Geolocation fraud detection and identity verification for regulated digital services
GeoComply detects location fraud and verifies digital identity across iGaming, streaming, banking, and payments using geolocation, device, and behavioral intelligence. The tech stack reveals a data-intensive operation: Python + PySpark + Databricks on GCP/AWS, paired with Kafka + RabbitMQ for streaming and BigQuery + Delta Lake for analytics, plus Vertex AI for machine learning. Active projects centered on scaling batch and streaming pipelines, mobile SDK forensics, and AI-assisted automation signal a company moving from rule-based fraud detection toward real-time, signal-driven systems.
GeoComply provides fraud prevention and identity verification solutions built on 13 years of development in regulated online gaming and sports betting. The platform processes over 10 billion transactions annually across 400+ million devices worldwide and serves digital services in iGaming, streaming video, banking, payments, and cryptocurrency. The product combines location intelligence, device fingerprinting, and machine learning to flag fraudulent activity earlier in user engagement and establish true digital identity. Engineering-heavy hiring (senior and staff positions) alongside ongoing work on mobile SDKs, streaming pipelines, and governance systems indicates focus on scaling infrastructure and expanding fraud detection capabilities.
Python, PySpark, Databricks, GCP, AWS, Kafka, RabbitMQ, BigQuery, Delta Lake, Vertex AI, Kubernetes, iOS/Android SDKs, and Salesforce (Sales Cloud, Service Cloud, Marketing Cloud).
GeoComply's software is installed on over 400 million devices worldwide and analyzes over 10 billion transactions annually.
GeoComply's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.