Difference between revisions of "MSCA Individual Research Projects"
| Line 19: | Line 19: | ||
= Modelling green credit scores for a network of retail and business clients = | = Modelling green credit scores for a network of retail and business clients = | ||
| + | *'''Host institution:''' University of Twente. | ||
| + | |||
| + | *'''Starting month:''' M6. | ||
| + | |||
| + | *'''Duration:''' 36 months. | ||
| + | |||
| + | *'''Pillar 1:''' Sustainable finance (UNA, 4 ECTs), Work Package 5 | ||
| + | |||
| + | *'''Work Packages Included:''' 5, 6, 7, 8 | ||
| + | |||
| + | == Objectives == | ||
| + | Some markets use green credit scores to assess SME credit risk in sustainable and circular economies. Simultaneously, network customers' default likelihood has been studied. This study develops and deploys green credit score models that account for customers' networks. We show the impact and give financial institutions methods to improve credit risk assessment and access. | ||
| + | == Expected Results == | ||
| + | Green credit score models will be developed and implemented. These models inform SMEs about their carbon | ||
| + | footprint, their main risks in a low-carbon economy, and how to mitigate them. SMEs leading on sustainability could gain easier access to capital by demonstrating positive relationships between creditworthiness and sustainability, creating a fairer credit risk assessment that explicitly factors in sustainability metrics and encouraging low-carbon measures. | ||
| + | == Planned Secondments == | ||
| + | *SWE, Prof. Dr. Tadas Gudaitis, M12, 18 months, ESG and credit score modelling | ||
| + | *ECB, Lukasz Kubicki, M33, 4 months, exposure to globally leading central bank, research training on EU principles, supervision | ||
| + | |||
= Industry standard for blockchain = | = Industry standard for blockchain = | ||
= A recommender system to re-orient investments towards more sustainable technologies = | = A recommender system to re-orient investments towards more sustainable technologies = | ||
Revision as of 12:51, 18 September 2023
Strengthening European financial service providers through applicable reinforcement learning
- Host institution: University of Twente.
- Starting month: M3.
- Duration: 36 months.
- Pillar 1: Introduction to AI for financial applications (WWU, 4 ECTs), Work Package 2
- Work Packages Included: 2, 6, 7, 8
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
- Host institution: University of Twente.
- Starting month: M6.
- Duration: 36 months.
- Pillar 1: Sustainable finance (UNA, 4 ECTs), Work Package 5
- Work Packages Included: 5, 6, 7, 8
Objectives
Some markets use green credit scores to assess SME credit risk in sustainable and circular economies. Simultaneously, network customers' default likelihood has been studied. This study develops and deploys green credit score models that account for customers' networks. We show the impact and give financial institutions methods to improve credit risk assessment and access.
Expected Results
Green credit score models will be developed and implemented. These models inform SMEs about their carbon footprint, their main risks in a low-carbon economy, and how to mitigate them. SMEs leading on sustainability could gain easier access to capital by demonstrating positive relationships between creditworthiness and sustainability, creating a fairer credit risk assessment that explicitly factors in sustainability metrics and encouraging low-carbon measures.
Planned Secondments
- SWE, Prof. Dr. Tadas Gudaitis, M12, 18 months, ESG and credit score modelling
- ECB, Lukasz Kubicki, M33, 4 months, exposure to globally leading central bank, research training on EU principles, supervision