtle anomalies and patterns that are otherwise invisible. These include:
1) Changes in spending behaviour that may suggest coercion or third-party control. Equally important is detecting the early signs of individual coercion. For example, an account that suddenly begins receiving transfers from unrelated parties or exhibiting erratic withdrawal behaviour may belong to someone being used as a money mule, often unknowingly. AI allows us to see those micro-patterns: subtle deviations in behaviour that wouldn’t raise alarms in isolation, but together tell a bigger story.
2) Unusual account relationships, such as new payment recipients or clusters of accounts interacting in novel ways
3) Emerging laundering typologies, identified not by static rules but by real-time behavioural analysis. AI gives institutions the ability to detect criminal typologies that don’t yet have names. Traffickers continuously adapt to bypass controls, whether that’s through legitimate-looking delivery businesses established during the pandemic, or short-term cash-intensive operations designed to facilitate rapid fund cycling.
Importantly, we ensure these AI models are built with human oversight. Analysts need transparency into why a transaction is flagged so they can make confident, compliant decisions.
The quantum horizon and the need for ethical vigilance
While financial institutions are still integrating AI into their AML strategies, another technological frontier is emerging: quantum computing. The UK’s recent £121 million investment in quantum technology signals the next phase in data processing capabilities.
Quantum computing could one day allow institutions to process and correlate AML data at speeds and complexity far beyond current capabilities, revealing criminal networks at scale.
Unprecedented speed in detecting hidden patterns: Quantum algorithms can evaluate millions of transaction permutations simultaneously, accelerating the detection of networks that operate across borders and institutions.
Predictive typology development: Quantum machine learning models may one day identify laundering typologies that haven’t yet been formally defined, providing early warning signals based on data correlation and behavioural anomalies.
Cross-institutional collaboration: Quantum’s processing power could support secure, privacy-preserving analytics across multiple financial institutions, enabling a more united front against global trafficking networks.
Post-quantum encryption: As criminals also seek out emerging technologies, quantum-resilient encryption will be vital to protect the confidentiality and integrity of compliance systems.
Financial institutions must begin planning for this now by engaging in cross-sector discussions on ethical use, data governance, and how best to prepare systems and teams for what quantum could bring.
The cost of irresponsible deployment
According to the Napier AI AML Index, money laundering costs the global economy $5.2 trillion USD annually. A significant portion of that supports human trafficking and other predicate crimes. Regulators are responding accordingly raising expectations for proactive detection, robust risk assessments, and the use of advanced analytics.
As with any powerful tool, quantum computing comes with risk. In the wrong hands, it could supercharge criminal operations, undermining the very defences we build.
That’s why now, not five years from now, is the time for financial institutions to start thinking about how they will govern and control these capabilities. We must learn the lessons from AI: regulation must evolve in parallel with technology, and we must prioritise transparency, ethics, and public trust at every stage.
While human trafficking is not a problem we can solve overnight, every suspicious transaction we catch, every pattern we uncover, and every victim we protect is a step in the right direction.
Ready to explore ways to implement AI into your compliance systems?
Read our 12 step guide explaining deployment, training and maintenance of AI models for financial crime compliance
Photo by Maxim Hopman on Unsplash
