Sökning: "Credit Risk"
Visar resultat 1 - 5 av 565 uppsatser innehållade orden Credit Risk.
1. Factors Influencing the Implementation of Information Security Risk Management : A case study of Nigerian Commercial Banks
Master-uppsats, Luleå tekniska universitet/Institutionen för system- och rymdteknikSammanfattning : The banking industry is one of the critical infrastructures in any economy. The services rendered by banks are systematically based on innovation, products, and technology to leverage their services. Several associated risks come along with the rendering of these banking services. LÄS MER
2. Expect the Unexpected: Measuring Noise & Bias in the Credit Assessment Process
Kandidat-uppsats, Lunds universitet/Företagsekonomiska institutionenSammanfattning : The purpose of the thesis is to measure how bias impacts loan officers’ decision-making upon assessing mortgage applications and the level of noise embedded within the process. Quantitative data were collected from 15 loan officers working at three different branches at Handelsbanken answering a questionnaire based on fictional mortgage applications. LÄS MER
3. The Expected Credit Loss Model's Impact on the Cyclicality of Credit Supply: A Study of the Implementation of IFRS 9
D-uppsats, Handelshögskolan i Stockholm/Institutionen för redovisning och finansieringSammanfattning : The accounting standard for recognizing loan loss provisions changed in 2018 from IAS 39 to IFRS 9. IFRS 9 introduced the expected credit loss model (ECL), intended to be an improved alternative to its predecessor, the incurred credit loss model (ICL), which was criticized for the "too little, too late" provisioning during the 2008 financial crisis. LÄS MER
4. Credit risk modelling and prediction: Logistic regression versus machine learning boosting algorithms
Kandidat-uppsats, Uppsala universitet/Statistiska institutionenSammanfattning : The use of machine learning methods in credit risk modelling has been proven to yield good results in terms of increasing the accuracy of the risk score as- signed to customers. In this thesis, the aim is to examine the performance of the machine learning boosting algorithms XGBoost and CatBoost, with logis- tic regression as a benchmark model, in terms of assessing credit risk. LÄS MER
5. Neural Networks for Credit Risk and xVA in a Front Office Pricing Environment
Master-uppsats, Lunds universitet/Matematisk statistikSammanfattning : We present a data-driven proof of concept model capable of reproducing expected counterparty credit exposures from market and trade data. The model has its greatest advantages in quick single-contract exposure evaluations that could be used in front office xVA solutions. The data was generated using short rates from the Hull-White One-Factor model. LÄS MER
