Adyen builds AI Applied Research Engineering team for Integrity Risk automation, foundational transaction models, and Uplift optimization
Traditional integrity risk processes (AML, KYC, CDD, CRR) require large human teams to manually research, analyze, and label cases; even Adyen's existing Graph Neural Network framework cannot automate those human review steps. A production readiness gap also exists for AI data analysis agents, with state-of-the-art models achieving only around 16% accuracy on multi-step reasoning tasks.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · GNN surfaces integrity risk cases
ML models, like Graph Neural Networks, surface integrity risk cases to human analysts with precision optimization.
Tools used
Graph Neural NetworksLLMsDABStepAdyen UpliftPPO
Outcome
Adyen established a research engineering team bridging AI research and production, co-built the DABStep benchmark with Hugging Face using over 450 real-world tasks, and runs Adyen Uplift in production platform-wide with an AI deciding the course of every transaction.
What failed first
Adyen's existing ML integrity risk framework using Graph Neural Networks surfaces cases with optimized precision but cannot automate the human analysis steps; and current state-of-the-art AI agents achieve only around 16% accuracy on multi-step reasoning benchmarks requiring structured and unstructured data.