 A network based fintech inclusion platformDaniel Ahelegbey, Paolo Giudici and Valentino Pediroda Socio-Economic Planning Sciences, 2023, vol. 87, issue PB Abstract: The paper evaluates, from a sustainable finance viewpoint, a machine learning model implemented in a fintech platform, whose aim is to assign credit ratings. The aim of the model is to learn from both micro economic data and macro economic trends the credit rating of companies that ask for credit. We show that the proposed model is able to reward the companies that have better financial performances with better ratings and, therefore, a higher probability/lower cost of obtaining credit. At the same time, the model correctly takes into account the overall evolution of the economy, favoring financial inclusion for the more penalized economic sectors, particularly during crisis times. The model, its application to credit rating, and its evaluation, are illustrated with reference to more than 100,000 European companies before and during the COVID-19 pandemic crisis. The results shows that, while the impact of the financial variables does not change over time, and particularly during the pandemic, the impact of sectors changes considerably, favoring financial inclusion and resilience. Keywords: COVID-19 pandemic; Credit rating models; Financial technologies (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:soceps:v:87:y:2023:i:pb:s0038012123000551 DOI: 10.1016/j.seps.2023.101555 Access Statistics for this article Socio-Economic Planning Sciences is currently edited by Barnett R. Parker More articles in Socio-Economic Planning Sciences from Elsevier |
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