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How AI improves anti-fraud systems
Introduction: from rules to smart protection
Classic anti-fraud is based on the rules: stop lists, limits, field patterns. It's fast but narrow: schemes change and rules become outdated. AI-anti-fraud complements the rules with models and graphs: it sees account connections, catches unfamiliar patterns, explains decisions and speeds up honest payments. The goal is the minimum of false locks, the maximum speed of "green" operations.
1) Data: signal foundation
Game events: bets/wins, odds, type of rounds (base/bonus), TTFP/hit-rate, episode lengths.
Payments: deposits/withdrawals, methods, commissions, retrays, chargeback flags, geo/device/method matching.
Devices and sessions: browser/device fingerprints, action frequency, gestures/input timings (behavioral biometrics).
Marketing/bonuses: coupons, referrals, wagering conditions, activation frequency.
Content/studios: provider, build version, live room/stream.
Principles: single event bus, idempotency, precise timestamps, PII minimization and tokenization.
2) Feechee: What risk "looks like" for a model
Pace and rhythm: bets "in the window" of quotation lags, peaks of activity, serial express trains.
Payment structure: split amounts, alternating methods, quick cancellation of conclusions.
Geo-behavior: sudden changes in location/devices, "karta≠geo≠IP."
Link graph: common IP/devices/maps/referrals → communities, bridges, "farms."
Bot patterns: stable click timings, a narrow range of delays between bets.
RG separation: night marathons and overbets are signals of care, not punishment.
3) Anti-fraud model stack
Rules-as-Code: mandatory regulatory checks and basic limits - "first barrier."
Unsupervised anomalies: isolation forest, autoencoders, One-Class SVM for "unseen" schemes.
Supervised scoring: GBDT/log on marked incidents; focus on PR-AUC and precision @ k.
Graph models: community search (Louvain/Leiden), link prediction and centrality for collusions/bonus farms.
Sequence models: RNN/Transformer for scripts "arbitration on lags," autoclicks, scripts.
XAI layer: SHAP/rule-surrogates for human-understandable reasons for decisions.
4) Orchestration: "green/yellow/red"
Green: low risk → instant confirmation of bets/cashouts and instant withdrawal.
Yellow: doubt → soft 2FA, method validation, sum/frequency capping, post-audit.
Red: high risk/graph cluster → pause, bonus freeze, HITL check, AML notification.
Each solution is logged in an audit trail with input features, model versions and thresholds.
5) Why AI is speeding up honest payouts
Low-latency scoring (p95 <50-100 ms) skips "green" operations without friction.
The payment orchestrator chooses a reliable provider for a risk profile, explains ETA and commissions.
XAI statuses ("instant/need verification/manual verification") reduce support.
6) Separate "luck" and fraud
A big win is not a signal in itself. We check: compliance with RTP/volatility, EVT tails, hit-rate by scenes, absence of suspicious graph links and version failures. Valid? → instalment payment and public proof of honesty.
7) Integrations: where AI gives the most
Payments: financial routing, dynamic limits, anti-chargeback scenarios.
Trading/lines (sports): detection of "bets in lag," notifications to trading, auto-capping markets.
LiveOps/bonuses: anti-farms, honest capping promo, RT block on suspicious clusters.
RG engine: with an increase in behavioral risk, we pause the promo, offer limits and Focus mode.
8) Privacy and justice
Federated training and local processing where possible.
Differential privacy on units and reports.
Fairness controls: monitoring bias by market/device; prohibition of discriminatory features.
Clear consent to the use of data and convenient personalization toggle switches.
9) Metrics that matter
PR-AUC/precision @ k/recall @ k on incidents; FPR on green profiles.
IFR - Instant Fulfillment Rate
TTD/MTTM: incident detection/mitigation time.
Graph-lift: contribution of graph features to the detection.
NPS of trust: to statuses and explanations for players/partners.
10) Reference architecture
Event Bus → Stream Aggregator → Online Feature Store → Scoring API (rules + models) → Decision Engine (зел./жёлт./красн.) → Action Hub
In parallel: Graph Service, Payment Orchestrator, XAI/Compliance Hub, Observability (metrics/trails/alerts), Trading Monitor.
11) MLOps and sustainability
Versioning of data/features/models/thresholds; lineage и reproducibility.
Drift monitoring of distributions and calibration; shadow runs, fast rollback.
Data chaos engineering (gaps/duplicates/delays) → graceful degradation, not failure.
Sandboxes for auditors with replay of historical flows; feature flags by jurisdiction.
12) Cases "from the fields"
Bonus farm on a proxy network: the graph combines 140 "beginners" with common devices → a red zone, a promo frieze, a KYC recess.
Arbitration of lines in live: a series of express trains "before updating quotes" → auto-capping of the market, notification to trading, a temporary pause of auto-cashouts.
Account hijacking: sharp change of device/geo + new payment method → forced password change, confirmation of the method, return of transactions if necessary.
Honest record win: EVT is normal, there are no connections → instant payment and public status, complaints - zero.
13) Implementation Roadmap (6-9 months)
Months 1-2: event bus, rules-as-code, online feature store, statuses for the player, basic anomalies.
Months 3-4: supervised scoring, count service, Decision Engine zel ./Yellow ./Red. , "XAI panel.
Months 5-6: integration with payments and trading-monitor, shadow runs, auto-mapping promo.
Months 7-9: federated learning, chaos tests, regulator sandboxes, IFR/TTD/MTTM optimization.
14) Frequent mistakes and how to avoid them
Confuse luck with fraud. Amount of winnings ≠ risk; Analyze the shape of distributions and relationships.
Live only by the rules. Without models and graph, misses and FPRs grow.
Ignore XAI. Without explanation, a conflict with the support and regulator is inevitable.
Mix RG and sanctions. Behavioral risks → in the contours of care, not punishment.
Chase "zero FPR." Excessive thresholds kill trust and payout speed - balance.
AI turns antifraud into a controlled engineering discipline: graphs reveal networks, models catch new ones, the orchestrator makes fair decisions, XAI explains, and green operations take place instantly. The platform wins where speed, accuracy, transparency and RG priority are built into the architecture - and an honest player feels this in every operation.