Publications
Benchmarks, technical reports, and in-depth research on reliable AI deployment in financial services.
Moritz Geist, David Ahn, Maximilian Eber, PhD
A benchmark of AI agents that trace ownership structures for beneficial ownership checks on UK and German registers. Measures tracing accuracy across frontier models, importance of country specific configuration, and how much of the work agents complete on their own.
Koen Roelofs, Jakob Schmitt, Maximilian Eber, PhD
The first benchmark of prompt-injection resistance for agentic underwriting. Measures defense success across 16 frontier models, three providers, and five attack vectors — with and without untrusted-content tagging.
David Ahn, Maximilian Eber, PhD, Sahith Jagarlamudi
The first public benchmark of AI-driven adverse media investigation. Evaluates detection accuracy, evidence quality, reliability across agent runs, and cost efficiency across frontier models.
Nico Klees, Maximilian Eber, PhD
The first public benchmark for agentic financial document processing. Evaluates extraction accuracy, cross-document reasoning, calculation correctness, and structured output quality across seven frontier models. Built on anonymized production data from financial institutions.
Dustin Eaton, Maximilian Eber, PhD
Why AML teams must now apply model risk management standards to AI systems. Published in ACAMS Today, exploring how regulators are extending MRM frameworks to AI deployed in compliance functions — and what institutions need to do to prepare.