File
Credit risk modeling: A comparative analysis of artificial and deep neural networks
Digital Document
Content type |
Content type
|
---|---|
Collection(s) |
Collection(s)
|
Resource Type |
Resource Type
|
Genre |
Genre
|
Origin Information |
|
---|
Persons |
Author (aut): Vasudevan, Marriappan
Thesis advisor (ths): Mahbobi, Mohammad
Degree committee member (dgc): Kimiagari, Salman
Degree committee member (dgc): Zhang, Li
Degree committee member (dgc): Tomal, Jabed
|
---|---|
Organizations |
Degree granting institution (dgg): Thompson Rivers University. Bob Gaglardi School of Business and Economics
|
Abstract |
Abstract
Credit risk assessment plays a major role in the banks and financial institutions to prevent counterparty risk failure. One of the primary capabilities of a robust risk management system must be detecting the risks earlier, though many of the bank systems today lack this key capability which leads to further losses (MGI, 2017). In searching for an improved methodology to detect such credit risk and increasing the lacking capabilities earlier, a comparative analysis between Deep Neural Network (DNN) and machine learning techniques such as Support Vector Machines (SVM), K-Nearest Neighbours (KNN) and Artificial Neural Network (ANN) were conducted. The Deep Neural Network used in this study consists of six layers of neurons. Further, sampling techniques such as SMOTE, SVM-SMOTE, RUS, and All-KNN to make the imbalanced dataset a balanced one were also applied. Using supervised learning techniques, the proposed DNN model was able to achieve an accuracy of 82.18% with a ROC score of 0.706 using the RUS sampling technique. The All KNN sampling technique was capable of achieving the maximum true positives in two different models. Using the proposed approach, banks and credit check institutions can help prevent major losses occurring due to counterparty risk failure. |
---|---|
Language |
Language
|
Degree Name |
Degree Name
|
---|---|
Degree Level |
Degree Level
|
Department |
Department
|
Institution |
Institution
|
Handle |
Handle
Handle placeholder
|
---|
Use and Reproduction |
Use and Reproduction
author
|
---|---|
Rights Statement |
Rights Statement
|
Keywords |
Keywords
credit risk
deep neural network
artificial neural network
support vector machines
sampling techniques
|
---|---|
Subject Topic |
Subject Topic
|