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Visar resultat 1 - 5 av 21 uppsatser som matchar ovanstående sökkriterier.

1. 1. The Power of Credit Scoring: Evaluating Machine Learning and Traditional Models in Swedish Retail Banking

   Master-uppsats, Göteborgs universitet/Graduate School

   Författare :Emma von der Burg; Saga Strömberg; [2023-06-29]
   Nyckelord :;

   Sammanfattning : In this paper, we investigate and compare different credit scoring models, with special attention paid to machine learning approaches outperforming traditional models. We explore a recently proposed method called the PLTR model, which is a combination of machine learning and traditional logistic regression. LÄS MER

3. 2. Explainable Artificial Intelligence and its Applications in Behavioural Credit Scoring

   Master-uppsats, Stockholms universitet/Institutionen för data- och systemvetenskap

   Författare :Robert Iain Salter; [2023]
   Nyckelord :Behavioural Credit Scoring; Deep Learning; Machine Learning; Long Short-Term Memory; Default Prediction;

   Sammanfattning : Credit scoring is critical for banks to evaluate new loan applications and monitor existing customers. Machine learning has been extensively researched for this case; however, the adoption of machine learning methods is minimal in financial risk management. LÄS MER

4. 3. A Gradient Boosting Tree Approach for Behavioural Credit Scoring

   Master-uppsats, KTH/Matematisk statistik

   Författare :Axel Dernsjö; Ebba Blom; [2023]
   Nyckelord :Machine learning; Random forest; Uncertainty measure; Material development; Empirical Bayes; Maskininlärning; Random forest; Osäkerhetsmått; Materialutveckling; Empirical Bayes;

   Sammanfattning : This report evaluates the possibility of using sequential learning in a material development setting to help predict material properties and speed up the development of new materials. To do this a Random forest model was built incorporating carefully calibrated prediction uncertainty estimates. LÄS MER

5. 4. Deep Learning Approach for Time- to-Event Modeling of Credit Risk

   Master-uppsats, KTH/Matematisk statistik

   Författare :Mehnaz Kazi; Natalija Stanojlovic; [2022]
   Nyckelord :Survival Analysis; Credit Risk; Credit Scoring; Time-To-Event; Default Probability; Överlevnadsanalys; Kreditrisk; Kreditprövning; Tid-till-utfall; Sannolikhet för fallissemang;

   Sammanfattning : This thesis explores how survival analysis models performs for default risk prediction of small-to-medium sized enterprises (SME) and investigates when survival analysis models are preferable to use. This is examined by comparing the performance of three deep learning models in a survival analysis setting, a traditional survival analysis model Cox Proportional Hazards, and a traditional credit risk model logistic regression. LÄS MER

7. 5. Credit Scoring using Machine Learning Approaches

   Master-uppsats, Mälardalens universitet/Akademin för utbildning, kultur och kommunikation

   Författare :Bornvalue Chitambira; [2022]
   Nyckelord :Credit Scoring; Logistic Regression; Decision Trees; Artificial Neural Networks; Random forests; Support Vector Machine; k-nearest neighbour; cross validation; imbalanced dataset;

   Sammanfattning : This project will explore machine learning approaches that are used in creditscoring. In this study we consider consumer credit scoring instead of corporatecredit scoring and our focus is on methods that are currently used in practiceby banks such as logistic regression and decision trees and also compare theirperformance against machine learning approaches such as support vector machines (SVM), neural networks and random forests. LÄS MER