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| dc.contributor.author | Yekoye, Tarekegn | |
| dc.date.accessioned | 2022-11-21T07:31:27Z | |
| dc.date.available | 2022-11-21T07:31:27Z | |
| dc.date.issued | 2022-07 | |
| dc.identifier.uri | http://ir.bdu.edu.et/handle/123456789/14497 | |
| dc.description.abstract | Trade misinvoicing has become a great problem for all economies especially for the developing countries like Ethiopia. The importers and exporters are involved in the misappropriation in invoice settings and prepares trade bill more or less than the original price of trade invoices. Misappropriation in invoice settings is mainly a problem for all the countries that are in the process of development. And it is difficult to ascertain true picture of economy in these circumstances of fake trade records. This study was conducted to construct model detecting trade misinvoicing at Ethiopian Customs Commission using machine learning algorithm. Thus, the study has developed model that detect companies that misinvoicing trade for various reasons. As a result, the study has used annual report of the organization for two years (2019-2020) for the data set of 31,142 for the transaction made at three routes; Addis Ababa Bole Airport, Kality and Modjo customs stations. The study data was collected from ASYCUDA database. The data was used for training and experiment and analyzed by using machine learning algorithms such as random forest, decision tree, MLP, KNN, and SVC. The predicting accuracy is highest for random forest classifier from ensemble learner and followed by decision tree classifier algorithm from tree learner with values of 0.9344 and 0.9094 respectively. On the other hand, lowest accuracy was computed for MLP classifier algorithm from neural network learner; GaussianNB algorithm from Naive Bayes learner and SVC algorithm from SVM learner with value of 0.4622, 0.5404 and 0.5502 respectively. The accuracy score of KNeighbors classifier algorithm from neighbor‟s learner is 0.7464 but this accuracy level is not enough to develop model that significant number of instances are incorrectly predicted. Based on its performance, the trade misinvoicing detecting model was developed by using random forest classifier. Key Words: Under-invoicing, Ethiopia Customs Commission, ASYCUDA, Machine Learning | en_US |
| dc.language.iso | en_US | en_US |
| dc.subject | Faculty of Electrical and Computer Engineering | en_US |
| dc.title | Detecting Trade Misinvoicing at Ethiopian Customs Commission Using Machine Learning Techniques | en_US |
| dc.type | Thesis | en_US |
