Use of data analysis techniques in the process of identifying tax evasion

Abstract

Macroeconomically speaking, the power to analyze digital data and digital information of a fiscal nature, plays a key role in implementing economic strategies. The large volume of daily data resulting in real time, at a national level by sending tax receipts from economic operators to the National Agency for Fiscal Administration (ANAF) using electronic fiscal cash registers (AMEF), facilitates the possibility of a predictive analysis that allows pattern recognition and signaling atypical ones in order to identify tax evasion. Identifying the relevant parameters in tax receipts, collecting data and integrating it into the platform, processing tax data using specific Big Data tools and techniques, analyzing information using a data extraction model, organizing and structuring this data through various aggregation techniques and improved data will lead to the validation of results based on clusters and their interpretation. The advantage of data analysis is that it frees up more time and energy for other tasks, such as creative and meaningful ones, that use the interpretation of data models in the process of strategic decision making. Another relevant aspect is the aspect of implementing the theoretical principles in the business area and how the cash registers operating policies are used to fight crime and tax fraud.

Keywords

• big data technics
• analytics
• machine learning
• data mining
• cloud computing
• data aggregation
• data enrichment
• fiscal digital data
• electronic fiscal devices