Sökning: "Credit Scoring Model"

Visar resultat 6 - 10 av 21 uppsatser innehållade orden Credit Scoring Model.

  1. 6. 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

  3. 7. 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

  4. 8. 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

  5. 9. Weight of evidence transformation in credit scoring models: How does it affect the discriminatory power?

    Magister-uppsats, Lunds universitet/Statistiska institutionen

    Författare :Rickard Persson; [2021]
    Nyckelord :Credit Credit-scoring WOE weight of evidence Credit-scoring models; Mathematics and Statistics;

    Sammanfattning : Weight of evidence (WOE) transformation has been used for several decades in the credit industry. However, despite its widespread use, it has, surprisingly, been an overlooked approach in published literature. In this paper, we, therefore, investigate what effect WOE transformation has on the discriminatory power of a credit-scoring model. LÄS MER

  7. 10. Explaining Automated Decisions in Practice : Insights from the Swedish Credit Scoring Industry

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

    Författare :Filip Matz; Yuxiang Luo; [2021]
    Nyckelord :Explainable Artificial Intelligence; XAI implementation; Automated Decisions; Understandability; Interpretability; Practical Framework; Credit Scoring; Förklarbar artificiell intelligens; XAI implementation; automatiserade beslut; förklarbarhet; praktiskt ramverk; kreditupplysning;

    Sammanfattning : The field of explainable artificial intelligence (XAI) has gained momentum in recent years following the increased use of AI systems across industries leading to bias, discrimination, and data security concerns. Several conceptual frameworks for how to reach AI systems that are fair, transparent, and understandable have been proposed, as well as a number of technical solutions improving some of these aspects in a research context. LÄS MER