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The Predictive Credit Risk Model with Implementation of Basel Regulations

Li, Fan LU (2017) NEKP03 20171
Department 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.
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author
Li, Fan LU
supervisor
organization
course
NEKP03 20171
year
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}},
}