Our research addresses the application of AI in financial services with emphasis on compliance, auditability, regulatory alignment in the EU and international, and sector-specific efficiency gains.
Research focus: Development of frameworks and methodologies for the integration of AI systems in financial institutions under EU and DACH compliance regimes (MiCA, BaFin, ESMA, EBA). Outcome: Establishment of reproducible compliance models enabling safe AI adoption while reducing regulatory risk and supervisory friction.
Research focus: Design of AI-assisted audit trail mechanisms on distributed ledger infrastructures to ensure verifiable transparency, accountability, and supervisory reporting. Outcome: Provision of tamper-resistant, machine-verifiable audit records that strengthen regulatory trust and reduce systemic risk.
Research focus: Application of AI techniques to Open Banking ecosystems under PSD2 and PSD3, addressing secure data exchange, consent management, and fraud detection. Outcome: Creation of scalable AI-based tools that enhance interoperability, consumer protection, and compliance in cross-institutional financial data sharing.
Research focus: Development of AI models for portfolio optimisation, ESG criteria integration, and advanced risk modelling aligned with EU sustainable finance directives. Outcome: Improved allocation efficiency, regulatory-compliant ESG reporting, and enhanced risk-adjusted returns for asset managers.
Research focus: Empirical analysis of AI deployment in banks, insurers, credit unions, and investment firms, with emphasis on explainability, compliance, and supervisory oversight. Outcome: Evidence-based frameworks supporting responsible AI adoption across institutional finance, lowering operational risk and improving supervisory compliance.
Research focus: Design of interoperable, AI-powered KYC infrastructures that ensure GDPR compliance, data sovereignty, and cross-border interoperability without centralised custody. Outcome: Deployment of privacy-preserving KYC solutions that reduce compliance costs, improve efficiency, and facilitate secure EU-wide identity verification.
The research examines the application of AI in public governance with focus on regulatory alignment, accountability, and transparency, and on developing AI systems for European institutions, supervisory bodies, and auditors.
Research focus: Development of AI systems that allow EU institutions and member states to navigate, analyse, and harmonise complex EU legal frameworks, including cross-border compliance and sector-specific regulation. Outcome: Tools that improve legislative consistency, reduce regulatory fragmentation, and accelerate policy implementation.
Research focus: Design of explainable AI tools for auditors, regulators, and supervisory authorities to detect anomalies, evaluate compliance, and assess systemic risks. Outcome: Increased efficiency and accountability in EU-level and national audits, reducing oversight costs while enhancing transparency.
Research focus: Development of an AI system that coordinates EU-level AI initiatives with country-specific AI frameworks, ensuring interoperability and compliance with both EU and national regulations. Outcome: A harmonised governance layer that enables the Commission and member states to align AI strategies, share data responsibly, and manage cross-border use cases.
Research focus: Creation of AI tools that simulate the economic, social, and environmental impact of new EU policies across member states, using multi-sector data. Outcome: Evidence-based policymaking, faster regulatory feedback loops, and improved foresight capacity for the EU Commission and Parliament.
The research investigates the role of AI in education with emphasis on accessibility, personalised learning, skills assessment, and regulatory alignment in EU and DACH contexts, while addressing data protection, transparency, and long-term societal impact.
Research focus: Development of adaptive AI systems that tailor educational content to individual learning styles, abilities, and linguistic backgrounds. Outcome: Increased accessibility and equity in education, supporting EU goals for inclusion and lifelong learning.
Research focus: Design of AI-driven assessment methods for real-time evaluation of student competencies, aligned with European Qualifications Frameworks. Outcome: More reliable and scalable assessment models that support harmonised certification across EU member states.
Research focus: Application of AI in vocational training programmes, especially in digital and green economy sectors, with attention to EU labour market demands. Outcome: Faster reskilling pathways, bridging skill gaps in critical industries, and supporting EU digital transition policies.
Research focus: Use of AI to analyse educational systems, measure policy outcomes, and forecast the effects of reforms across member states. Outcome: Evidence-based policy guidance that supports harmonisation of education strategies at EU level.