Sökning: "Credit Scoring Model"

Visar resultat 1 - 5 av 18 uppsatser innehållade orden Credit Scoring Model.

1. 1. 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

3. 2. 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

4. 3. Kreditbedömning av små aktiebolag : En kvalitativ studie om kreditbedömning ur bankers, kreditupplysningsföretags och revisorers perspektiv

   Kandidat-uppsats, Södertörns högskola/Institutionen för samhällsvetenskaper

   Författare :Muskan Joon; Dana Moradi Asl; [2022]
   Nyckelord :Credit assessment; lending; credit reporting companies; banks; audit; 5C; CAMPARI; scoring; Kreditbedömning; kreditgivning; kreditupplysningsföretag; banker; revision; 5C; CAMPARI; scoring;

   Sammanfattning : Idag finns det cirka 1,2 miljoner företag i Sverige varav 96% av dessa är små företag. Svenska banker lånade ut cirka 3 000 miljarder kronor till företag 2021. Små företag har svårt för att finansiera sin verksamhet och vänder sig därför till finansiärer som banker för lån. LÄS MER

5. 4. The value of detailed product information in credit risk prediction : A case study applied to Klarna’s Pay Later orders in Sweden

   Master-uppsats, KTH/Skolan för industriell teknik och management (ITM)

   Författare :Mimmi Andersson; Louise von Sydow Yllenius; [2022]
   Nyckelord :Credit Risk Management; Consumer Credit; BNPL; Credit Scoring; Alternative Data; Product Category; Product Type; Responsible Lending; e-commerce; Kreditriskbedömning; BNPL; konsumenkredit; alternativ data; produktkategori; produkttyp; hållbar kreditgivning; onlinehandel;

   Sammanfattning : In this study we propose to enhance the predictive power of a Buy Now, Pay Later (BNPL) consumer credit scorecard by leveraging detailed product information. The object of analys is in this study is Klarna Bank AB, which is the largest retail finance provider in Sweden. LÄS MER

7. 5. Predicting Subprime Customers' Probability of Default Using Transaction and Debt Data from NPLs

   Master-uppsats, KTH/Matematisk statistik

   Författare :Lai-Yan Wong; [2021]
   Nyckelord :Credit Scoring Model; Probability of Default; Payment Behaviour; Subprime Customer; Non-performing Loan; Logistic Regression; Regularization; Feature Selection; Kreditvärdighetsmodell; Sannolikhet för Fallissemang; Betalningsbeteende; Högriskkunder; Nödlidandelån; Logistik Regression; Regularisering; Variabelselektion;

   Sammanfattning : This thesis aims to predict the probability of default (PD) of non-performing loan (NPL) customers using transaction and debt data, as a part of developing credit scoring model for Hoist Finance. Many NPL customers face financial exclusion due to default and therefore are considered as bad customers. LÄS MER