Digital Taxation
| dc.contributor.author | Wanjagi, Jedidah | |
| dc.contributor.author | Ruto, Kipkirui | |
| dc.date.accessioned | 2020-07-27T09:29:37Z | |
| dc.date.accessioned | 2022-06-06T18:55:02Z | |
| dc.date.available | 2020-07-27T09:29:37Z | |
| dc.date.available | 2022-06-06T18:55:02Z | |
| dc.date.issued | 2020 | |
| dc.description | ATCR Journal Article | en_US |
| dc.description.abstract | This paper sought to determine how tax administrators could use data mining and pattern recognition to enhance tax compliance on online business transactions. The specific objectives of the study were to examine technology required in adopting data mining; to determine the tax audit and control required to detect error and fraud in data mining; and to determine the risks involved in data mining and pattern recognition to enhance tax compliance on online business transactions. The latent role and benefits of data mining in tax administrations are elucidated in view of the overall technology, operational framework and organization. The researcher reviewed various articles, research papers and books on various data mining applications. Techniques used for data mining included statistical techniques, decision tree and neuro network technique. Findings indicate that decision tree and neural network technique provided better results than the other techniques. The predictive modeling using the “Delphi” method was discovered as perfect tool that assisted agency to differentiate non-compliance from compliant clients and to focus on audits that would lead to a positive tax adjustment. The KRA may consider the use of this model to predict the risk involved in data mining. This actually assists the tax authorities to make better use of human personnel and therefore minimize the tax burden. The process of data mining helps the tax administrators to refine its traditional audit strategies in order to raise their tax budget | en_US |
| dc.identifier.citation | Wanjagi, J., & Ruto, K. (2020). Digital Taxation. African Tax and Customs Review, 3(1), p30-38. Retrieved from https://atcr.kra.go.ke/ojs/index.php/atcr/article/view/63 | en_US |
| dc.identifier.issn | 2664-9527 | |
| dc.identifier.issn | 2664-9535 | |
| dc.identifier.uri | https://ikesra.kra.go.ke/handle/123456789/807 | |
| dc.identifier.uri | https://atcr.kra.go.ke/index.php/atcr | |
| dc.language.iso | en | en_US |
| dc.publisher | Kenya School of Revenue Administration | en_US |
| dc.subject | Data mining | en_US |
| dc.subject | Pattern recognition | en_US |
| dc.subject | Tax Compliance | en_US |
| dc.title | Digital Taxation | en_US |
| dc.title.alternative | An analytics study of data mining and pattern recognition to enhance tax compliance on online business transaction | en_US |
| dc.type | Article | en_US |