| Author: | Lo, Kok-ming Andrew |
| Title: | A credit risk management model of a financial institution using data mining |
| Degree: | M.Sc. |
| Year: | 2001 |
| Subject: | Risk management Data mining Hong Kong Polytechnic University -- Dissertations |
| Department: | Department of Computing |
| Pages: | 133 leaves : ill. ; 30 cm |
| Language: | English |
| Abstract: | To cope with the rapid changing external and internal environments in the domestic financial industry, it has been one of the most thorny issues for Credit Risk Management Experts to monitor bad performing loans as early as possible. Be an out performer, Bankers must be able to monitor their assets efficiently and effectively against bad debts. This dissertation demonstrates one of the practical ways for pro-active credit risk monitoring. Data Mining Tool C4.5 has been deployed to predict delinquency of Instalment Loan and a novel Genetic Algorithm - Steady State Genetic Algorithm (SSGA) was utilized to reclassify the loan portfolio from currently five to nine clusters. Average Hit Rate/accuracy for delinquency prediction and clustering is as high as 62.9% and 92.1% respectively. The beauty of using data mining is that it expands the horizon of Credit Risk Modeling by provide non-trivial rules which cannot not be normally expected from Credit Risk Management Experts. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| b15664351.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 3.62 MB | Adobe PDF | View/Open |
Copyright Undertaking
As a bona fide Library user, I declare that:
- I will abide by the rules and legal ordinances governing copyright regarding the use of the Database.
- I will use the Database for the purpose of my research or private study only and not for circulation or further reproduction or any other purpose.
- I agree to indemnify and hold the University harmless from and against any loss, damage, cost, liability or expenses arising from copyright infringement or unauthorized usage.
By downloading any item(s) listed above, you acknowledge that you have read and understood the copyright undertaking as stated above, and agree to be bound by all of its terms.
Please use this identifier to cite or link to this item:
https://theses.lib.polyu.edu.hk/handle/200/3834