Possible Application of Data Mining Technology in Supporting Term Loan Risk Assessment: The Case of United Bank S.C.
| dc.contributor.advisor | VNV , Manoj (Prof.) | |
| dc.contributor.author | Tadesse, Samson | |
| dc.date.accessioned | 2020-06-15T10:49:10Z | |
| dc.date.accessioned | 2023-11-18T12:46:10Z | |
| dc.date.available | 2020-06-15T10:49:10Z | |
| dc.date.available | 2023-11-18T12:46:10Z | |
| dc.date.issued | 2009-01 | |
| dc.description.abstract | A Conunercial Bank is a financial intermediary that holds deposits for individuals and businesses in the form of checking and savings accounts and certificates of deposit of varying maturities while it issues loans in the form of personal and business as well as mortgages. It arises due to a debtor's non-payment of a loan or other line of credit. In order to control and manage the risk, banks normally have discipline called risk management. Hence it is very important to develop and implement an effective technology that can support risk management. This research focused on the application of data mining techniques in supporting loan risk assessment taking as case study United Bank Share Company. It used two data mining techniques namely, decision tree and neural network. Different decision tree models using j48 algorithm were constructed during the experiments and among them a tree with overall accuracy of 95 .65% with conceivable rule was selected. The important attributes that were identified by the selected decision tree were: Networking capital, Current Ratio, Total Asset, TLfA, Current Liability, Collateral Value, Years in Business, Number of prior term loans settled, Performance of term Preordains, Collateral Type, Credit Relationship with other bank, Trade Sector, Performance in other types of loan and Current Asset. Based on the above selected attributes different types of neural network models with multilayer perception algorithm were constructed and a model that maximizes the accuracy in predicting poor payment performance was selected with over all accuracy of 92.83%. When evaluation was done, the overall accuracy of decision tree found better than the neural network even if nether research is needed in addition the result of decision tree is more interpret able than neural network. In general the result showed the possible application of data mining in loan risk assessment term joan. | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/12345678/21605 | |
| dc.language.iso | en | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Information Science | en_US |
| dc.title | Possible Application of Data Mining Technology in Supporting Term Loan Risk Assessment: The Case of United Bank S.C. | en_US |
| dc.type | Thesis | en_US |