Date of Award
2019
Document Type
Open Access Master's Report
Degree Name
Master of Science in Mathematical Sciences (MS)
Administrative Home Department
Department of Mathematical Sciences
Advisor 1
Benjamin Ong
Advisor 2
Gowtham
Committee Member 1
Allan Struthers
Abstract
A key activity within the banking industry is to extend credit to customers, hence,
credit risk analysis is critical for nancial risk management. There are various methods
used to perform credit risk analysis. In this project, we analyze German and
Australian nancial data from UC Irvine Machine Learning repository, reproducing
results previously published in literature. Further, using the same dataset and various
machine learning algorithms, we attempt to create better models by tuning available
parameters, however, our results are at best comparable to published results.
In this report, we have explained the algorithms and mathematical framework that
goes behind developing the machine learning models. We conclude with a discussion
and comparision of summarizing the best approach to classify these datasets. K
- Nearest Neighbors (KNN), Logistic Regression (LR), Naive Byaes Classication,
Support Vector Machine (SVM), Classication Trees and Articial Neural Networks
(ANN) are the machine learning models used for this report.
Recommended Citation
Thanawala, Dhruv Dhanesh, "CREDIT RISK ANALYSIS USING MACHINE LEARNING AND NEURAL NETWORKS", Open Access Master's Report, Michigan Technological University, 2019.