Incode published a customer case study stating that BetMGM replaced its previous identity provider with Incode and chose full automation instead of human review.
Cómo BetMGM detecta las IDs alteradas que la revisión manual dejaba pasar ↗
What happened
- The case study states that automated forensic checks analyze each document for signs of alteration and manipulation.
- The case study's FAQ states that about 9% of submitted documents were detected as altered after automated verification went live.
- The case study states Deepsight, Incode's deepfake defense layer, is the next item on BetMGM's roadmap.
Why it matters to Signicat
- The case study is a vendor-authored customer story in regulated iGaming in North America, outside Signicat's core European markets; direct overlap is limited to automated document IDV and deepfake/injection positioning.
- The 9% altered-document and 97% first-attempt figures are vendor-reported and not independently verified.
Evidence from the source (5 quotes)
- “Las verificaciones forenses automatizadas analizan cada documento en busca de señales de alteración y manipulación que un revisor humano aprueba bajo presión de tiempo.” source ↗
- “Aproximadamente el 9% de los documentos enviados se detectaron como alterados una vez que la verificación automatizada entró en funcionamiento.” source ↗
- “Es la siguiente capa en la hoja de ruta de BetMGM” source ↗
- “BetMGM opera tres marcas en EE. UU. y Canadá” source ↗
- “Aproximadamente el 9% de los documentos enviados se detectaron como alterados” source ↗
Document IDV biometricsFraud deepfake injectionCase studyGlobal
4 of 4 facts verified against the source · confidence medium · claude-haiku-5-5