Crypto Casino
Torrent Gear
AI analysis of player behavior and fraud protection
Gambling is an environment with high transaction speeds, micro-margin and constant pressure from cybercriminals: multiaccounting for bonuses, arbitration "teams," account hijacking (ATO), "chargeback teams," cashing schemes through P2P and crypto. The AI approach combines events from payments, gameplay and devices into a single behavior model in order to predict risk in real time and automatically apply measures - from soft limits to hard blocking. Below is a system guide for data, models, architecture and metrics.
1) Basic fraud scenarios
Multiaccounting (Sockpuppets): registration of a "family" of accounts for bonuses/cashback, laundering through mutual bets/tournaments.
Bonus abuse: "stuffing" into promo windows, splitting deposits, "deposit-bonus-minimum wager-output" cycles.
ATO (Account Takeover): theft through phishing/password leaks, logins from new devices, a sharp change in behavior.
Payment fraud/chargebacks: stolen cards, "friendly fraud," cascades of small deposits.
Collusion and chip dumping: collusion in PvP/poker, translation of EV from "merging" to "withdrawing."
Laundering (AML risks): fast input-minimum activity-output cycles, fiat/crypt arbitration, atypical routes.
2) Data and features: what behavior is built from
Transactions: deposits/withdrawals, cancellations, cards/wallets, chargeback flags, speed "depozit→stavka→vyvod."
Gaming events: time structure of bets, markets, odds, ROI/volatility, participation in tournaments/missions.
Devices and network: device fingerprint, User-Agent stability, cursor/touch behavior, IP-AS, proxy/VPN, time to 2FA confirmation.
Account: account age, KYC stage, matches on addresses/phones/payments.
Socio-graph features: common devices/payment tools, refcodes, common IP/subnets, input sequences.
Context: geo/time zone, promo calendar, traffic type (associate/organic), country/payment method risk.
Examples of features:- Session-based: session length, frequency of micro-rates, pauses between events, abnormal "ideality" of timings.
- Velocity features: N deposits/rates per X minutes, password login/reset attempts.
- Stability features: share of sessions with the same device/browser, fingerprint stability.
- Graph features: degree/triangles, pagerank inside the "family" component, distance to famous scammers.
3) Model stack: from rules to graph neural networks
Composition> one algorithm. Typical stack:- Deterministic: business gates and sanctions (KYC status, BIN/IP stop lists, velocity limits, geo-locks).
- Anomaly-detectors (Unsupervised): Isolation Forest, One-Class SVM, Autoencoder for behavioral embeddings.
- Supervised: GBDT/Random Forest/Logistic for the fraud/non-fraud label on confirmed cases.
- Sequences (Seq-models): LSTM/Transformer for time series of events, identification of "rhythms" of abuse.
- Graph analytics: community detection (Louvain/Leiden), link prediction, Graph Neural Networks (GNN) with node/edge features.
- Multitask approach: a single model with heads for scripts (multi-acc, ATO, bonus abuse) with a common embedding block.
Calibration: Platt/Isotonic, Precision-Recall balance control for a specific scenario (for example, for ATO - high Recall with moderate Precision, with additional verification in the orchestrator).
4) Real-time pipeline and orchestration of actions
1. Data stream (Kafka/Kinesis): logins, deposits, rates, device changes.
2. Feature Store with online features (seconds) and offline layer (history).
3. Online scoring (≤100 -300 ms): ensemble of rules + ML, aggregation in Risk Score [0.. 1].
4. Policy-engine: thresholds and measure ladder:- soft: SCA/2FA, re-session request, limit reduction, withdrawal delay, medium: manual check, KYC docks request, bonus/activity freeze, hard: block, AML report, T&C win recall.
- 5. Incident repository: trace solutions, causes (feature attribution/SHAP), investigation statuses.
- 6. Feedback-loop: marked cases → additional training; scheduled auto-reloading.
5) Behavioral and biometric signals
Mouse/touch K-pians, trajectories, scrolling rhythm - distinguish people from scripts/farms.
Latency profile: reaction time to the coefficient/promo window update; "non-human" uniform intervals.
Captcha-less behavioral verification: combined with device fingerprint and history.
Risk patterns in Telegram WebApp/mobile: switching between applications, quick account changes, clicks on deeplink campaigns.
6) Typical attacks and detection patterns
Bonus abuse: multiple registrations with related device fingerprints, deposits with minimal amounts in the promo window, fast cache out with a low vager → velocity + graph cluster pattern.
Arbitration teams: synchronous bets in a narrow market immediately after a micro-event → clustering by time/markets + cross-site line comparison.
ATO: new country/ASN login, device change, 2FA disconnect, non-standard output route → sequence-model + high-risk action gate.
Chargeback farms: cascades of small deposits with close BIN, mismatch billing, quick withdrawal → supervised + BIN/IP reputation.
Chip dumping in poker: atypical game with negative EV from the "donor," opponent's repeatability, abnormal sizing → graph + sequences.
7) Quality metrics and business KPIs
ML metrics: ROC-AUC/PR-AUC, KS, Brier, calibration. Separately according to scenarios.
Operating: TPR/FPR at given thresholds, average investigation time,% of auto decisions without escalation.
Business: reducing direct losses (net fraud loss), Hold uplift (due to the protection of the bonus pool), the share of prevented chargers, LTV-retention among "good" players (at least false positive).
Compliance: share of cases with explainability (reason codes), SLA by SAR/STR, traceability of solutions.
8) Explainability, fairness and confidentiality
Explainability: global and local importance (SHAP), reason codes in each solution.
Fairness control: regular bias audits for sensitive features; "minimum sufficient personalization."
Privacy: pseudonymization of identifiers, minimization of storage, retention policies, PII encryption, differentiation between offline learning and online scoring.
Regulatory: decision log, versioned models, consistent T&C and notifications to users.
9) Architectural reference (schematic)
Ingest: SDK/logins/payments → Stream.
Processing: CEP/stream-aggregation → Feature Store (online/offline).
Models: Ensemble (Rules + GBDT + Anomaly + GNN + Seq).
Serving: Low-latency API, canary-deploy, backtest/shadow.
Orchestration: Policy-engine, playbooks, case management.
MLOps: drift monitoring (population/PSI), retrain jobs, approval gates, rollback.
10) Response playbooks (examples)
Multicast signal (score ≥ 0. 85) + cluster graph:1. bonus and output frieze, 2) extended KYC (POA/Source of Funds) request, 3) family deactivation, 4) device stop lists/BIN/IP update.
ATO (spike + sequence anomaly):1. immediate log-out of all sessions, 2) forced password change + 2FA, 3) transaction hold 24-72 h, 4) player notification.
Chargeback risk:1. limiting withdrawal methods, 2) increased hold, 3) manual transaction review, 4) proactive PSP/bank contact.
Collusion/chip dumping:1. cancellation of the results of suspicious matches, 2) blocking accounts, 3) report to the regulator/tournament operator.
11) Training and markup: how not to "poison" dataset
Positive/negative mining: choose "pure" examples of fraud (chargeback confirmed, AML cases) and carefully select "pure" players.
Temporal validation: time diversity (train Label drift: regular revision of markup rules; tracking the change of attack tactics. Active learning: semi-automatic selection of "questionable" cases for manual moderation. 12) Practical implementation checklist Online Feature Store, SLA scoring ≤ 300 ms, fault tolerance. Ensemble of models + rules, calibrated speeds, reason codes. Graph analysis and behavioral embeddings in prod (not only offline reports). Separation of thresholds by scenarios (ATO/Bonus/Chargeback/Collusion). MLOps: drift monitoring, canary/shadow deploy, auto-reloading. Playbooks and unified case management with an audit trail. Privacy-by-Design policy, honest T&C and player notifications. AI behavior analysis transforms antifraud from "manual hunting" to a predictive risk control system. Operators who combine three elements win: a rich behavioral layer of data, an ensemble of models with a graph perspective, and strict operational discipline (MLOps + compliance). Such a stack reduces losses, protects the bonus economy and at the same time reduces friction for conscientious players - which in the long run increases retention, LTV and brand confidence.