Adyen AI Applied Research Engineering: Integrity Risk Agents, Uplift, and Data Analysis
Traditional Integrity Risk operations required large human teams to manually research, analyze, and label cases; companies broadly struggle to apply AI meaningfully in critical large-scale production flows; and current SOTA AI agents achieve only around 16% accuracy on complex multi-step financial data 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 cases to human analysts.
Adyen has deployed Uplift across every transaction on the platform and co-built the DABStep benchmark with Hugging Face using over 450 real-world tasks; ongoing AI agent research for Integrity Risk and foundational models carries potential for substantial productivity gains.
What failed first
Pre-LLM ML models surface Integrity Risk cases to human analysts but the framework still requires a human team to spend time on tasks that have the potential to be automated; current SOTA reasoning agents score only around 16% on Adyen's multi-step data analysis benchmark.