A credit risk management model of a financial institution using data mining

Pao Yue-kong Library Electronic Theses Database

A credit risk management model of a financial institution using data mining


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: Dept. of Computing
Pages: 133 leaves : ill. ; 30 cm
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b1566435
URI: http://theses.lib.polyu.edu.hk/handle/200/3834
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.

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