Browsing by Author "Yilma, Yishak"

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    Application of Data Mining Techniques to Support Importers and Exporters Delinquency Prediction: The Case of National Bank of Ethiopia
    (Addis Ababa University, 2009-01) Yilma, Yishak; V.N.V, Manoj (PhD)
    The application of Data mining is becoming increasingly common in both the private and public sectors. Industries such as banking, insurance, medicine, and retailing commonly use data mining to reduce costs, risks, and increase sales. Foreign currency is a scarce financial resource in Ethiopia. This scarcity calls for the consolidation and fostering of existing financial management systems to ensure optimum utilization of the available resource. The National Bank of Ethiopia (NBE) has the task of monitoring the settlement of importers and exporters foreign exchange commitments as per the existing directives. Currently in the NBE there are many importers and exporters who are delinquent and expected to settle their commitment. Thus, it is the aim of this study to examine the potential applicability of data mining technology in building a predictive data mining model that helps to predict potentially delinquent or non delinquent importers and exporters in relation to their utilization of the foreign currency. To conduct the study, the researcher adopted the Cross-Industry Standard Process for Data Mining (CRISP–DM) process model. Several predictive classification models were built both in decision tree and neural network techniques using WEKA software. The best performing model was chosen by comparing the models using standard evaluation criteria such as accuracy, precision, recall and interpretability. According to the evaluation results, both techniques have shown a promising performance. However, the best models for both export and import transactions were obtained using decision tree techniques. The decision tree approach brings about 94.02% accuracy in the case of predicting export transactions and 98.03% for import transactions. Moreover, the models built bythe decision tree show better results in terms of precision, recall and interpretability for both transactions. Thus, compared to neural network, the decision tree approaches are more applicable in addressing the research problem. Accordingly, some important rules are derived using the selected attributes such as, MethodofPayment, BaseOfShipment, Country_Region, AmtOfBirrIn_Range, Currency, Validity_period and EconomicSector that are relevant in business decision making. In general, the results obtained from the study proved the potential applicability of data mining technology to predict importers and exporters into predefined classes (delinquent and nondelinquent) based on their transaction characteristics.
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    Application of Data Mining Techniques to Support Importers and Exporters Delinquency Prediction: The Case of National Bank of Ethiopia
    (Addis Ababa University, 2009-01) Yilma, Yishak; V.N.V, Manoj (Prof.)
    The application of Data mining is becoming increasingly common in both the private and public sectors. Industries such as banking, insurance, medicine, and retailing commonly use data mining to reduce costs, risks, and increase sales. Foreign cogency is a scarce financial resource 111 Ethiopia. This scarcity calls for the consolidation and fostering of existing financial management systems to ensure optimum utilization of the available resource. The National Bank of Ethiopia (NBE) has the task of monitoring the settlement of importers and exporters foreign exchange commitments as per the existing directives. Contently in the NBE there are many importers and exporters who are delinquent and expected to settle their commitment. Thus, it is the aim of this study to examine the potential applicability of data mining technology in building a predictive data mining model that helps to predict potentially delinquent or non delinquent importers and exporters in relation to their utilization of the foreign currency. To conduct the study, the researcher adopted the Cross-Industry Standard Process for Data Mining (CRISP-OM) process model. Several predictive classification models were built both in decision tree and neural network techniques using WEKA software. The best performing model was chosen by comparing the models using standard evaluation criteria such as accuracy. precision, recall and interpretability. According to the evaluation results, both techniques have shown a promising performance. However, the best models for both export and import transactions were obtained using decision tree techniques. The decision tree approach brings about 94.02% accuracy in the case of predicting export transactions and 98.03% for import transactions. Moreover, the models built by the decision tree show better results in terms of precision, recall and interpretably for both transactions. Thus, compared to neural network, the decision tree approaches are more applicable in addressing the research problem. Accordingly, some important rules are derived using the selected attributes such as . Method of Payment, Base Of Shipment, Country-Region , Amt Of Birrln_-Range. Currency. Validity-period and Economic Sector that are relevant in business decision making. In general, the results obtained from the stud y proved the potential applicability of data mining technology to predict importers and exporters into predefined classes (delinquent and no delinquent) based on their transaction characteristics.