Essays about: "probability of default"

Showing result 16 - 20 of 97 essays containing the words probability of default.

  1. 16. 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

  3. 17. ESTIMATING AND EVALUATING THE PROBABILITY OF DEFAULT – A MACHINE LEARNING APPROACH

    University essay from Uppsala universitet/Statistiska institutionen

    Author : Andreas Hild; [2021]
    Keywords : ;

    Abstract : In this thesis, we analyse and evaluate classification models for panel credit risk data. Variables are selected based on results from recursive feature elimination as well as economic reasoning where the probability of default is estimated. READ MORE

  4. 18. 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. 19. BNPL Probability of Default Modeling Including Macroeconomic Factors: A Supervised Learning Approach

    University essay from KTH/Matematisk statistik

    Author : Patrik Hardin; Robert Ingre; [2021]
    Keywords : Buy Now Pay Later; IFRS 9; Probability of Default; Expected Credit Loss; Macroeconomic factors; Machine Learning; Artificial Neural Network; XGBoost;

    Abstract : In recent years, the Buy Now Pay Later (BNPL) consumer credit industry associated with e-commerce has been rapidly emerging as an alternative to credit cards and traditional consumer credit products. In parallel, the regulation IFRS 9 was introduced in 2018 requiring creditors to become more proactive in forecasting their Expected Credit Losses and include the impact of macroeconomic factors. READ MORE

  6. 20. SUPPORT VECTOR MACHINE VS. LOGISTIC REGRESSION FOR PREDICTING MORTGAGE DEFAULTS

    University essay from Lunds universitet/Matematisk statistik

    Author : Aram Olvbo; [2021]
    Keywords : Technology and Engineering;

    Abstract : Mortgage loan providers estimate the credit risks it caries when approving a mortgage loan to their clients. Further, defaulting a mortgage loan is a risk that has been calculated through decades using statistical models. READ MORE