Detecting Multi-Accounting with AI: Signals, Graphs, and Risk Scoring
Multi-accounting in iGaming is not just about lost bonuses—it involves bypassing self-exclusion limits, abusing welcome bonuses, and evading KYC/AML verification. A single player creates dozens of accounts using different browsers, VPNs, and virtual cards. Traditional rules (IP match or card number) are easily circumvented. In practice, operators lose up to 30% of their bonus budget to multi-accounting, and manual checks consume thousands of hours per month. Our solution reduces these losses by 60–80% and automates 90% of reviews, saving $200k+ annually for mid-size operators. Effective detection is built on uncontrollable signals—those the user cannot consciously change: behavioral biometrics, device fingerprint, payment graph. We developed a system that combines device fingerprinting, behavioral biometrics, payment graph analysis, and machine learning. In this article, we break down which signals work, how the connection graph is built, and why risk scoring reduces false positives. The result is detection accuracy up to 97% with a false positive rate below 5%.
Signals for Analysis: Device Fingerprint, Biometrics, Payment Graph
Device Fingerprinting—a set of technical device characteristics. The combination is unique in 94–97% of cases (Canvas fingerprinting). We collect:
- Canvas fingerprint (rendering via Canvas API)
- WebGL parameters (GPU ID, renderer, extensions)
- AudioContext fingerprint
- Installed fonts, plugins, screen resolution
- Navigator properties, time zone, language
One person with multiple accounts often uses the same device. Fuzzy comparison via Jaccard similarity and Hamming distance finds similar fingerprints even with minor changes (e.g., browser update).
Behavioral biometrics—mouse trajectory, click intervals, typing speed—these patterns are as unique as fingerprints. Even with a VPN and a different browser, motor habits remain. We use LSTM on time series of mouse/keyboard events. Cosine similarity between behavioral embeddings of different accounts identifies the same user with >95% accuracy.
Payment Graph—bank cards, e-wallets. Graph analysis: account A tops up with card X, card X was previously used to top up account B—connection likely. Transitive closure of the graph reveals clusters of related accounts. Edge weight depends on type: shared payment method = high, shared public IP = low (NAT).
IP and network history—same VPN provider, same subnet, patterns of concurrent sessions. Temporal correlation: account A logs out, account B logs in from the same IP 30 seconds later—strong signal.
KYC document comparison—Face Recognition for photo comparison, OCR + NLP for extracting and matching data. Common passport on two accounts—100% connection.
How Graph Analysis Reveals Hidden Connections?
The key component is a graph where nodes are accounts and edges are shared attributes (device, IP, payment, behavioral similarity). Using community detection algorithms like Louvain or Leiden, we find clusters. Edge weights vary: payment method match—weight 0.9, shared IP—0.3. Weighted aggregation yields a connection score. Result: up to 40% of connections are invisible when analyzing a single signal. In one project, we reduced false positives from 15% to 4% and increased detection by 30%.
Example: Clustering Bonus Hunters
On one project, we processed 2 million accounts. The graph revealed 1500 clusters of 3–15 accounts, of which 80% were confirmed by manual review. Hidden connections via shared device fingerprint + behavioral embedding showed precision of 97%.
Why Risk Scoring is Better Than Binary Decisions?
The final risk score = f(device similarity, behavioral similarity, payment graph, IP history). Scale 0–100:
| Range | Action |
|---|---|
| 0–30 | Normal account |
| 31–60 | Monitoring + additional verification on bonuses |
| 61–80 | Bonus block, enhanced KYC |
| 81–100 | Immediate review, freeze |
Score is not a verdict but grounds for additional checks. Reviewers get an explanation: "These 3 accounts are connected via shared device fingerprint + payment method." Precision/recall is tuned to business goals: lower threshold = less fraud but more false positives.
Managing False Positives
Families sharing a computer—a legitimate case of similar fingerprints. Spouses sharing a card—also. Our approach: weight of each signal is configurable, community detection accounts for "family" profiles. False positive rate below 5% with recall of 85%.
Comparison of approaches:
| Approach | Precision | Recall | False Positive |
|---|---|---|---|
| Rule-based | 60-70% | 50-60% | 10-15% |
| ML on 5 signals | 85-90% | 75-80% | 5-8% |
| Graph + Biometrics + LSTM | 94-97% | 85-90% | <5% |
Our combination of graph + biometrics + LSTM is 3–4 times more effective than rule-based detection. This results in substantial budget savings by reducing losses from bonus hunters and decreasing the load on security teams.
What is Included in the Work (Turnkey Deliverables)
Our turnkey solution includes everything from audit to post-launch support. Deliverables include:
- Audit of current data and a report with recommendations.
- Development and configuration of signal collection pipelines (device fingerprint, behavioral biometrics, payment graph, IP history, documents).
- Training and validation of the model on your data: LSTM for behavioral biometrics, graph algorithms Louvain/Leiden.
- Integration via REST API or webhooks—with comprehensive documentation, test container, and SLA.
- Training your team: dashboard, threshold setup, graph interpretation, alert response.
- Post-implementation support for 3 months: monitoring, retraining, model adjustment based on your data.
Implementation Process (Within 6–10 Weeks)
- Data audit—analysis of historical logs, identification of key signals.
- Pipeline development—signal collection via JavaScript fingerprinting, event tracking, payment gateway integration.
- Model training—LSTM for behavioral biometrics, graph algorithms for connections.
- Integration—API for transmitting risk scores to your CRM/Platform.
- Team training—how to read the graph, set thresholds, respond to alerts.
- Support—monitoring, retraining as new patterns emerge.
Implementation time: 6–10 weeks depending on integration complexity. Cost is calculated individually. Experience: over 50 projects in iGaming, FinTech, and E-commerce. We guarantee adaptation to your infrastructure and accuracy SLA. Get a consultation: discuss your scenarios and find the optimal solution. Order an assessment of your project—we will prepare a proposal. Contact us for a free project assessment and custom quote—we deliver in as little as 6 weeks.