Why casinos use AI for anti-fraud

Fraud in iGaming is becoming more and more difficult: multi-accounts, syndicates, bonus abuse, "mules," proxy networks, cashing through conclusions, masking devices and "clean" documents. Rules and threshold filters catch basic patterns, but quickly get tired of new schemes. The AI approach is a layer of adaptive models that learn from behavior, find non-trivial connections, and notice anomalies before the damage becomes meaningful.

1) Where AI really helps

Multiaccounting and collusion. Graph models identify groups linked by devices, payments, IP/ASN and rate patterns.

Bonus abuse. Behavioral scoring distinguishes "offer hunting" from normal onboarding.

Payment fraud and chargebacks. Models assess risk by device, payment method, chargeback retrospective, and routes.

KYC fakes. Computer vision and liveness modules catch deepfakes/masks/repetitions of documents.

AML abnormalities. Detecting structuring, pass-through and "disproportionate" turns under the player's profile.

Spam/support. NLP filters abuse promos and classifies appeals by risk.

2) Model types (and why combine them)

Rules (baseline). Explainable and cheap. Remain a "safety net" (velocity, limits, geo-rules).

Supervised (gradient boosting/logging/neural networks). The forecast is "fraud/not fraud" according to the marked history (chargeback, confirmed abuse).

Unsupervised (anomalies). Isolation Forest, autoencoders - catch "new" schemes without tags.

Graph (GNN/ Node2Vec/link prediction). See syndicates, shared devices/wallets, "mules."

NLP/vision. OCR quality of documents, comparison of selfies, analysis of support/affiliate texts.

Renewal/Baesian models. For adaptive thresholds and TPR/FPR balance at seasonality.

Composition: rules → anomalies → supervision → graphs - cascade with risk ranking.

3) Fichy: what makes up the risk

Behavior: rhythm of sessions, "chase," variance of bets, speed of transitions, time of day.

Device/network: fingerprint, emulated devices, proxy/VPN/ASN reputation, geo drift.

Payments: mix of methods, share of cancellations/chargeback, "quick withdrawal," rare PSPs.

Graph signals: shared device/card/wallet/IP, common referrals, simultaneous inputs.

KYC: liveness rate, biometrics/document match, pattern repeatability.

Content/text: complaints, keywords, attempts to circumvent bonus rules.

4) Real-time data flow and scoring

1. Event bus (Kafka/PubSub) collects deposits, bets, logins, KYC events.

2. Feature store supports "online" and "offline" features with the same transformations.

3. Real-time inference (≤50 -150 ms): the model assigns a risk rate and an action: skip/lower limits/request KYC/manual review/block.

4. K-loop: feedback from case management (true label) for further training and calibration.

5) Risk decisions

Soft friction: low risk → lower limits, email/phone verification.

Step-up KYC/EDD: medium risk → additional documents, address, source of funds.

Tough measures: high risk → stop withdrawal, hold operations, manual investigation.

Combinations: graph-flag + high ML-speed → priority in the investigation queue.

6) Explainability and trust

SHAP/Permutation importance show why the model raised the risk (proxy, shared map, fast output).

Rules-sanity checks over the model - "understandable protection against idiocy."

Feature blacklists (prohibition of sensitive attributes that are not compatible with local law).

Playbook for support: how to explain to the user step-up measures without revealing anti-fraud signals.

7) Model monitoring and drift

Quality: ROC-AUC/PR-AUC, TPR/FPR, Precision @ K, profit/loss.

Data/prediction drift: PSI/KS, alerts when traffic channels are shifted.

latency stability and timeout share in the product.

Champion/Challenger: Parallel run of new model and A/B score on real traffic.

8) Privacy and compliance

PII minimization, separate storages (PII/KYC/transactions/features), pseudonymization of identifiers.

Encryption: TLS 1. 3 in transit, AES-256-GCM in storage, KMS/HSM and key rotation.

GDPR/DSR: right of access/removal, DPIA for anti-fraud pipeline, logic of legal grounds.

WORM archives for investigation logs and reproducibility of decisions.

9) Economy: How to count the benefits

Direct effect: reduction of chargeback/fraud-loss%, returns, prevented conclusions.

Indirect effect: fewer manual reviews, faster "clean" conclusion, NPS growth.

Funnel metrics: time to withdrawal, proportion of "clean" customers affected by checks (friction).

Increment: cohort comparison with/without AI, uplift tests.

10) Frequent errors

Voodoo-ML without rules. Need baseline from deterministic filters.

Feature leakage and data leakage (use of future events in training).

There are no uniform online/offline transformations. Discrepancy in features → degradation.

Too "black box." Without explanation, complaints and regulatory risks will increase.

Ignoring the graph. "Farms" and syndicates remain invisible.

Lack of money idempotency. Webhooks replays → double operations.

Blending goals. One speed for AML and promo abuse - a compromise for the sake of metrics, but worse quality.

11) Checklist for the introduction of AI anti-fraud (save)

Event bus + single feature store (online/offline)
Rule baseline + ML (supervised) + anomalies + graph signals
Real-time scoring ≤150 ms, fallback solutions for timeouts
Explainability (SHAP), auditing solutions, playbook for support
Champion/Challenger and A/B Economic Impact Assessment
Model monitoring: drift, quality, latency, alerts
Privacy/Encryption, DPIA, Separate Storage, KMS/HSM
Case management with feedback (tags for additional training)
Money idempotence, signed webhooks (HMAC), anti-replay
MRM (Model Risk Management) processes: versions, owner, update policy

12) Mini-FAQ

AI to replace analysts? No: it reduces noise, but the final decisions and marking of "gold" are for people.

How much data do you need? For boosting - tens of thousands of marked cases; for anomalies - a fairly wide sample of events.

Why is FPR still high? Check class balance, threshold calibration, drift and online/offline feature difference.

Is it possible without a graph? It is possible, but multi-accounts and syndicates will "skip."

Will the conversions hurt? In a stepwise approach - on the contrary: "clean" customers pass faster.

AI in antifrode is not "magic," but a discipline: correct data and features, a cascade of rules and models, graph signals, explainability, privacy and constant quality monitoring. This stack reduces direct losses, speeds up bona fide customers and withstands the evolution of attacks - which means it supports the economy, brand trust, and regulatory requirements.