Artificial intelligence is increasingly presented as a solution to one of the most persistent problems in regulated gambling: accurately measuring the black market. Supporters argue that AI can process vast data sets, detect hidden patterns, and deliver insights traditional methods cannot. Critics warn that AI may amplify existing biases and create a false sense of precision. The debate is no longer theoretical—it is shaping regulatory strategies across iGaming and sports betting.
Why Measuring the Black Market Remains So Difficult
Illegal gambling activity is designed to be invisible. Unlicensed operators avoid regulation, shift domains frequently, and operate across borders. Traditional measurement tools rely on incomplete proxies such as surveys, payment data, or enforcement reports.
Each method captures only part of the picture, making overall estimates unreliable. This is the gap AI is expected to close—but whether it can do so accurately is the core question.
How AI Is Being Applied to Black Market Analysis
AI systems are currently used to analyze large-scale datasets that would be impossible to process manually. These include web traffic patterns, payment flows, app usage data, and behavioral signals.
Common AI Use Cases in iGaming
Machine learning models can identify clusters of suspicious activity, track player migration between licensed and unlicensed platforms, and estimate market share based on probabilistic modeling. In theory, this provides a more dynamic and real-time view of the black market than static reports.
However, AI does not create data—it interprets what it is given.
The Promise: Speed, Scale, and Pattern Recognition
The strongest argument in favor of AI is its ability to scale. Unlike surveys or audits, AI models can continuously update estimates as new data arrives.
Key advantages often cited include:
- ability to process millions of data points simultaneously
- detection of non-obvious correlations across markets
For regulators dealing with fast-moving online gambling ecosystems, this responsiveness is highly attractive.
The Core Problem: Garbage In, Garbage Out
AI systems are only as good as their inputs. If the underlying data is incomplete, biased, or poorly defined, AI-generated outputs can be misleading.
Data Bias and Assumption Risk
Many AI models rely heavily on web traffic and payment data. This can skew results toward more visible illegal operators while missing private networks, crypto-based platforms, or local betting channels.
Without standardized definitions of what constitutes “black market activity,” AI may simply automate inconsistent assumptions rather than correct them.
Transparency and Explainability Concerns
One of the biggest regulatory challenges with AI is explainability. Policymakers need to understand how conclusions are reached, especially when those conclusions influence tax policy or enforcement priorities.
Black-box models that produce percentages without clear methodological explanations undermine trust. This is particularly risky in politically sensitive debates around gambling harm and market size.
Can AI Replace a Gold Standard—or Does It Need One?
AI is often framed as a replacement for traditional measurement frameworks. In reality, it may require an agreed-upon standard even more than human-led methods.
AI as a Tool, Not an Authority
Without a shared baseline—definitions, validation rules, and confidence intervals—AI outputs remain estimates, not facts. Used responsibly, AI can support decision-making. Used carelessly, it can create overconfidence in flawed numbers.
This is why many experts argue that AI should complement, not replace, structured methodologies.
Regulatory Implications of AI-Based Estimates
When regulators adopt AI-generated black market estimates, those numbers quickly influence licensing conditions, tax rates, and advertising restrictions. If the estimates are wrong, the consequences are systemic.
Overestimating the black market can justify excessive restrictions that weaken legal operators. Underestimating it can delay enforcement and expose players to unregulated risks.
The margin for error matters.
Industry and Operator Perspectives
Operators generally support data-driven approaches but remain cautious about AI-led conclusions being treated as definitive. Inconsistent or opaque models increase regulatory uncertainty and investment risk.
For the iGaming industry, predictability is as important as accuracy. AI that produces fluctuating estimates without clear explanations can destabilize markets rather than strengthen them.
What a Balanced AI Approach Looks Like
A credible approach integrates AI within a transparent framework. Models should be validated against multiple data sources, regularly audited, and openly documented.
This allows AI to enhance insight while remaining accountable—a critical requirement in regulated gambling environments.
The Bottom Line
AI has the potential to significantly improve how the iGaming industry understands the black market, but it is not a silver bullet. Without standardized definitions, transparent methodologies, and human oversight, AI risks amplifying uncertainty rather than reducing it.
The real debate is not whether AI can measure the black market, but whether the industry is ready to use it responsibly. In a sector where policy decisions carry high economic and social stakes, precision without accountability is not progress.