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How fraud protection works in betting
The betting business is a high-frequency environment with thin margins and instant cash flows. Any delay or erroneous tolerance is direct loss. Modern defense against fraud is not a set of manual rules, but an orchestra: signal collection, behavioral analytics, graph connections, real-time ML scoring and clear action playbooks. Below is a system analysis, as it works in practice.
1) Threat map
Multiaccounting: "families" of accounts for bonuses/cashback, farm through the same devices/networks.
Bonus abuse: deposit in the promo window, minimal vager, quick withdrawal; "carousels" on stocks.
ATO (Account Takeover): hijacking accounts through phishing/leaking passwords, device spoofing, IP/ASN change.
Collusion/chip dumping: poker collusion/PvP, EV translation between linked accounts.
Arbitrage/" sniping "of stale prices: bets on outdated odds after a micro-event.
Payment fraud and chargebacks: stolen cards, friendly fraud, cascades of small deposits.
Laundering (AML risks): fast cycle "input → minimum activity → output," non-standard routes.
2) Data and features: what anti-fraud rests on
Transactions: deposits/withdrawals, payment methods, amounts, timings, chargeback flags.
Game Events: Betting Frequency, Markets, Odds, ROI, Cashouts, Live Behavior
Devices and network: device-fingerprint, browser stability/OS, IP/ASN, proxy/VPN/TOR.
Authentication: logins, 2FA, password resets, unsuccessful login attempts.
Account: account age, KYC/SoF progress, matches on addresses/phones/payments.
Graph connections: common devices, IP, cards/wallets, refcodes, chains of logins in time.
Context: geo and time zone, promo calendar, traffic type (affiliate/organic), country/payment method risk.
Examples of features:- Velocity: N deposits/bets/logins per X minutes, speed "depozit→stavka→vyvod."
- Stability: the proportion of sessions with one device/browser fingerprint.
- Sequence: Click/bet rhythm, latency between line update and bet.
- Graph: degree of knot, triangles, distance to known violators, cluster metrics.
3) Real-time anti-fraud architecture
1. Ingest (stream): logins, payments, bets, device changes → event bus (Kafka/Kinesis).
2. Feature Store: online aggregations (seconds) + offline history (days/months).
3. Online scoring (≤100 -300 ms): rule ensemble + ML (GBDT/analog) + anomalies + graph signals → Risk Score [0.. 1].
4. Policy-engine: thresholds and "ladder of measures" (from soft frictions to blocking and AML report).
5. Case-management: incident card, reason codes, decision log, SLA investigation.
6. Feedback-loop: marked cases return to training; planned reloading.
4) Detection technologies
Rules (deterministic): BIN/IP/ASN stop lists, KYC gates, velocity limits.
Abnormal models: Isolation Forest/One-Class SVM/autoencoders on behavioral embeddings.
Classifiers: gradient boosting/logistic regression on marked fraud.
Sequences: LSTM/transformers by time series of account events.
Graph analytics: community detection (Louvain/Leiden), link prediction, rules on subgraphs.
Multimodal signals: device + behavioral biometrics (cursor/touch profiles) + payments.
Calibration of scoring (Platt/Isotonic) is mandatory - for transparent thresholds and stable Precision/Recall.
5) Key scenarios and patterns
Multiaccounting: common devices/wallets, the same entry time windows, clusters on IP subnets → freeing bonuses, increasing KYC/SoF requirements, deactivating the "family."
Bonus bonus: sequence "minimum deposit → single rate of low volatility → quick withdrawal" + coincidence by devices → temporary hold, manual check, update of stop lists.
ATO: login from new ASN/country + disable 2FA + change of device → immediate logout of all sessions, force password change, payment hold 24-72 h.
Collusion/chip dumping: negative EV of the "donor" against a specific opponent, repetition of pairs, abnormal sizing → cancellation of results, blocking, notification of the regulator/tournament operator.
Arbitrage of stale prices: a surge in bets in seconds after a micro-event, sniper hit in an outdated line, latency ~ 0 seconds → lowering limits, short suspend, auto-hedge, line alignment.
Chargeback farms: cascades of small deposits with close BIN/geography, mismatch billing → limitation of withdrawal methods, increased holds, proactive interaction with PSP.
6) Authentication, devices and network
Device-fingerprint 2. 0: hardware/browser parameters, resistance to substitution, control of emulators/rooting.
Behavioral biometrics: mouse/touch micro movements, scrolling rhythms, input patterns.
Network checks: IP/ASN reputation, proxy/VPN/TOR, geo-announcement, address change frequency.
SCA/2FA: push/OTP/WebAuthn - adaptive by risk.
7) Payments and AML
Transaction risk scoring: BIN, country, amount, frequency, post-deposit behavior.
SoF/SoW: sources of funds at high limits/winnings.
Rules of conclusion: risk holds, compliance with the input/output method, limits on new methods.
Reporting: SAR/STR, log storage and traceability of solutions.
8) Policy-engine and ladder measures
According to the risk scale:1. Soft frictions: repeated login, 2FA, captcha-less behavioral verification, limit reduction.
2. Mean: temporary hold, KYC/SoF add. request, partial withdrawal.
3. Hard: blocking, cancellation of bonuses/results for T&C, AML report, constant ban of devices/payments.
All actions - with reason codes and entry in the audit log.
9) MLOps and quality control
Drift monitoring: PSI/population shift, change of tactics of intruders.
Shadow/Canary-deploy: running models on a share of traffic with guardrails.
Backtesting/temporal split: time difference (train Explainability: global and local importance (reason codes in the case card). Scheduled reloading: with validation and emergency rollback. 10) Anti-fraud metrics and KPIs Model: ROC-AUC/PR-AUC, KS, Brier, calibration. Operating: TPR/FPR at thresholds,% of auto-decisions, average investigation time, share of incidents with full reason code. Business: net fraud loss ↓, chargeback rate ↓, saved bonus pool, Hold uplift, impact on LTV of "good" players (minimum false positive). 11) Response playbooks (compressed templates) ATO High: logout of all sessions → compulsory change of the password → 2FA-enforce → hold payments of 48 h → notification of the client. Bonus cluster: bonus/output frieze → extended KYC/SoF → family graph cleaning → device/wallet ban. Stale prices "sniping": immediate suspension of the market → recalculation of the auto-hedge → line → reduction of limits on the cluster → retrospective audit. 12) Privacy, justice, communication Privacy-by-Design: pseudonymization, PII minimization, encryption, retention policies. Fairness: prohibition of discrimination on protected grounds, regular bias audits. UX and trust: clear T & Cs, transparent explanations on flags and hold dates, understandable appeals. 13) Typical mistakes and how to avoid them Bet on one rules. Solution: ensemble (rules + ML + graph). No online. Solution: SLA scoring ≤ 300 ms, priority paths. No calibration. Solution: regular calibration/validation. Ignoring the graph. Solution: mandatory graph features and cluster alerts. Overblock "good." Solution: reason codes, fine thresholds, "soft" measures first. No MLOps. Solution: drift monitoring, canary/rollback, version log. 14) Implementation checklist An effective anti-fraud is not one "magic algorithm," but a consistent system: a rich layer of data, real-time scoring, graph perspective, strict discipline MLOps and understandable playbooks. This architecture simultaneously reduces losses, protects the bonus economy and protects the experience of conscientious players - which means it directly improves the unit economy and brand reputation.