The Role of Data Mining in the Risk Assessment of Customs: (With special reference to The Ethiopian Customs Authority)
| dc.contributor.advisor | Desai, B.L. (PhD) | |
| dc.contributor.advisor | Getachew, Mesfin (PhD) | |
| dc.contributor.author | Belew, Girma | |
| dc.date.accessioned | 2020-06-05T07:55:09Z | |
| dc.date.accessioned | 2023-11-18T12:45:12Z | |
| dc.date.available | 2020-06-05T07:55:09Z | |
| dc.date.available | 2023-11-18T12:45:12Z | |
| dc.date.issued | 2004-07 | |
| dc.description.abstract | Customs Organizations are responsible for two Opposing In g yet equally important responsibilities . These are t e provision of efficient services to traders for the smooth flow of shipments and the protection of the country from any kind of risk threats that is associated with interactional trade. Reform and monetization of custom services through automation and setting transparent working procedures has resulted in the provision of efficient services . However addressing the issue of implementing a proper risk management strategy remain s a challenge The Ethiopian Customs Authority at present is handling the control of customs risks using subjective methods. The subjective method of handling risks solely depends on experts' judgment of selecting shipments for physical examination. The Subjective method is essential since the knowledge and experience of customs experts and their observation of the behaviors of intervening agents like traders and clearing agents is very important. However depending only on subjective analysis for strategic risk management ha s its shortcomings. This study was aimed at supporting the current selective physical examination system o f incoming shipments in the Ethiopian Customs Authority with objective methods using data mining. The study was conducted through the annals is of customs fraud cases seized in the past. For this stud y one of the data mining techniques known as decision tree was employed. The dataset used in the stud y consisted of 10 364 record s out of which 17 0 cases were fraud cases. The distribution of the two classes was highly imbalanced. To deal with the class imbalance pro blend the over- sampling approach was use d. Five experiments we re conducted by varying the rate of over-sampling. After over- sampling the five datasets had a ratio of 90: 10, 80:20, 70:30, 60:40 and 50 :50 all non- fraud to fraud. Using an independent dataset that also contain 2616 non-fraud and 39 fraud cases all the models generated by the five data sets were validated. The dataset with a proportion of 70:30 has shown the best result in terms of correctly classifying the fraud cases. The model correctly classified 22 fraud cases out of the 39 cases. Combining the subjective method with the subjective methods can improve the efficiency of risk assessment and selective physical examination of shipments in the Ethiopian customs authority. Models developed by automatic analysis can also be used across all the different customs offices in the country consistently. | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/12345678/21439 | |
| dc.language.iso | en | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Information Science | en_US |
| dc.title | The Role of Data Mining in the Risk Assessment of Customs: (With special reference to The Ethiopian Customs Authority) | en_US |
| dc.type | Thesis | en_US |