Mining E-Filing Data For Predicting Fraud: The Case Of Ethiopian Revenue And Custom Authority
| dc.contributor.advisor | Mulugeta Dr., Wondwossen | |
| dc.contributor.author | Geremew, Beris | |
| dc.date.accessioned | 2018-11-08T13:21:10Z | |
| dc.date.accessioned | 2023-11-18T12:49:02Z | |
| dc.date.available | 2018-11-08T13:21:10Z | |
| dc.date.available | 2023-11-18T12:49:02Z | |
| dc.date.issued | 2017-06-02 | |
| dc.description.abstract | Nowadays the technological advancement is at improving stage. These technological advancements have their own side effect (loop hole) on the growing economy and the taxation system of a nation. Fraud is one of the risks in this digital environment of tax. Beside the technological advancement, the controlling and monitoring environment is necessary. In this study, experiments were conducted by strictly following the six step Cios et al. (2000) process model. It start from business understanding in ERCA taxation system and fraud, specifically on E-filed data set. By taking the data from database of ERCA and understanding of the data with the help of domain expert and literature. In data preprocessing; inconsistencies, missing value, outliers and related issue handled properly. After that, construction of models and analysis of the result done to facilitate decision making in the business risk analysis. For this study, used a total of 2954 records to training the classifier model. Experiment on deferent classification algorithms including J48, random forest and multilayered perception algorithms were done. We have compared the result of the various models to find the best model using 10-fold cross validation and percentage split (66/34%) evaluation methods. The study, finds that J48 classification algorithm performs with best accuracy when cross checked with deferent testing mechanisms. J48 recorded an accuracy of 94.72% where 2798 instances are correctly classified out of 2954 test cases. Future research directions are also forwarded to come up with an applicable system in the area of the study. | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/12345678/13998 | |
| dc.language.iso | en | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Data Mining, e-filing, classification, j48, fraud. | en_US |
| dc.title | Mining E-Filing Data For Predicting Fraud: The Case Of Ethiopian Revenue And Custom Authority | en_US |
| dc.type | Thesis | en_US |