DC1: Strengthening European financial service providers through applicable reinforcement learning

Objectives

Reinforcement Learning (RL) has become popular for automating uncertain decision-making in complex environments. Deep reinforcement learning can make impressive algorithmic decisions in closed environments, but real-world applications in open environments are harder. This project examines how RL can advance digital finance.

Expected Results

The project will address several RL implementation issues in digital finance. Utility-based RL deliverables will improve financial decision-making by developing multi-criteria analysis, extreme scenarios, and risk management methods. RL in decision-support will be optimised for explainability, regulatory compliance, model abstractions, and human judgement. We will also examine technological challenges like Digital Twin environments, machine learning pipelines, and digital finance ecosystem integration.

Secondments (2)

Recruitment & Hosting Details

DC 1 UTW University of Twente M9 36

2

DC 1 UTW UTW Month 9 36 months D 2.1, 2.2

Strengthening European financial service providers through applicable reinforcement learning (WP 3)

Objectives: Reinforcement Learning (RL) has become popular for automating uncertain decision-making in complex environments. Deep

reinforcement learning can make impressive algorithmic decisions in closed environments, but real-world applications in open

environments are harder. This project examines how RL can advance digital finance.

Expected Results: The project will address several RL implementation issues in digital finance. Utility-based RL deliverables will improve

financial decision-making by developing multi-criteria analysis, extreme scenarios, and risk management methods. RL in decision-support

will be optimised for explainability, regulatory compliance, model abstractions, and human judgement. We will also examine technological

challenges like Digital Twin environments, machine learning pipelines, and digital finance ecosystem integration.

Planned secondments: Cardo AI, Altin Kadareja (CEO), M2755, 6 months, applied research on Fintech innovations with Deep learning

ECB, Lukasz Kubicki, M33, 12 months, exposure to globally leading central bank, research training on EU principles, supervision

Fellow Host institution PhD enrolment Start date Duration Deliverables

2

Deliverables