Players forgive a lot: a rough balance patch, a dull update, even a server queue. What they do not forgive is the feeling of being cheated. A single visible cheater in ranked play, one bot farming the economy, or one wave of chargebacks after an event can undermine trust that took years to build. For a director of trust & safety or product security, fair play and anti-fraud have stopped being a feature next to the game and become a condition of its existence.
In our experience, the hard part is not catching one cheater but building a system that keeps up with the ingenuity of attackers without punishing honest players in the process. We do not have a shipped title we can point to here, so we’ll be straight with you: we enter this space with competence. We provide people experienced in data engineering, behavioural analytics and ML models who can design and maintain such a system inside your studio.
Why does fair play decide a game’s retention?
Retention in games rarely collapses for a single reason, but losing the sense of fairness can cut it sharply. A player who loses to someone using an aimbot does not file a complaint; they leave and take their friends with them. That makes fair play a business metric, not only an ethical one.
You see it on several levels at once: top players, who drive the community and streams, are the most sensitive to cheating; the in-game economy swells with resources farmed by bots, which breaks monetisation; and support drowns in reports that cannot be handled by hand. So we treat fair play as a loop: detection, decision, enforcement, appeal, and only the whole loop protects retention.
How does cheat and bot detection work in practice?
Effective detection rests on two layers that complement each other. The first is rules (fast, legible, easy to audit), the second is models, which catch what a rule cannot describe. A rule will tell you someone is shooting with 99% accuracy across a whole session. A model will flag a subtle bot that plays “human” on purpose to stay under the threshold.
When we build such a system, we usually start from the signals worth collecting and correlating:
- gameplay telemetry (accuracy, reaction time, movement patterns, unusual inputs),
- hardware and network signals (device fingerprints, account links, traffic anomalies),
- behavioural patterns over time (a 24/7 play schedule, repetitive farming routes),
- economic signals (sudden resource transfers, mule accounts, off-market trading).
On top of this data, our engineers build anomaly detectors and bot classifiers, and they surface the result not as a hard “ban” but as a risk score with a rationale that then moves on through the decision loop.
How do you reduce payment fraud and chargebacks?
Game fraud is not only cheaters. The payment layer can be just as costly: stolen cards, accounts taken over to launder value, and chargebacks that hit your margin after the player has already consumed the content. Here detection shifts toward transaction data and identity.
We combine purchase signals (transaction velocity, geography, card and device links) with the account’s in-game behaviour, because payment fraud almost always leaves a trace in gameplay too: a fresh account that immediately buys the most expensive bundle and rapidly offloads resources looks different from a loyal player. Scoring models let you catch this at the moment of the transaction, not only when the chargeback arrives. The goal is not to block every risky payment but to strike the right balance between fraud losses and friction for honest payers.
How do you design the reporting and moderation workflow?
Automation never catches everything, so player reports remain an important source of signal. The trouble is that raw reports are noise. Value appears when they flow into one workflow that groups, prioritises and attaches evidence to them.
We design that flow so a moderator does not start from scratch on every case:
- report aggregation around a single account or incident,
- prioritisation by risk score and impact (a streamer versus an alt),
- an evidence pack (clips, telemetry, account history) in one view,
- queues and SLAs so nothing hangs indefinitely.
That way moderation scales with the number of players rather than the number of headcount.
How do you run bans and appeals so they stay fair?
A ban is the sharpest tool, and the easiest way to lose trust if it behaves like a black box. So we design enforcement as a spectrum of responses, not a binary switch: from a warning, through a shadow ban and routing suspects into a separate match pool, to permanent removal on hard evidence.
The appeal path matters just as much. False positives will always happen, and a system without appeals turns every mistake into a lost player and a public crisis. That is why each ban decision should carry a record of the signals that led to it, so an appeal can be reviewed properly rather than by gut feeling.
Why must a human stay in the decision loop?
It is tempting to hand the whole thing to automation, but in anti-cheat and anti-fraud, full automation is a route to loud mistakes. A model picks suspects well, but it does not understand context: a new meta, an unusual yet legitimate playstyle, or the fact that a given clip is simply great reflexes.
So we build systems in a human-in-the-loop model: models and rules sift the obvious cases and set priorities, while a human resolves disputed and highest-stakes ones. That setup gives you two things at once: the scale of automation and the judgement of a moderator. It also adds priceless feedback, because every human decision retrains the models and sharpens the rules, so the system grows more accurate over time.
Which skills do we provide for anti-fraud and fair play?
This brings us back to the core: we enter this space with people. We shape the team around the stage you are at, not “just in case”:
- data engineers who join telemetry, payments and signals into one usable stream,
- ML / data science specialists building bot detection, anomaly and fraud-scoring models,
- backend developers standing up the rules layer, moderation queues and enforcement APIs,
- engineers supporting moderation tooling and panels for the trust & safety team.
We ground our AI and data competence in the RITS Group, including our AI/LLM/data teams specialised in LLMs, ML and data. That means we bring a real modelling craft, not just hands to code rules.
How do we do it at RITS?
We do it as a nearshore partner from Poland that strengthens your team with competence instead of selling a ready-made box:
- we staff the roles missing on the studio side (data, ML, backend, moderation tooling),
- we co-build the detection system and moderation workflow together with your trust & safety team,
- we design the loop of detection-decision-enforcement-appeal so it stays auditable,
- we maintain the rules and models, because anti-cheat is a process, not a one-off rollout.
We most often start with team augmentation where a specific skill is missing, and move to custom software built by our team when the system is built from scratch. We laid out the differences between these models in our article on IT engagement models.
Where to start with anti-fraud and fair play?
Start from the single most painful fraud vector (e.g. an aimbot in ranked or chargebacks after an event) and from the question of what data you already collect and what you are missing. That defines the first set of signals, the model scope and the roles you need. It is better to close one vector with a full decision loop than to detect everything with no way to enforce it fairly.
Building fair play and fraud protection in your game? Let’s talk - we’ll match the skills and design a detection system for your title.