The Use of Data Mining to Predict the Loan Repayment Risk: the Case of Oromia Credit and Saving Share Company
| dc.contributor.advisor | Mulugeta, Wondwossen (PhD) | |
| dc.contributor.author | Feyissa, Ketema | |
| dc.date.accessioned | 2019-07-26T14:38:18Z | |
| dc.date.accessioned | 2023-11-18T12:47:25Z | |
| dc.date.available | 2019-07-26T14:38:18Z | |
| dc.date.available | 2023-11-18T12:47:25Z | |
| dc.date.issued | 2018-09-04 | |
| dc.description.abstract | Data Mining is the process of extracting useful patterns from the huge amount of database and many data mining techniques are used for mining these patterns. Data mining is still a technology of having great expectations to enable the organizations to take more benefit of their huge data bases. Recently, one of the remarkable facts in microfinance institute is the rapid growth of data and this microfinance data is expanding quickly without any advantage to the organization for decision making. The main aim of this research work is utilizing of data mining by developing classification model in order to predict customer loan repayment behavior for loan risk that could help for better decision making and maximize the benefit of the microfinance from organizational datasets. In this research the applicability of classification data mining techniques to implement customer loan repayment prediction model in Oromia credit and saving share company have been explored within the approach of CRISP-DM process model. After understanding business objectives of the organization, customer profile data are extracted, collected, cleaned, transformed, integrated and finally prepared for experimentation with the classification algorithm to develop a prediction model. The final dataset prepared for experimentation have 147,285 customer profile instances. The findings of this study revealed all the models built from J48 Decision Tree classifier, Naïve Bayes classifier and Neural Network have high classification accuracy and are generally comparable in predicting customer loan repayment. However, comparison that is based on their performance accuracy suggests that the J48 model performs slightly better in predicting customer loan repayment with classification accuracy of 98.89%. In this study the following attributes: customer follow up, purpose of loan, distance of customers from microfinance center and amount of loan disburse are the most interesting attributes in determining customer loan repayment prediction. The result of this study used efficiently to model and predict customer loan repayment. Based on the findings of the study, we recommend that microfinance institutions should adopt data mining to enhance their performance. The organizations need to make sure that there is enough data to analyze as well as assure quality of data. Organizations should ensure that the analysts are trained well and deduct the correct information which serves the purposes of the problem in the first place. | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/12345678/18679 | |
| dc.language.iso | en | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Loan Repayment Risk | en_US |
| dc.subject | Microfinance | en_US |
| dc.subject | Oromia Credit and Saving Share Company Organization | en_US |
| dc.title | The Use of Data Mining to Predict the Loan Repayment Risk: the Case of Oromia Credit and Saving Share Company | en_US |
| dc.type | Thesis | en_US |