University of Twente Student Theses
Modelling green credit scores for Retail Clients and SMEs
Nikolov, Aleksandar (2024) Modelling green credit scores for Retail Clients and SMEs.
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| Abstract: | Forecasting credit risk for small and medium-sized enterprises (SMEs) presents significant challenges for financial institutions. Accurately assessing the creditworthiness of SMEs is crucial for reducing financial risk and ensuring the stability of lending practices. While traditional credit risk models focus on financial metrics, the integration of sustainability-related features into these models remains underexplored. Incorporating sustainable practices can enhance the robustness and relevance of credit risk assessments, offering a more comprehensive evaluation of an SME’s overall risk profile. This research investigates the application of machine learning models and techniques in forecasting credit risk for SMEs. A systematic literature review and analysis were conducted to identify the current applications of machine learning in credit risk forecasting and to examine sustainable-related features. The study addresses environmental factors, aiming to integrate these aspects into credit scoring practices to improve model robustness and sustainability. The analysis revealed that while several machine learning models are effective in predicting SME credit risk, incorporating sustainable-related features can significantly enhance model accuracy and reliability. The study provides a detailed understanding of the effectiveness and challenges of existing models and offers insights into potential improvements. By integrating environmental factors, the research contributes to the development of more sustainable and comprehensive credit scoring practices. |
| Item Type: | Essay (Bachelor) |
| Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
| Subject: | 06 documentary information |
| Programme: | Business & IT BSc (56066) |
| Link to this item: | https://purl.utwente.nl/essays/100802 |
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