Essays about: "credit payments"

Showing result 6 - 10 of 45 essays containing the words credit payments.

  1. 6. Prediction of Short-term Default Probability of Credit Card Invoices Using Behavioural Data

    University essay from KTH/Matematisk statistik

    Author : Billy Lu; [2022]
    Keywords : Probability of Default; Credit Risk; Short-term Default Prediction; Machine Learning; Gradient Boosting; Thresholding; Sannolikheten för Fallissemang; Kreditrisk; Kortsiktig Fallissemang Prediktion; Maskininlärning; Gradientförstärkning; Tröskling;

    Abstract : Probability of Default (PD) is a standard metric to model and monitor credit risk, a major risk facing financial institutions. Traditional PD models are used to forecast risk levels in the long-term, while short-term PD predictions are rarer, but they can support management decisions on an operational level. READ MORE

  2. 7. Automated Outlier Detection for Credit Risk KPI Time Series in E-commerce : A Case Study on the Business Value and Obstacles of Automated Outlier Detection

    University essay from KTH/Skolan för industriell teknik och management (ITM)

    Author : Jennifer Lindberg; [2022]
    Keywords : Credit Risk Management; Underwriting; Automation; Outlier Detection; KPI; Monitoring; Kresditriskhantering; underwriting; automatisering; outlier detection; KPI; monitorering;

    Abstract : E-commerce has grown significantly the last decade, and made a considerable leap during Covid19. The final step in e-commerce is payments, and as a result of this, credit risk management in real-time has become increasingly important. READ MORE

  3. 8. Dark Patterns in Digital Buy Now Pay Later Services

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Isabella Johannesson; [2021]
    Keywords : Buy Now Pay Later; Dark Patterns; Digital Payments; Digital Invoice Services; Ethics; Pay After Delivery;

    Abstract : Buy Now Pay Later (BNPL) is a financial service whereby customers defer payment on a purchase against a short-term debt. While BNPL services have a long history, digital invoice services are now the largest market for BNPL. For the study, two of the largest providers in Sweden, and their checkout interfaces were reviewed for dark patterns. READ MORE

  4. 9. Predicting Subprime Customers' Probability of Default Using Transaction and Debt Data from NPLs

    University essay from KTH/Matematisk statistik

    Author : Lai-Yan Wong; [2021]
    Keywords : 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;

    Abstract : 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. READ MORE

  5. 10. In-store mobile payment in western countries : A study on readiness and adoption

    University essay from KTH/Skolan för industriell teknik och management (ITM)

    Author : XINYU LI; YI LIU; [2021]
    Keywords : ;

    Abstract : The purpose of this master thesis is to investigate the characteristics that made in-store mobile payment in China fast and highly adopted and to give suggestions for developing in-store mobile payment in western countries with the readiness of these characteristics. From the literature study, several characteristics are identified, including trust in mobile payments, expectation of fast transactions, quick and easy authentication, willingness to share personal data, low level of consumer credit, high adoption of QR codes for payment, friendly development environment, price-sensitive, easily influenced by other people, high cash preference, low adoption of NFC, and low availability of different payment options. READ MORE