Financial crime, and money laundering in particular, remains one of the more persistent and elusive threats to economic stability. It’s a world where obfuscation is the rule, not the exception. Criminals are inventive; they learn, they adapt. Law enforcement and regulators, meanwhile, must continuously refine their tools and methods just to keep pace. Amid this ongoing contest, the role of ISIC codes may seem, at first, almost incidental—a matter for statisticians or compliance officers, perhaps. Yet on closer inspection, these codes have become a crucial weapon in the fight against illicit finance.
At the surface, ISIC codes do nothing more than classify economic activities: agriculture, real estate, manufacturing, financial intermediation, and so on. But the truth is, these classifications are the scaffolding for risk assessment in both public and private sectors. Financial institutions, obliged to “know their customer,” use ISIC codes to compare a company’s transaction activity against established industry patterns. Law enforcement agencies rely on these codes to prioritize investigations—sometimes with remarkable effect.
Let’s take an example. Suppose a business classified under ISIC code 6810—“Real estate activities”—is suddenly flagged for a series of large, round-dollar international wire transfers. If those transfers seem disproportionate to the typical volume or frequency for the real estate sector in that jurisdiction, alarm bells begin to ring. Why? Because, as experience has shown, real estate is often a conduit for money laundering: large sums can be moved quickly, assets are difficult to value precisely, and regulatory oversight can be uneven. By using the ISIC framework, financial institutions and regulators can set up industry-specific thresholds, making it much harder for suspicious activity to blend into the background noise.
It’s not just about real estate, of course. Wholesale trade, casinos, precious metals, and certain segments of professional services—each has its own risk profile. The Financial Action Task Force (FATF), for example, regularly highlights sectors that merit closer scrutiny. ISIC codes offer a shared vocabulary for discussing, regulating, and, crucially, automating these risk distinctions. When combined with machine learning and data analytics, the potential becomes even more apparent.
Here’s where things become both technical and interesting. Machine learning models, tasked with monitoring millions of transactions daily, need a framework for distinguishing “normal” from “anomalous.” ISIC codes serve as the backbone of this process. By training algorithms on historical data, incorporating the codes alongside transaction size, frequency, geography, and counterparty information, models can spot deviations—sometimes subtle, sometimes glaring—from what one would expect given a business’s declared activity. If a construction company (ISIC 4120) starts receiving payments in patterns more typical of high-volume retail (ISIC 4711), it’s worth a second look. Not every anomaly is criminal, of course, but the codes help narrow the haystack.
Still, as with any system, there are limitations. Criminals are not standing still; they can and do attempt to circumvent controls by misclassifying their businesses, setting up shell companies, or moving illicit activity into sectors with weaker oversight. Regulators, therefore, have to be vigilant not just in monitoring transactions but in verifying the underlying accuracy of ISIC assignments. Company registries, trade databases, and even cross-border data exchanges become sources of intelligence in their own right.
There’s an argument, and I think it’s a good one, that much of the value in ISIC-based anti-money laundering (AML) systems lies in their adaptability. As new risks emerge—say, the sudden appearance of online gaming platforms, or a surge in cryptocurrency exchanges—regulators can update risk models simply by refining which ISIC codes are flagged for enhanced scrutiny. The framework is flexible enough to accommodate both broad trends and localized risks.
It’s also worth noting the potential for improvement. Many current risk-scoring methodologies, while sophisticated, still rely on relatively static models: sectoral risk is assigned in advance and updated infrequently. There’s a case to be made for more dynamic approaches—ones that leverage real-time trade flows, cross-referenced against company registry updates and the latest law enforcement intelligence. For example, a spike in imports of high-value goods by newly-registered companies, all sharing a particular ISIC code, could prompt earlier intervention. Similarly, trade data anomalies—unusual spikes in declared shipments or changes in trading partners—might suggest typologies that existing models miss.
What about policy reform? The challenge, as always, is balance. Enhanced due diligence is costly, both for the private sector and for public agencies. Overly aggressive risk scoring can burden legitimate business, especially in sectors already struggling with compliance fatigue. But the cost of doing too little is higher still. By incorporating ISIC codes—continuously refined and cross-validated—into the core of risk assessment, authorities can allocate resources more intelligently. Some jurisdictions, particularly those with advanced data infrastructures, are already moving in this direction. Others lag behind, hampered by fragmented data or inconsistent implementation.
As someone who has watched these systems evolve over the years, I’m struck by a recurring lesson: the effectiveness of ISIC-based analysis is only as good as the underlying data and the commitment of institutions to act on it. Machine learning models, for all their promise, are not silver bullets. They are tools—sometimes blunt, sometimes precise—that depend on the skill of their users. It’s not glamorous work, most days. But it is essential.
ISIC codes may never capture the public imagination. They are, after all, administrative constructs. But in the hidden world of financial crime prevention, they have become indispensable. When regulators, financial institutions, and technologists use them well—refining, updating, challenging their own assumptions—they give society a better chance to defend itself against those who would exploit the system’s blind spots. And if there’s one thing history teaches, it’s that the battle against financial crime will always require both vigilance and adaptation. ISIC data, for now at least, belongs at the heart of that effort.