Difference between revisions of "MSCA Individual Research Projects"
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= Strengthening European financial service providers through applicable reinforcement learning = | = 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. | ||
| + | == Planned Secondments == | ||
| + | *CAR, Altin Kadareja (CEO), M6, 18 months, applied research on Fintech innovations with Deep learning | ||
| + | *ECB, Lukasz Kubicki, M27, 4 months, training on EU principles, supervision policies and research | ||
| + | |||
= Modelling green credit scores for a network of retail and business clients = | = Modelling green credit scores for a network of retail and business clients = | ||
= Industry standard for blockchain = | = Industry standard for blockchain = | ||
Revision as of 12:40, 18 September 2023
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.
Planned Secondments
- CAR, Altin Kadareja (CEO), M6, 18 months, applied research on Fintech innovations with Deep learning
- ECB, Lukasz Kubicki, M27, 4 months, training on EU principles, supervision policies and research