Sökning: "Credit Scoring Model"
Visar resultat 1 - 5 av 21 uppsatser innehållade orden Credit Scoring Model.
1. The Power of Credit Scoring: Evaluating Machine Learning and Traditional Models in Swedish Retail Banking
Master-uppsats, Göteborgs universitet/Graduate SchoolSammanfattning : 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
2. Explainable Artificial Intelligence and its Applications in Behavioural Credit Scoring
Master-uppsats, Stockholms universitet/Institutionen för data- och systemvetenskapSammanfattning : 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
3. A Gradient Boosting Tree Approach for Behavioural Credit Scoring
Master-uppsats, KTH/Matematisk statistikSammanfattning : 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
4. Deep Learning Approach for Time- to-Event Modeling of Credit Risk
Master-uppsats, KTH/Matematisk statistikSammanfattning : 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
5. Credit Scoring using Machine Learning Approaches
Master-uppsats, Mälardalens universitet/Akademin för utbildning, kultur och kommunikationSammanfattning : 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