Abstract
This paper presents a statistics-based method for detecting value-added tax evasion by Kazakhstani legal entities. Starting from features selection we perform an initial exploratory data analysis using Kohonen self-organizing maps; this allows us to make basic assumptions on the nature of tax compliant companies. Then we select a statistical model and propose an algorithm to estimate its parameters in unsupervisedmanner. Statistical approach appears to benefit the task of detecting tax evasion: our model outperforms the scoring model used by the State Revenue Committee of the Republic of Kazakhstan demonstrating significantly closer association between scores and audit results.
Original language | English (US) |
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Title of host publication | Intelligent Decision Technologies 2016 - Proceedings of the 8th KES International Conference on Intelligent Decision Technologies, KES-IDT 2016 |
Editors | Lakhmi C. Jain, Robert J. Howlett, Ireneusz Czarnowski, Alfonso Mateos Caballero, Lakhmi C. Jain, Lakhmi C. Jain |
Publisher | Springer Science and Business Media Deutschland GmbH |
Pages | 37-49 |
Number of pages | 13 |
ISBN (Print) | 9783319396293 |
DOIs | |
State | Published - 2016 |
Externally published | Yes |
Event | 8th KES International Conference on Intelligent Decision Technologies, KES-IDT 2016 - Puerto de la Cruz, Tenerife, Spain Duration: Jun 15 2016 → Jun 17 2016 |
Publication series
Name | Smart Innovation, Systems and Technologies |
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Volume | 56 |
ISSN (Print) | 2190-3018 |
ISSN (Electronic) | 2190-3026 |
Conference
Conference | 8th KES International Conference on Intelligent Decision Technologies, KES-IDT 2016 |
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Country/Territory | Spain |
City | Puerto de la Cruz, Tenerife |
Period | 6/15/16 → 6/17/16 |
Bibliographical note
Publisher Copyright:© Springer International Publishing Switzerland 2016.
Keywords
- Anomaly detection
- Cluster analysis
- Self-organizing maps
- Tax evasion detection