Sökning: "credit risk machine learning"

Visar resultat 1 - 5 av 42 uppsatser innehållade orden credit risk machine learning.

1. 1. Explainable Artificial Intelligence and its Applications in Behavioural Credit Scoring

   Master-uppsats, Stockholms universitet/Institutionen för data- och systemvetenskap

   Författare :Robert Iain Salter; [2023]
   Nyckelord :Behavioural Credit Scoring; Deep Learning; Machine Learning; Long Short-Term Memory; Default Prediction;

   Sammanfattning : Credit scoring is critical for banks to evaluate new loan applications and monitor existing customers. Machine learning has been extensively researched for this case; however, the adoption of machine learning methods is minimal in financial risk management. LÄS MER

3. 2. Portfolio Risk Modelling in Venture Debt

   Master-uppsats, KTH/Matematisk statistik

   Författare :John Eriksson; Jacob Holmberg; [2023]
   Nyckelord :Startup Default Probability; Venture Debt; Gaussian Copula; Value-at-Risk; Expected Shortfall; Exposure at Default; Loss Given Default; Forecast; Linear Dynamic System; ARIMA Time Series; Monte Carlo Simulation; Linear Regression; Central Limit Theorem;

   Sammanfattning : This thesis project is an experimental study on how to approach quantitative portfolio credit risk modelling in Venture Debt portfolios. Facing a lack of applicable default data from ArK and publicly available sets, as well as seeking to capture companies that fail to service debt obligations before defaulting per se, we present an approach to risk modeling based on trends in revenue. LÄS MER

4. 3. Credit Exposure Modelling Using Differential Machine Learning

   Master-uppsats, Lunds universitet/Matematisk statistik

   Författare :Måns Karp; Samuel Wagner; [2023]
   Nyckelord :Counterparty credit risk; Differential machine learning; Exposure modelling; Heston model; Option pricing; Mathematics and Statistics;

   Sammanfattning : Exposure modelling is a critical aspect of managing counterparty credit risk, and banks worldwide invest significant time and computational resources in this task. One approach to modelling exposure involves pricing trades with a counterparty in numerous potential future market scenarios. LÄS MER

5. 4. RNN-based Graph Neural Network for Credit Load Application leveraging Rejected Customer Cases

   Uppsats för yrkesexamina på avancerad nivå, Högskolan i Halmstad/Akademin för informationsteknologi

   Författare :Oskar Nilsson; Benjamin Lilje; [2023]
   Nyckelord :Machine Learning; Deep Learning; Reject Inference; GNN; GCN; Graph Neural Networks; RNN; Recursive Neural Network; LSTM; Semi-Supervised Learning; Encoding; Decoding; Feature Elimination;

   Sammanfattning : Machine learning plays a vital role in preventing financial losses within the banking industry, and still, a lot of state of the art and industry-standard approaches within the field neglect rejected customer information and the potential information that they hold to detect similar risk behavior.This thesis explores the possibility of including this information during training and utilizing transactional history through an LSTM to improve the detection of defaults. LÄS MER

7. 5. Peeking Through the Leaves : Improving Default Estimation with Machine Learning : A transparent approach using tree-based models

   Uppsats för yrkesexamina på avancerad nivå, Umeå universitet/Institutionen för matematik och matematisk statistik

   Författare :Elias Hadad; Angus Wigton; [2023]
   Nyckelord :Machine learning; Expected credit loss; Probability of default; ECL; PD; Risk Management; Credit Risk Management; Default Estimation; AI; Artificial intelligence; Fintech; Supervised learning; Decision tree; Random forest; XG boost; Transparency; Machine learning transparency;

   Sammanfattning : In recent years the development and implementation of AI and machine learning models has increased dramatically. The availability of quality data paving the way for sophisticated AI models. Financial institutions uses many models in their daily operations. LÄS MER