Date of Award


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


Committee Member 1

Allan Struthers


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.