Crypto Casino
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How casinos fight money laundering (AML)
Gambling has historically attracted regulatory attention due to the risk of money laundering (AML) and terrorism financing (CTF). A modern casino is not only games and payments, but also a full-fledged compliance system: risk assessment of customers and products, identity verification, transaction monitoring, investigations, reporting and staff training. Below is a practical map of the A-Z AML process.
Risk-based approach (RBA)
Risk map: customers (profile, geography, behavior), products (slots, live, rates, high-roller salons), channels (online/offline), payment rails (cards, A2A, cash, crypto).
Segmentation: baseline/elevated/high risk with associated controls.
Threshold triggers: amount/frequency of deposits and withdrawals, turnover rate, cross-border, new/abnormal payment methods, night activity.
Periodic review: at least annually, as well as after incidents/changes in business.
KYC/CDD/EDD: Who are we putting into the system
KYC (onboarding): confirmation of identity and age, verification of address, beneficiaries (for B2B/VIP), verification of the name coincidence with the payment method (closed-loop).
CDD: basic verification of all customers + sanctions/REP/negative media, simple revenue assessment.
EDD (in-depth): for VIP, high limits, complex geographies: Source of Funds/Wealth, additional documents, independent confirmations, interviews.
Periodic KYC-refresh: re-check by risk events or timelines.
Screening: Sanctions, PEP, negative media
Sanctions lists: national/international lists (automatic daily rescreening).
PEP/SoE: identification of politically exposed persons and associated risks/limits.
Adverse Media: media alerts to mention fraud, corruption, drug trafficking, etc.
Deduplication and data quality: normalization of full name, transliteration, coincidence by date of birth.
Real-time transaction monitoring
Rules and models: if-then hybrid + ML/anomaly analysis (device scoring, behavior, account-card-device-IP relationship graph).
Scenarios and limits: velocity control, day/week thresholds, cache in→mgnovennyy cache-out, "carousels" between payments.
Online signals: device change/geo, proxy/VPN, "even" intervals (bots), massive small deposits.
Alerts and queues: prioritization of cases by risk, SLA for analysis, feedback in the model.
Typical laundering schemes and how they are caught
Structuring (surfing): many small deposits below the thresholds → detection by frequency/clustering.
Chip dumping/" cash out" through the tables: agreed games for transferring funds → anomalies by return/mutual bets/IP.
Mules and networks of affiliated accounts: common devices/payment details/addresses → graph analysis, device-fingerprinting.
Cache-In-Cache-Out: Fast No-Play Output → Minimum Turnover Rules/Time Windows/Manual Review.
Cross-border overflows: deposits from high-risk countries, conclusions to other → geo-flags and limits.
Crypto risks: fresh addresses/mixers/" peel-chain →" address risk scoring, block lists, online analytics providers.
Investigations, SAR/STR and Escalations
Case management: fact collection, timeline, payment metadata, employee activity log.
Solutions: limit reduction, SoF/SoW request, freezing before clarification, account closure.
SAR/STR: submitting reports on suspicious transactions on time, prohibiting "tipping-off" (the player is not notified of the fact of the message).
Interaction with regulators/banks: secure channels, completeness of dossier, audit storage.
Payouts and closed-loop policies
Return by the same method: minimizing the risk of "washing out" through new details.
Limits on new recipients: "cool-off" period, manual verification of large amounts.
depozit→vyvod chain audit: matching names, documents, devices.
AML Technology and Architecture
Fichestor and data: uniform signs online/offline, real-time synchronization.
Tools: scoring engine (rules + ML), graph base, online analytics, sanctions/REP module, case management module.
Observability: p95 decision time on alert, false positive rate, SAR/STR count, time on KYC-refresh.
Reliability: fault tolerance, rule/model versioning, immutable logs.
Training and compliance culture
Training plan: onboarding + annual courses, exams, scenario training.
Roles and responsibilities: AMLCO/MLRO, analysts, support, risk committee, independent audit.
The speak-up principle: secure channels for reporting violations.
Data privacy and security
Minimization: Collect only what is needed for AML/RG.
Security: encryption, access control, DLP, segmentation of environments.
Shelf life and disposal: by law and license, then - safe disposal.
Transparency: notifications to the player about the purposes of processing, access/correction rights.
KPIs and AML Quality Metrics
Effectiveness: the share of prevented suspicious turnover, the quality of SAR/STR (regulator feedback).
Efficiency: FPR/TPR alerts, average investigation time, p95 on payment decisions.
Customer impact: proportion of customers with excessive friction, KYC application time, NPS after verification.
Governance: SLA compliance, audit results, percentage of recommendations implemented.
Common operator errors
1. They set it up once - they forgot: there are no RBA updates, the models are going sour.
2. Rules only, no data/ML: high FPR and queue clog.
3. Late SoF/SoW: Documents only asked on withdrawal.
4. A weak link with RG: affordability and AML go separately → the abuse window.
5. No closed-loop: conclusions to new details for no reason are a direct AML risk.
6. Poor documentation: no audit of actions and explainability of decisions.
AML Process Implementation/Update Checklist
1. Update RBA: Customer/Product/Channel Risk Matrix.
2. KYC/CDD/EDD: clear thresholds and lists of documents, re-KYC plan.
3. Screening: sanctions providers/POP + daily rescreening.
4. Transaction monitoring: hybrid of rules and ML, graph analysis, onchain module.
5. Payout-control: closed-loop, limits on new details, cooling.
6. Cases and SAR: Unified Case Management, SAR/STR Templates, Do Not Warn Customer Training.
7. Data and security: fichester, logs, access rights, encryption.
8. Training and audit: annual plan, tests, external/internal audit.
9. KPI boards: FPR/TPR, investigation time, SAR quality, impact on UX.
10. Degradation plan: manual overrides, backup screening providers, emergency procedures.
Mini-FAQ
How is AML different from KYC?
KYC - identification of the client at the entrance. AML is a broader framework: monitoring, investigation, reporting and risk management throughout the client's life cycle.
Is SoW always needed?
No, it isn't. More often for VIP/high limits and when the spending profile does not match income.
Is it possible to accept cryptocurrency and be compliant?
Yes, with targeted risk scoring, on-chain analytics, KYC and transparent exchange/withdrawal (and if permitted by license/law).
How to reduce false positive alerts?
Hybrid rules + ML, better-features (graph, behavior, device), A/B tuning of thresholds, feedback from analysts in the model.
How to combine AML and fast service?
Risk-based authentication: low-risk - seamless; medium - step-up; high - pause and EDD.
An effective AML in a casino is not a "tick for the sake of the regulator," but a strategic system: risks → data → rules + ML → investigations → reporting → training. Such a circuit simultaneously protects the business from sanctions and reputational losses, reduces financial risks, helps the responsible player and makes operations resistant to constantly changing laundering schemes.