Emre Saylan
Enchancing Credit Insurance with Glassbox Models and LLMs for Transparent Decision-Making.
Rel. Daniele Apiletti. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2024
Abstract
Credit insurance is a critical component of the financial ecosystem, protecting busi- nesses against the risk of non-payment for goods and services provided on credit. This insurance promotes economic stability by allowing companies to extend credit with re- duced risk, thus supporting business growth and resilience. This thesis, conducted in collaboration with Allianz Trade the sector leader in credit insurance focuses on develop- ing an explainable artificial intelligence (AI) framework to automate and enhance credit limit decision-making within the industry. The thesis centers on building a transparent AI system that meets both predictive accuracy and interpretability requirements essential to credit insurance.
To achieve this, an Explainable Boosting Machine (EBM) was designed and trained on a comprehen- sive dataset of approximately three million credit limit requests across multiple Euro- pean countries and industry sectors
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