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Advancing Credit Risk Analysis through Machine Learning Techniques : Utilizing Predictive Modeling to Enhance Financial Decision-Making and Risk Assessment

Assessment of credit risk is crucial for the financial stability of banks, directly influencing their lending policies and economic resilience. This thesis explores advanced techniques for predictive modeling of Loss Given Default (LGD) and credit losses within major Swedish banks, with a focus on sophisticated methods in statistics and machine learning. The study specifically evaluates the effectiveness of various models, including linear regression, quantile regression, extreme gradient boosting, and ANN, to address the complexity of LGD’s bimodal distribution and the non-linearity in credit loss data. Key findings highlight the robustness of ANN and XGBoost in modeling complex data patterns, offering significant improvements over traditional linear models. The research identifies critical macroeconomic indicators—such as real estate prices, inflation, and unemployment rates—through an Elastic Net model, underscoring their predictive power in assessing credit risks.

Identiferoai:union.ndltd.org:UPSALLA1/oai:DiVA.org:umu-226128
Date January 2024
CreatorsLampinen, Henrik, Nyström, Isac
PublisherUmeå universitet, Institutionen för matematik och matematisk statistik
Source SetsDiVA Archive at Upsalla University
LanguageEnglish
Detected LanguageEnglish
TypeStudent thesis, info:eu-repo/semantics/bachelorThesis, text
Formatapplication/pdf
Rightsinfo:eu-repo/semantics/openAccess

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