Fraud detection built for one problem, not every problem
Kixo9 exists because bonus abuse, multi-accounting and collusion are specific, well-understood problems — and general-purpose fraud tools tend to treat them as an afterthought.
Why we built Kixo9
Gaming platforms lose real money to bonus abuse and multi-accounting every day — not through headline-grabbing breaches, but through slow, steady leakage that's easy to underestimate until someone adds it up. Most fraud tools on the market are built for e-commerce chargebacks or account-takeover, and bolt on gaming-specific detection as an afterthought.
Kixo9 starts from the opposite direction: every model, every signal, and every default threshold is built around how bonus abuse, multi-accounting and collusion actually show up on gaming platforms — sweepstakes, iGaming, skill-gaming and DFS.
How we work with teams
We start most relationships with a data review, not a sales pitch: bring a sample of your own signup or claim data, and we'll show you what our models find in it before you commit to anything.
From there, integration is designed to be lightweight — a REST API or webhook, not a platform migration.
What we believe about fraud tooling
Specificity beats generality
A model trained on gaming-specific abuse patterns will consistently outperform a generic fraud model adapted after the fact, because the underlying behaviors — bonus claiming, referral loops, collusion — don't map cleanly onto retail chargeback patterns.
Explainability isn't optional
A risk score a fraud team can't interrogate is a risk score they'll eventually stop trusting. Every score Kixo9 returns comes with the specific signals behind it, not just a number.
Trust gets earned in stages
We don't expect a new customer to flip on automatic blocking on day one, and we design the onboarding process — data review, shadow mode, review-only, then automation — around that.
Where this fits in your stack
Kixo9 is deliberately narrow in scope, which is part of the design, not a limitation.
We don't do document verification, and we don't do anti-money-laundering monitoring — both are mature, well-served categories with established vendors, and duplicating that work wouldn't add value. What we do is the layer that sits between those systems: taking the outputs of your KYC provider, your payments stack, and your own account and bonus data, and looking across all of it for the specific patterns that indicate bonus abuse, multi-accounting, and collusion. That focus is why the models are as accurate as they are for this specific problem, and it's also why integration tends to be straightforward — we're adding one clear layer, not asking you to rearchitect your fraud stack around us.
A few questions people ask early
Are you built for a specific gaming vertical?
No — Kixo9's models are trained across sweepstakes, iGaming, skill-gaming and DFS, with vertical-specific tuning rather than a one-size-fits-all score.
Do you work with platforms of any size?
Yes, though the value tends to show up clearest once a platform has enough signup volume for abuse patterns to form clusters — usually a few thousand monthly active users and up.
How do you handle data privacy?
Signal data is processed to power detection and retained only as needed for that purpose; specifics are covered in a data processing agreement during onboarding.
What makes a good first project for a new customer?
Almost always a data review against real historical data, because it answers the only question that actually matters early on: does this find something in your specific data, not just in general.
What we're not trying to be
It's worth being direct about scope, because a lot of fraud tooling oversells what it does. Kixo9 doesn't stop chargebacks, doesn't verify identity documents, doesn't run AML transaction monitoring, and doesn't replace a fraud team's judgment. It's a detection layer that surfaces the specific account-linkage and bonus-claim patterns that are hard to see manually, with enough evidence attached that a human can make a fast, informed decision. The value is in narrowing what your team has to look at, not in removing them from the loop.
Want to see what Kixo9 finds in your data?
No integration required for the first look.