Since May 2021
I have been appointed as a joint professor under the UT–ING collaboration, working alongside ING’s Global Analytics team to drive AI research in finance. Our focus spans federated learning, synthetic data, reinforcement learning, and credit risk early warning systems.
This five-year partnership brings together academic research and industry practice to develop AI-driven solutions in finance, including model risk frameworks, data analytics, and scalable business integration (ai-in-finance.eu).
Student Projects and Theses: Completed 8+ master’s theses at ING covering topics like Explainable AI, credit risk, ESG integration in credit models, and sustainability-report extraction. A current PhD project explores confidence scoring in large language model outputs within RAG pipelines (ai-in-finance.eu).
PhD & Postgrad Programs: Guiding doctoral candidates in Reinforcement Learning in Digital Finance through the MSCA DIGITAl network and COST FinAI action, co-organising training weeks and specialist workshops (ai-in-finance.eu).
Workshops & Lectures: Co-led educational events, including the Data Analytics & Quantitative Models workshop (Amsterdam, Feb 2025), covering model validation, risk management, and AI techniques such as RAG, risk analytics, and governance testing (ai-in-finance.eu).
Synthetic Data & LLM Integration
Explored GAN- and VAE-based synthetic financial data generation.
Enabled LLM-based RAG pipelines for document extraction, focusing on confidence scoring and hallucination detection (ai-in-finance.eu, ai-in-finance.eu, ai-in-finance.eu).
Credit Risk Forecasting & Early Warning
Built hybrid credit risk early warning systems combining time-series and classical ML algorithms.
Supervised master theses on credit deterioration detection and retail default prediction (ai-in-finance.eu).
Explainable AI & Regulatory Alignment
Developed Explainable AI (XAI) models to support interpretability in fraud detection and default probability models, validated for compliance and governance .
Reinforcement Learning in Finance
Hosted and co-taught reinforcement learning modules in the MSCA training week, covering Q-learning and policy-based methods tailored to dynamic portfolio management (ai-in-finance.eu).
Supported 8+ master theses, now deployed in ING’s credit risk and fraud detection frameworks.
Contributed to PhD training programs and professional events, strengthening the AI talent pipeline (ai-in-finance.eu, ai-in-finance.eu).
Enabled real-world AI deployments at ING, including novel LLM-based data extraction and federated risk models, aligning with regulatory and privacy requirements.
My joint professorship in the ING–UT collaboration focuses on integrating cutting-edge AI—with a focus on federated learning, synthetic data, reinforcement learning, XAI, and LLM pipelines—into ING’s risk management and analytics infrastructure. The partnership has produced tangible research outputs, student projects, and industry-grade AI models, strengthening ING’s AI capabilities and contributing to a vibrant academic–industry research ecosystem.