Cross-Brand Player Identity Resolution
Multi-brand iGaming groups often run each skin's player database as its own island, so the same real person can hold accounts on three or four brands under one license with no system connecting them into a single identity. That fragmentation understates every risk view that depends on whole-player behavior: an AML risk score built from one brand's transaction history misses deposits made under a different brand name, an RG risk indicator misses total spend across the group, and a KYC record verified once gets re-requested separately on every brand the same person happens to register with, creating friction for the player and duplicate cost for the operator.
STARTING PRICE
From €799
Complex tier · Multi-system orchestration, custom logic, and higher-volume or higher-risk processing.
Get a quote →Saves roughly 10-15 hrs/week of manual cross-brand account investigation, plus more accurate whole-player AML and RG risk views.
How the automation works
We build a cross-brand identity resolution layer that matches accounts belonging to the same real person across every brand and skin under the license, using a weighted combination of identity document data, payment instrument fingerprints, device signals and address matching rather than any single field alone. Once accounts are linked with sufficient confidence, downstream systems, including AML risk scoring, RG indicator monitoring and KYC document status, can read from the consolidated identity instead of five separate partial views. Matches below a high-confidence threshold are surfaced for a compliance analyst to confirm or reject rather than merged automatically, since incorrectly linking two different people's accounts creates its own serious data and privacy problem.
Process flow
- 01
New account or periodic match run trigger
A new registration on any brand, or a scheduled cross-brand match run, checks for potential identity links against the existing player base.
- 02
Match identity signals ai
Identity document data, payment instrument fingerprints, device signals and address data are compared across brands to find candidate matches for the same real person.
- 03
Score match confidence ai
Candidate matches are scored on how many independent signals agree, distinguishing a high-confidence multi-signal match from a coincidental single-field overlap like a shared household IP.
- 04
Analyst confirms below-threshold matches output
High-confidence matches link automatically into a consolidated identity; anything below the confidence threshold is routed to a compliance analyst to confirm or reject before any records are merged.
- 05
Feed consolidated view to risk systems integration
Confirmed links feed a single consolidated player identity that AML risk scoring, RG monitoring and KYC status checks can read from instead of five separate per-brand partial pictures.
- 06
Log linkage decisions output
Every automatic link and every analyst decision is logged with the underlying evidence, ready for audit of how the consolidated identity was built.
Inputs
- Player registration and account data per brand
- Identity document data
- Payment instrument and device signals
- Existing KYC verification records
Outputs
- Consolidated cross-brand player identity
- Confidence-scored match candidates
- Analyst linkage decisions
- Linkage audit trail
Works with
Prefer a fully custom build instead of an off-the-shelf integration? We scope both options during your free consultation — most jobs like this one work fine on standard connectors, but higher-volume or non-standard systems sometimes need bespoke API work, reflected in the complex tier.
Where this goes wrong if you get it wrong
- Matching on a single field, such as shared payment instrument or shared household IP, produces false links between genuinely different people, such as family members or roommates — cross-brand identity resolution needs multiple independent corroborating signals before treating two accounts as the same person.
- Automatically merging accounts on a match, rather than linking them for risk-view purposes while keeping underlying records intact, destroys the ability to unwind an incorrect match — link, don't merge, and keep the original per-brand records recoverable.
- A consolidated identity that feeds AML or RG risk scores needs its own data-handling review, since combining previously separate profiles into one cross-brand view is itself a data protection consideration under GDPR, not just a compliance convenience.
- Once accounts are linked, a KYC document verified on one brand shouldn't need to be re-requested on another brand for the same person, but the linkage confidence bar for skipping re-verification should be stricter than the bar for a general risk-view link — reusing verification status carries more consequence than just improving a risk score.
Frequently asked questions
Does this automatically merge player accounts across brands?
No — high-confidence matches are linked for the purpose of building a consolidated risk view, but underlying per-brand records stay intact and recoverable; anything below the confidence threshold goes to a compliance analyst before any linkage at all.
Can a KYC document verified on one brand be reused on another once accounts are linked?
It can, but the confidence bar for skipping re-verification is set higher than the bar used for general risk-view linking, since reusing a KYC decision carries more consequence than improving a fraud or AML score.
How does this differ from bonus-abuse or general multi-accounting detection?
Those tools flag suspicious duplicate accounts for restriction. This builds a legitimate consolidated identity for the same real, verified person across brands they're allowed to hold accounts on, so their true AML and RG risk profile is visible group-wide.
What happens with false matches?
Matches below the confidence threshold never link automatically — they go to a compliance analyst who reviews the evidence and confirms or rejects the link, since a wrong merge of two different people's data is a real privacy problem.