Possible Application of Data Mining Technology in Supporting Credit Risk Assessment: the Case of Nib International Bank S.C.
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Date
204-07
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Addis Ababa University
Abstract
Financial institutions in a nation playa crucial role in the development of its
economy. The banking sector as one type offinancial institution is indisputably the
new ji'ontier of economic development in a country. In this respect, banking has to
be sound and safe jar its clistomers as well as jar the stability of the currency and
economy of a counl1y. One factor that affects the well fimctioning of the banking
sector is credit risk. This factor is also a general problem among commercial
banks in Ethiopia.
In order to deal with high default rates banks in other countries are making use of
data mining. The possible application of data mining in the commercial banking
sector of Ethiopia has also been tested by the use of neural network techflique. As
credit risk is a risk type that bank managers give more emphasis in the loan
disbursement process because it is one of the major reasons that cause a bank to
fail, the study of the possible application of data mining needed jilrther
investigation. To this end, the present study focuses on the application of data
mining to support credit risk assessment taking as a case study Nib International
Bank S.C.(NIB). In doing so the aim of this research was to assess the potential
applicability of decision tree technique to help in the loan disbursement decisionmaking
process of banks.
The methodology used for this research had three basic steps. These were
collecting of data, data preparation, and model building and testing. The required
data was selected and extracted ji'01l/ Nib International Bank records. Then, data
preparation tasks (such as data tram!ormation, deriving of new fields, and
handling of missing variables) were undertaken. Decision tree data mining
technique was employed to build and test models.
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Several decision tree models were built and testedfor their classification accuracy
and the model with encouraging results was taken to generate rules to support
credit decision makers and the procedures adopted are described in this document .The peliormance of the developed model is validated using new datasets and its
predictive accuracy is also tested. The result shows that the use of decision tree
technique produces rules for justifiable credit decision-making and that it is the
best technique that needs to be adopted for NIB bank as it presents a means of
providing explanation for proposed decisions as compared to neural network
techniqlles.
A 1/ things considered, the existence of an electronic system to support the credit
risk assessment of NIB bank will promote the services of the bank to its customers
as well as minimize risk
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