MGA Interim Report 2025: What It Signals for AI in iGaming

Malta’s iGaming industry doesn’t grow because it’s loud. It grows because it’s governed.

The Malta Gaming Authority’s Interim Performance Report for January to June 2025 reads like a scoreboard for regulatory work: 28 new licence applications, 8 licences issued, 723 criminal probity checks, 87 thematic reviews, 1,720 player assistance requests resolved, €139,360 in administrative penalties, and one licence cancellation. Those aren’t just compliance numbers. They’re signals.

If you’re building or scaling AI in iGaming in Malta—whether that’s multilingual content, player comms, marketing automation, risk scoring, or safer gambling tooling—this report tells you the environment you’re operating in: active supervision, measurable enforcement, and a market that rewards operators who can prove control.

What the MGA’s H1 2025 numbers really say

Answer first: The report shows a regulator that’s doing high-volume, high-specificity oversight—exactly the kind of ecosystem where AI can help operators stay compliant without slowing down growth.

A lot of people treat “regulated market” as code for “slower market.” Malta’s data points suggest the opposite. The MGA is processing licensing and renewals, running audits and reviews, and handling large volumes of player-related cases—while also coordinating integrity work internationally.

Here are the signals worth paying attention to:

• Licensing is active, not static: 28 applications and 8 licences issued in six months indicates steady interest and ongoing market entry.
• Fit-and-proper is not a formality: 34 Fit and Proper Committee decisions, with 4 negative outcomes, plus 2 rejections by the Supervisory Council after false/misleading submissions.
• Supervision is continuous: 7 full-scope compliance audits plus 87 thematic reviews across compliance, player protection, and betting integrity.
• Player protection is workload-heavy: 1,720 assistance requests resolved (including carry-over). That’s a lot of human time unless you systemise triage.
• Enforcement has teeth: 23 cease and desist letters, 15 warnings, 23 administrative penalties, and one licence cancellation.

This matters because AI adoption in iGaming often fails for one boring reason: the operator can’t evidence governance. Malta’s posture makes evidence non-negotiable.

Licensing and probity checks: where AI can help without creating risk

Answer first: AI can speed up internal due diligence and documentation—if you keep it strictly assistive and fully auditable.

The report notes 723 criminal probity screening checks on authorised persons, qualifying interest holders, directors, key persons, and funding/control third parties. That number is a hint about how much scrutiny is concentrated around “who controls what” and “who is responsible.”

Practical AI use cases that fit a Maltese licensing reality

If you’re an operator or supplier in Malta, you can use AI to reduce friction in the licensing lifecycle while staying on the safe side of governance:

1. Document intelligence for licensing packs

   • Auto-classify corporate documents, policies, and evidence attachments.
   • Summarise long procedures (AML, responsible gaming, complaints handling) into consistent internal briefs.
   • Flag missing artefacts before submission.

2. Consistency checks across submissions

   • Detect mismatches between application data, org charts, and policy ownership.
   • Track version control so you can show exactly what changed and why.

3. Board and key function readiness

   • Generate role-based checklists (director, key person, MLRO support functions).
   • Produce “interview prep” packs based on your own policies (not generic templates).

My stance: If your AI system can’t produce an audit trail—inputs, outputs, human approvals—it’s not “smart.” It’s operational debt.

Supervision, audits, and thematic reviews: AI as your evidence engine

Answer first: The MGA’s audit-and-review volume makes AI most valuable as a compliance evidence layer: logging, testing, and explaining controls in plain language.

The report highlights 7 full-scope compliance audits and 87 thematic reviews. Thematic reviews are especially relevant for AI strategy because they target specific risk areas (player protection, integrity, compliance). That means operators need to demonstrate control not just in general, but in detail.

Where AI genuinely reduces audit pain

• Control testing automation: Build scripts and ML-assisted checks that test limits, self-exclusion logic, KYC completeness, and bonus rule application across samples.
• Policy-to-implementation mapping: Use AI to map each policy clause to system settings, Jira tickets, and release notes.
• Incident and exception analysis: Cluster repeated player issues (e.g., deposit limit confusion) and show remediation.

Done properly, AI doesn’t “argue” compliance. It organises evidence so compliance teams can explain it.

A simple operating model that works

If you’re integrating AI into compliance ops, keep it structured:

1. AI proposes (summaries, flags, anomaly clusters)
2. Human decides (approval, escalation, remediation)
3. System records (who approved what, when, and why)

That third step is where many teams fail. In Malta, it’s the difference between “nice internal tool” and “audit-ready capability.”

Player protection workload: AI for multilingual support and safer gambling

Answer first: The report’s 1,720 player assistance resolutions point to a scalable need: faster triage and clearer communication, especially across languages.

This blog series is about how intelliġenza artifiċjali is transforming iGaming and online gaming in Malta—particularly through multilingual content, marketing automation, and player communication. Player support is the most immediate place where AI can deliver value without touching game outcomes.

How AI improves player assistance without crossing lines

• Triage and routing: Classify incoming tickets by category (payments, limits, self-exclusion, bonus disputes) and urgency.
• Multilingual first response drafts: Generate Maltese/English/Italian/Spanish drafts based on approved templates and policy.
• Safer gambling nudges: Detect patterns in messages (“I can’t stop”, “help me close my account”) and route to trained agents.

A useful rule: AI can speed up communication; it shouldn’t decide player harm outcomes. Humans should approve high-impact actions (closures, refunds, exclusions), but AI can make sure those cases surface fast.

What to measure (so it’s not vague)

If you’re pitching AI internally, tie it to numbers your ops team cares about:

• First response time (FRT)
• Time to resolution (TTR)
• Re-open rate
• Quality audits (template adherence, correct policy references)
• Language coverage without increasing headcount

AML/CFT and MLRO readiness: AI helps, but governance must lead

Answer first: AI is useful for AML/CFT monitoring and documentation, but only when it strengthens explainability, not opacity.

The report notes 11 AML/CFT Compliance Examinations initiated and 11 concluded, plus 3 licensees under remediation measures. It also references 16 interviews on prospective MLROs.

This tells you something important: Malta expects AML capability to be real, staffed, and understood—not outsourced to a black box.

AI use cases that typically pass the “common sense” test

• Alert quality improvement: Use machine learning to reduce false positives while keeping rule-based logic as a baseline.
• Narrative drafting for SAR/STR support: Produce structured case summaries from transaction timelines (with analyst review).
• Training simulations: Generate scenario-based quizzes based on your own risk assessment and procedures.

What usually backfires

• Vendor tools that can’t explain why an alert was raised
• Models trained on irrelevant jurisdictions without calibration
• AI-generated policies that look polished but don’t match actual system settings

If you want to be taken seriously by both compliance and product teams, your AI strategy for AML should include model documentation, validation routines, and a clear human escalation chain.

Integrity, suspicious betting, and unauthorised URLs: AI for detection and response

Answer first: The report shows a live integrity battlefield—AI helps most in detection, correlation, and fast evidence packaging.

The MGA received 149 suspicious betting reports from licensees and shared 88 alerts after risk-based filtering, participating in 30 investigations globally. It also reviewed 75 URLs tied to unregulated activity, finding 34 with fraudulent references.

Where AI fits naturally

• Fraudulent brand/reference detection: Crawl and classify pages that misuse MGA or licensee branding.
• Suspicious betting pattern recognition: Detect correlated accounts, syndicates, or odds-manipulation signals.
• Case file automation: Compile timelines, account links, and event correlations into consistent investigation packs.

The operators that do well here aren’t the ones with the fanciest models. They’re the ones who can answer:

“What did you see, when did you see it, what did you do, and can you prove it?”

A practical checklist for AI teams building in Malta’s regulated iGaming market

Answer first: If you can’t demonstrate control, documentation, and human oversight, your AI project will stall—either in audit, procurement, or internal risk review.

Use this as a starting point before you ship anything AI-adjacent:

1. Define the decision boundary

   • What is AI allowed to suggest vs decide?

2. Create an audit trail by default

   • Log prompts/inputs, outputs, user approvals, and downstream actions.

3. Lock templates and tone for multilingual player comms

   • Keep responses consistent with responsible gaming and complaints policies.

4. Validate models against your own risk profile

   • Malta-facing operations aren’t the same as unregulated markets.

5. Build “show me” dashboards

   • Compliance teams need explainability, not model performance charts.

6. Prepare for thematic reviews

   • Treat player protection, integrity, and AML as continuous test areas.

Where this is heading for 2026 (and why you should care now)

The MGA will publish the full-year industry performance report in the second half of 2026 with the annual report for the financial year ending 31 December 2025. That timeline matters if you’re planning budgets now: procurement cycles and platform changes don’t happen overnight.

Here’s the bet I’d make for Malta’s iGaming landscape: the winners won’t be the teams that “use AI.” They’ll be the teams that can prove their AI is controlled, fair, and aligned with player protection—at scale, across languages.

If you’re working on AI for multilingual player engagement, automated marketing, or safer gambling in Malta, the MGA’s interim report is your reminder that innovation and regulation aren’t enemies. They’re the operating system. What you build on top of it is up to you.