Media, Gaming & Sports
Game Fraud and Bot Detection API
Cheating does not usually cost you the cheater. It costs you the honest players who leave because the game stopped feeling fair.
- Multiplayer game studios
- Mobile game publishers
- Game backend providers
- Esports organisers
- Trust and safety teams
What your customers would see
example interfaceMockupIllustrative mockup of the interface we would build. Figures shown are examples, not results from a live deployment.
The problem this solves
Abuse in games arrives in several shapes that need separating. Automation farming currency, aim assistance, wall-hacking, account sharing and boosting, real-money trading, multi-accounting to dodge sanctions, and payment fraud against the store. Each has a different signature, a different population, and a different appropriate response — and lumping them into one trust score makes all of them harder to handle.
Client-side anti-cheat catches a portion and provokes an arms race, while players object to what it installs. Server-side behavioural detection is harder to evade because it watches outcomes rather than processes, but it is statistical: it tells you a pattern is improbable, not that somebody cheated.
That distinction is the whole design problem, because the cost of error is asymmetric and lands on individuals. A missed cheater degrades some matches. A wrongly banned player loses an account they may have paid into for years, usually with no way to argue. Any system that issues permanent sanctions on a probability needs human review and a genuine appeal route built in from the start, not bolted on after the first wave of complaints.
Who buys this
Multiplayer game studios, Mobile game publishers, Game backend providers, Esports organisers, Trust and safety teams
What the API returns
What comes back
- Automation and bot indicators from input timing, path and action-sequence regularity
- Aim and reaction anomaly measures compared against the player's own skill distribution
- Information-access anomalies suggesting visibility a legitimate client would not have
- Account-sharing and boosting signals from behavioural and device discontinuity
- Multi-account linking across devices, payment instruments and behaviour
- Economy abuse detection for currency farming and real-money trading patterns
- Evidence bundles with the specific matches, sessions and measurements behind each flag
- Confidence bands with explicit recommendations for review rather than automatic action
Endpoints
| Method | Path | Returns |
|---|---|---|
| POST | /v1/sessions/analyse | Analyse a completed session for abuse indicators |
| GET | /v1/accounts/{id}/risk | Current risk profile with contributing evidence |
| GET | /v1/accounts/{id}/evidence | Full evidence bundle for moderator review |
| POST | /v1/accounts/{id}/outcome | Report a moderation decision or successful appeal |
| GET | /v1/clusters | Linked account groups with the linking basis |
| WS | /v1/stream | Push high-confidence detections as sessions complete |
Request and response
illustrativeRequest
{
"account_id": "acc_441920",
"window": { "sessions": 40 },
"include_evidence": true,
"detectors": ["automation", "aim_anomaly", "info_access", "account_sharing"]
}Response
{
"account_id": "acc_441920",
"evaluated_at": "2026-10-22T20:12:55Z",
"risk_score": 77,
"band": "high",
"recommendation": "manual_review",
"automated_action": "none",
"detections": [
{
"detector": "automation",
"confidence": 0.89,
"evidence": "Input inter-arrival variance 4.1ms across 2,180 actions; human baseline for this mode is 38-120ms",
"sessions": ["s_99120", "s_99144", "s_99181"]
},
{
"detector": "aim_anomaly",
"confidence": 0.52,
"evidence": "Snap-to-target angle distribution 3.2 sd above this account's own 90-day baseline",
"caveat": "Consistent with a hardware or sensitivity change; not conclusive"
}
],
"linked_accounts": [
{ "account_id": "acc_441933", "basis": "device_and_payment_overlap",
"strength": 0.81 }
],
"appeal": { "evidence_retained_until": "2027-04-22",
"reproducible": true },
"audit_reference": "det_2026102220125500441"
}Where the data comes from
| Your gameplay telemetry | Server-authoritative action, position and timing data — server-side signal is the backbone because it cannot be edited by the client |
| Session and device metadata | Device identifiers, platform, connection characteristics and session continuity |
| Economy and transaction logs | In-game currency flows and store purchases, which is where farming and real-money trading become visible |
| Player reports | Useful as a prior and as a review trigger, but noisy and frequently aimed at players who are simply good |
| Confirmed moderation outcomes | Upheld sanctions and successful appeals — the only honest calibration data, and appeals matter as much as bans |
| Payment and chargeback records | For store fraud, under the same handling constraints as any payment data |
The intelligence layer
The difference between a demo and a service is mostly in this section. These are the parts that keep the answer trustworthy once real traffic is hitting it.
- Separate detectors per abuse type rather than one trust score, because automation, aim assistance and account sharing have nothing in common statistically
- Within-player baselines so a highly skilled player is compared against their own history rather than the population mean
- Input-timing analysis on distributional regularity, which is what distinguishes a script from a fast human
- Graph linking across devices, payment instruments and behavioural fingerprints to surface sanction evasion
- Deliberately asymmetric thresholds: the operating point is chosen around the cost of a wrongful ban, not around detection rate
- Every flag reproducible from stored evidence, versioned so a decision can be re-examined during an appeal months later
- Calibration against both upheld sanctions and overturned appeals, with false-positive rate tracked as a first-class metric
- Drift monitoring per detector, since cheat tooling adapts within weeks of a detection change
What it changes for the business
Honest players stay
Perceived fairness drives retention far more than any individual cheater's spend.
Review throughput
Evidence bundles mean a moderator decides in minutes rather than reconstructing a case from raw logs.
Defensible sanctions
Reproducible evidence and a retained audit trail are what make a ban survive an appeal or a public complaint.
Economy integrity
Catching farming and real-money trading protects the in-game economy and the store revenue attached to it.
How it fits into your product
How it embeds
- Asynchronous analysis as sessions complete, with a stream for high-confidence detections that warrant a fast response
- Flags routed into your existing moderation tooling with evidence attached, not a separate console nobody checks
- Outcome endpoint so upheld decisions and successful appeals both feed calibration
- Deployment inside your own backend, which is normal since telemetry volumes are large and the data is sensitive
- Graduated response hooks: shadow queue, match restriction, review flag or sanction, chosen by your policy rather than ours
- Evidence retention configured to outlast your appeal window so a disputed case can always be re-examined
Included in every build
- Authentication, scoped API keys and per-customer rate limiting
- Input validation with predictable, documented error responses
- Versioned endpoints so your integration never breaks without notice
- Request and response logging for audit and dispute resolution
- Latency, error-rate and usage monitoring with alerting
- OpenAPI specification, sandbox environment and client examples
- Backups, failover design and a documented recovery procedure
- An agreed support arrangement once the service is live
How we would build it
about 20 weeksDiscovery and feasibility
Weeks 1–3
This one depends heavily on data access, so we start by proving the sources exist, can be licensed for your use, and actually carry the signal the model needs. If they do not, you find out here.
Architecture and baseline model
Weeks 4–6
A written architecture and a baseline model measured against a holdout period, so you know what accuracy you are buying before the full build starts.
Production build
Weeks 7–16
Data pipelines, model training and serving, historical archive, validation harness, API, security, monitoring and documentation.
Validation and integration
Weeks 17–20
Backtesting against held-out history, accuracy reporting your team can audit, integration support and an agreed retraining cadence.
Commercial models
There is no fixed price list, because the cost is driven by data complexity, integration count, model requirements, request volume and the support level you need. Scope is agreed in writing before development starts.
Fixed-scope prototype
A defined slice of the service, built and priced as one piece of work. The usual first step when the business case needs proving internally.
Fixed-price build
An agreed scope against a written architecture, delivered for an agreed price. Best when the requirements are already clear.
Build then retainer
We build the service, then operate and improve it under a monthly agreement covering monitoring, updates and model maintenance.
White-label product
The service runs under your brand, your domain and your pricing, with MLPivot invisible to your customers.
Questions buyers ask
Can we ban automatically on a high score?
For reversible measures — shadow queues, match restrictions, rate limits — that is reasonable and we would build the hooks. For permanent bans we would push back hard. These are statistical detections with a real false-positive rate, and a wrongly banned player loses an account they have paid into. Permanent action should pass through human review with an appeal route, and the API returns automated_action as none by default for exactly that reason.
What is the false-positive rate?
It depends on the detector and the operating point, and we would measure it on your data rather than quote a figure. Automation detection on input timing can be made very precise. Aim anomaly detection is far weaker — hardware changes, sensitivity tweaks and genuine improvement all look similar — which is why that detector returns caveats and is not suitable for acting on alone.
Does this replace client-side anti-cheat?
No. It is complementary. Client-side tooling sees processes and memory; behavioural detection sees outcomes and is harder to evade but slower and statistical. Most studios run both, and the honest framing is that neither eliminates cheating — they raise the cost of it.
How do appeals work?
Every detection stores its inputs and detector version, so a case can be reproduced exactly as it was made. Retention is configured to outlast your appeal window. We also treat overturned appeals as calibration data, because a detector whose appeals keep succeeding is telling you something important.
Will it catch a cheat we have never seen?
Sometimes, where the cheat produces statistically improbable behaviour — that is the advantage of watching outcomes. But novel tooling frequently works for a while before it becomes detectable, and we would not claim otherwise. Expect a lag between a new cheat appearing and the detectors adapting to it.
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Want this built for your platform?
Tell us what your product does today and what you want another system to be able to ask it. We will come back with an approach, a scope and a price.