Possible Application of Data Mining Technology in Supporting Term Loan Risk Assessment: The Case if United Bank S.C.
| dc.contributor.advisor | VNV, Manoj (Professor) | |
| dc.contributor.author | Tadesse, Samson | |
| dc.date.accessioned | 2018-11-29T14:25:58Z | |
| dc.date.accessioned | 2023-11-29T04:56:55Z | |
| dc.date.available | 2018-11-29T14:25:58Z | |
| dc.date.available | 2023-11-29T04:56:55Z | |
| dc.date.issued | 2009-01 | |
| dc.description.abstract | A Commercial 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, TL/TA, Current Liability, Collateral Value, Years in; Business, Number of prior term loans settled, Performance of term PriorLoans, 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 perceptron 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 further research is needed. In addition the result of decision tree is more interpretable than neural network. In general the result showed the possible application of data mining in loan risk assessment term loan. | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/123456789/14709 | |
| dc.language.iso | en | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Data Mining Technology in Supporting Term Loan Risk | en_US |
| dc.title | Possible Application of Data Mining Technology in Supporting Term Loan Risk Assessment: The Case if United Bank S.C. | en_US |
| dc.type | Thesis | en_US |