PROACTIVE APPROACH IN TAX RISK MANAGEMENT: DATA ANALYSIS TECHNIQUES TO IDENTIFY HIGH-RISK TAXPAYERS - Funda KARAKOYUN (Prizren Social Science Journal, 2024)
There is a transition from labour-intensive traditional methods to the use of technology-intensive systems to identify risky taxpayers in tax audits. With this process, which has been accelerating for decades, statistical models that will reduce tax loss and evasion of states and increase efficiency in collection have been diversified in the field of machine learning. While innovations in software programs with digitalisation transformation reduce the manual workload of the tax administration, machine learning algorithms are used with experts employed in the field in continuously developing risk analysis studies. For tax administrations that fall behind these developments, the identification of high-risk taxpayers, the effectiveness of collection, and tax compliance are becoming more difficult to audit in the context of rapidly increasing global data.