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How artificial intelligence identifies scammers
Introduction: speed, accuracy, explainability
Gambling fraud is diverse: multi-accounting, bonus abuse, arbitration by line lags, collusions in PvP/crash games, account hijacking, payment schemes. Manual checks do not have time. AI antifraud turns a stream of events into risk signals in milliseconds, while all solutions are reproducible and explainable. The task is the minimum of false locks, the maximum speed of honest payments, respect for privacy and the priority of responsible play (RG).
1) Data: raw materials for detection
Game events: bets, results, round types, odds, TTFP/hit-rate, episode lengths.
Payments: deposits/cashouts, methods, commissions, retrays, chargeback signals, geo/device/card inconsistencies.
Devices and sessions: browser/device fingerprints, input speed, gestures, network (behavioral biometrics).
Social/affiliates: referral codes, clans, joint activities, UGC trail.
Marketing/bonuses: coupons, conditions, wagering, activation frequency.
Context and studios: provider, live room, build version, region.
Principles: single event bus, idempotency, precise timestamps, PII minimization and tokenization.
2) Feechee: How a fraudster is "visible" to models
Pace and rhythm: peaks of bets before moving the line, bets "in the window" lags, synchronous express trains.
Rate structure: split amounts, share of high coefficients, repeating patterns of express trains.
Payment anomalies: new cards on an old device, a new device with the same cards, "multi-methods" in a short period.
Link graph: common IP/devices/methods/referrals → clusters, cycles, bridges.
Behavioral biometrics: gesture/timing stability vs "bot clique."
RG signals (not fraud!): Night marathons, cancellation of withdrawal → go into the circuit of care, not punishment.
Online features live in the online feature store (low latency), offline - in training windows.
3) Anti-fraud model stack
Rules-as-Code: age/jurisdiction, risk lists, bonus limits - mandatory predicates.
Anomalies (unsupervised): isolation forest, autoencoder, One-Class SVM on time series and window features.
Supervised scoring: GBDT/log on marked incidents (PR-AUC as the main guideline).
Graph models: identifying collisions/rings of bonus abuse, link prediction for "farms" of accounts.
Sequence models: Transformer/RNN for recognizing "arbitration on lags" scenarios and auto-scripts.
XAI layer: SHAP/rules-surrogates → human-clear reasons for decisions.
4) Don't confuse luck with fraud
A rare large payout is not equal to fraud. We check the context: RTP/volatility in the window, EVT tails, hit-rate by scenes, lack of suspicious graph links. If everything is normal - an instalment payment and a public proof of honesty. If the anomaly is local (room/version/device cluster) - escalation.
5) Decision orchestrator (zel ./yellow/red.)
Green: low risk → instant confirmation of bets/cashouts, instant withdrawal.
Yellow: doubt → soft 2FA, method validation, sum/frequency capping, post-audit.
Red: high risk/graph cluster → operation pause, bonus freeze, HITL check, AML/studio notification.
All steps fall into the audit trail with input features, model versions and thresholds.
6) Typical schemes and reactions
Multi-account/bonus farm: many registrations with related prints/proxies → graph alert, promo frieze, KYC deepening.
Arbitrage of lines: bets before updating quotations → market limit, trading signal, temporary pause of auto-cashouts.
Collation in PvP/crash: synchronous inputs/outputs of a small group → payment pause, advanced graph analysis.
Account hijacking: abrupt device change/geo + unusual payments → forced password change, method confirmation, transaction rollback if necessary.
UGC farm/referrals: mass "gray" referrals → scoring of sources, mouthfuls, cleaning.
7) Transparency: What different sides see
Player: "instant/need checking/manual verification" statuses, ETA, short step reason (no jargon).
Regulator: rule/scoring logs, traces of model versions, incident-reports, provable invariability of RTP/payment tables.
Internal audit: reproducibility of any one-click solution.
8) Privacy and ethics
Layer consents, PII minimization and tokenization, access restriction.
Federated training where possible; differential noise on aggregates.
RG-priority: with alarming patterns of behavior - careful measures (limits/pauses/Focus-mode), not sanctions.
Prohibition of "dark patterns" and discriminatory rules.
9) Antifraud quality metrics
PR-AUC/precision @ k/recall @ k on marked cases.
FPR by "green" profiles (erroneous locks).
TTD/MTTM: incident detection/mitigation time.
IFR: proportion of honest transactions that have passed instantly.
Graph-lift: increase in detection when taking into account connections.
NPS of trust: to statuses and explanations.
10) Solution architecture
Event Bus → Stream Aggregator → Online Feature Store → Scoring API (rules + models) → Decision Engine (зел./жёлт./красн.) → Action Hub
In parallel: Graph Service, XAI/Compliance Hub, Payment Orchestrator, Observability (metrics/trails/alerts).
11) MLOps and sustainability
Versioning of data/features/models/thresholds; lineage control.
Drift monitoring of distributions and thresholds; shadow runs before release.
Data chaos engineering (gaps/duplicates/delays) - the system must degrade safely.
Sandboxes for auditors (replays of historical flows), feature flags by jurisdiction.
12) Implementation Roadmap (6-9 months)
Months 1-2: single event bus, rules-as-code, online feature store, basic anomalies, statuses for the player.
Months 3-4: graph-connections, supervised-scorins, Decision Engine (zel ./yellow/red.) , XAI panel.
Months 5-6: arbitration detector, PvP collisions, integration with financial routing, capping automation.
Months 7-9: federated learning, chaos tests, regulatory sandboxes, IFR/TTD/MTTM optimization.
13) Frequent mistakes and how to avoid them
Block "by win size." Size ≠ fraud; see the shape of distributions and connections.
Ignore the graph. Individual signals catch farms and rings worse.
Chase 0% FPR. Too hard thresholds will kill trust; balance on target PR-AUC and business risks.
There is no explainability. Without XAI, the conflict with the support and regulator increases.
Mix RG and antifrode. Risks of behavior - to care, not to sanctions.
AI makes anti-fraud fast, accurate and transparent: graphs find networks, models catch anomalies, the orchestrator applies soft and fair measures, and honest players receive instant payments and understandable statuses. Those who combine speed, explainability, RG priority and sustainable architecture win - and turn the fight against fraud from chaos into an engineering discipline.