Prop Firm Anti‑Cheat (2026): Rules Engine + Detection Blueprint

1) Threat model: what prop firms are actually fighting

"Cheating" in prop is a spectrum. Some cases are clear fraud. Others are gray areas driven by poor policy design. The best anti‑cheat programs are explicit: what's allowed, what's restricted, and how exceptions are handled.

Common abuse patterns

• Copy trading: one strategy replicated across multiple accounts to farm payouts.
• Account sharing: multiple people trading one account, or one person trading many accounts under different identities.
• Hedging across accounts: correlated opposite positions across different accounts to reduce challenge variance.
• Reverse trading: "shadow" accounts that offset exposure in ways that violate policy.
• Latency / execution abuse: using extreme timing advantages around news or price spikes.
• Payout fraud: stolen identities, mule beneficiaries, chargebacks on challenge purchases.

Your controls should be proportional. The goal is not to maximize bans; it's to maximize clean payouts and long-term trust.

2) Rules engine design: configurable, auditable, and consistent

Most prop firms start with hard-coded rules. It works until you have multiple products, multiple regions, and multiple staff. Then enforcement becomes inconsistent. A rules engine makes policy configurable and gives you a consistent evaluation model.

What a good rules engine stores

• Rule definition: name, description, severity, enabled/disabled
• Scope: which program (challenge type), which phase, which symbol groups
• Thresholds: configurable parameters (time window, correlation threshold, max accounts)
• Actions: warn, restrict, require review, block payout, terminate
• Evidence template: what data must be captured to support this rule

Rule outputs: score + flags, not just "ban"

Strong systems output a risk score plus flags. That lets you route cases: clean → auto payout, medium risk → manual review, high risk → freeze/deny.

3) Signals: IP/device, behavior, trade correlation

You reduce false positives by combining signals. A correlation model alone can flag legitimate traders who follow news. IP/device alone can flag traders using shared offices. The signal fusion is the product.

IP and device intelligence

• IP reputation, ASN, country mismatch, VPN indicators
• Device fingerprinting: browser/device characteristics, stable identifiers
• Session graph: which accounts share device/IP patterns over time

Behavioral signals (non-trade)

• Login patterns and timezones
• Rapid account creation bursts
• Repeated payment method patterns / beneficiary reuse

4) Copy trading detection: practical, defensible methods

Copy trading detection is correlation analysis across accounts. The key is to measure similarity in a way that is hard to game and easy to explain.

Signals that correlate strongly with copying

• Timing similarity: entries within a small window (e.g., 1–5 seconds)
• Direction match: same side (buy/sell) across the same instrument
• Lot-size ratio stability: follower sizes are consistent ratios of leader sizes
• Exit similarity: similar close timing, SL/TP levels, or forced closes

Make it explainable

Your output should be explainable to a non-technical reviewer: "Account A and Account B placed 47 trades in the same direction on the same symbols, with median entry time difference of 2 seconds and stable lot ratio of 1.98x over 14 days."

5) Account sharing & multi-account graphs

This is where device/IP signals are powerful. Build a graph: accounts ↔ devices ↔ IPs ↔ payments ↔ beneficiaries. Then define rules like "more than N active accounts per device within 30 days" or "multiple identities using one payout beneficiary".

6) Payout gatekeeping: the safest place to enforce

The payout stage is where your firm is exposed financially. Even if you allow trading, you can still enforce strong gates before payout:

• KYC status must be approved
• Risk score must be below threshold
• No unresolved high-severity flags
• Manual approval required above payout thresholds

7) Enforcement workflow: consistent and defensible

Your workflow should reduce support conflict and keep decisions consistent. A mature workflow looks like:

1. Flag: rule triggers + evidence captured
2. Queue: assign to compliance/risk reviewer with SLA
3. Decision: approve, warn, restrict, deny payout, terminate
4. Communication: templated, calm language with appeal mechanism
5. Audit: store reviewer, timestamp, decision reason, attachments

8) Ops dashboards, alerts, and evidence logs

Anti-cheat needs visibility. If reviewers can't see the full picture quickly, they'll either over-ban or under-enforce.

Dashboards that matter

• Flag volume by rule, program, and phase
• Payout risk queue with SLA timers
• Top correlated clusters (leader/follower groups)
• Device/IP clusters with account counts
• Dispute outcomes (appeals accepted/denied) to tune rules

Evidence log design (minimum)

• Rule triggered + thresholds at that time
• Trade list / correlation summary (if trade-based)
• Device/IP summary (if identity-based)
• Reviewer notes + attachments
• Decision + timestamps

9) Implementation checklist

Phase 1 (MVP anti-cheat)

• Define policy: what is prohibited and what evidence is required
• Implement rule engine with severity scoring
• Implement device/IP session graph
• Implement basic trade correlation for copy detection
• Implement payout gate workflow + manual review queue

Phase 2 (institutional maturity)

• Cluster detection (leader/follower groups)
• Advanced behavioral signals (payment, timing, geo anomalies)
• Automated alerts and runbooks
• Rule tuning dashboard (false positives tracking)

Where Brokeret fits

Brokeret builds back office and automation systems that are configurable, auditable, and scalable. If you want to implement anti‑cheat as a proper operating system (not ad-hoc scripts), we can help.

• Prop CRM
• Talk to Brokeret