DC2: Modelling green credit scores for a network of retail and business clients

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.

Secondments (2)

Recruitment & Hosting Details

DC 2 UTW University of Twente M9 36

3

DC 2 UTW UTW Month 9 36 months D 5.1, 5.2

Modelling green credit scores for a network of retail and business clients (WP 5)

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: Swedbank, Prof. Dr. Tadas Gudaitis, M21, 6 months, ESG and credit score modelling

BIS, Rafael Schmidt, M27, 18 months, contribute macro-economic datasets, ongoing projects as well as overall expertise in banking

supervision

Fellow Host institution PhD enrolment Start date Duration Deliverables

3

Deliverables