The Predictive Credit Risk Model with Implementation of Basel Regulations
(2017) NEKP03 20171Department of Economics
- Abstract
- Credit risk is one of the most important issues in the field of financial risk management.This topic has become a major focus by the financial institutions and regulatory authorities. It is critical for large banking institutions to adapt a robust credit risk assessment. In this paper, machine learning algorithms are compared on predicting the housing loan default status. The compared methods are: Logistic Regression Model, Support Vector Machine Model and Random Forest Model.
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8915603
- author
- Li, Fan LU
- supervisor
- organization
- course
- NEKP03 20171
- year
- 2017
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- Basel IRB, Credit Risk, Machine Learning.
- language
- English
- id
- 8915603
- date added to LUP
- 2017-06-15 08:36:06
- date last changed
- 2017-06-15 08:36:06
@misc{8915603, abstract = {{Credit risk is one of the most important issues in the field of financial risk management.This topic has become a major focus by the financial institutions and regulatory authorities. It is critical for large banking institutions to adapt a robust credit risk assessment. In this paper, machine learning algorithms are compared on predicting the housing loan default status. The compared methods are: Logistic Regression Model, Support Vector Machine Model and Random Forest Model.}}, author = {{Li, Fan}}, language = {{eng}}, note = {{Student Paper}}, title = {{The Predictive Credit Risk Model with Implementation of Basel Regulations}}, year = {{2017}}, }