The new watchdog: Using AI to ensure compliance and conquer fraud in Malta’s financial and iGaming hubs

Prof. Alexiei Dingli is Professor of Artificial Intelligence

Malta’s standing as a hub for financial services and iGaming brings growth, jobs and international attention. Banks, payment providers and online gaming operators come here for a stable regulatory framework and a skilled workforce. That success also attracts risk. Financial crime, money laundering and problem gambling are constant threats. The volume and velocity of modern activity make it impossible for human teams to track everything unaided. Used well, artificial intelligence can act as a new line of defence by supporting human judgment rather than replacing it.

For years, many institutions relied on static anti‑money laundering rules. Threshold triggers generated a large number of alerts, overwhelming analysts and still missing sophisticated schemes. AI changes the approach. Machine‑learning models can study historic and live transactions to spot unusual patterns and adapt as criminal tactics evolve. In practice, this moves firms from box‑ticking to risk‑led monitoring. Alerts become more precise, and scarce compliance time goes to the cases that matter.

Onboarding is an equally important pressure point. Know Your Customer checks must be quick enough to avoid losing legitimate clients, yet robust enough to keep criminals out. AI tools help verify identities and assess risk by reading and cross‑checking documents, registry data, media reports and other public sources. Natural language processing can sift large volumes of unstructured information for red flags in seconds. The result is faster processing with a lower error rate. That said, the technology is only as good as the data behind it. Poor data or opaque models can introduce bias, so firms must combine efficiency with explainability and sound data controls.

The gaming sector remains one of Malta’s economic pillars and illustrates the balance required. The Malta Gaming Authority sets expectations on player protection, including self‑exclusion options, deposit limits and staff training. Enforcing these policies across thousands of players and multiple platforms is resource-intensive. AI can help by monitoring behaviour at scale. Real‑time analysis of gameplay can identify markers of harm, such as rapid increases in spending or time online. Where risk is detected, operators can trigger targeted interventions, for example, a cooling‑off prompt, a message that points to support services or a temporary limit on activity.

To achieve this, trustworthy use of AI is essential. Clear principles should guide deployment: accountability, fairness, transparency, data integrity, security and privacy. Operators need to keep humans in the loop and tell customers when automation is being used. Models should be tested for bias and audited regularly. Without a firm governance framework, technology can backfire and erode confidence among players and regulators alike. The goal is not surveillance for its own sake but timely, proportionate interventions that reduce harm.

Financial institutions face many of the same issues as operators. Traditional rules generate heavy workloads and too many false positives. Well‑designed models can cut the noise and improve detection rates. Predictive analytics allows teams to anticipate suspicious movements before losses occur. By aggregating insights across clients, products, and geographies, institutions can identify emerging risks earlier, including new laundering typologies or the misuse of crypto assets. These insights help firms allocate resources more effectively and support better policy-making by supervisors.

None of this removes the need for transparency. Regulators in Malta and abroad expect auditable decisions. If a system flags a client as high risk, a compliance officer must be able to explain why. That is harder with complex deep‑learning models. Firms, therefore, need to balance power with interpretability and reserve the most opaque techniques for use in cases where they can be explained and governed. Documentation, model validation and regular review are not box‑ticking exercises. They are the basis of defensible compliance.

People remain central. AI will not run a compliance function on its own. Teams need the skills to understand outputs, challenge assumptions and set thresholds that reflect real risk. Many firms have found value in embedding data specialists directly within compliance so that technical and regulatory expertise sit side by side. Training should cover data quality, model limitations and ethical use, not just tools. The strongest programmes pair technology with a culture that encourages staff to speak up when something does not look right.

Integrating AI into anti‑money laundering and responsible gaming frameworks is a significant undertaking. Models must be trained on representative, high‑quality data. Privacy obligations must be respected at every step. Bias has to be tested for and mitigated. There is competition for experienced data scientists, and compliance professionals are already stretched. Yet the prize is substantial: better detection, faster onboarding, lower operational cost and higher confidence among customers and regulators.

The choice for Malta is not whether to use AI, but how to use it well. The island has an advantage in its compact size, concentration of expertise and established regulatory infrastructure. If firms invest in specialist talent, strengthen governance and keep humans firmly in control, AI can help Malta’s flagship sectors set standards rather than chase them. That will protect consumers, deter criminals and reinforce the country’s reputation as a trusted, innovative jurisdiction.