<![CDATA[Tax audit is an effective instrument for preserving tax compliance, and risk-based tax audit selection can optimize it. Risk-based tax audit selection selectively auditing on high financial risk wealthy taxpayers. In contrast, manually selecting amid the plethora of taxpayer data is difficult, prone to human error, costly and time-consuming. Fortunately, using Extensible Business Report Language (XBRL) as a well-known financial statement reporting standard enables automation. This project proposed software named XAFR as a model for extracting, transforming, and loading the latest XBRL Open Information Model (OIM) 1.0 standard US-SEC dataset and provided it as a data source for risk classification using rule-based risk scoring and Machine Learning. Several thorough testing exposed Random Forest classifier as the best model for Machine Learning risk classification with high accuracy, revealing the excellent collaboration of rule-based risk scoring approach with Machine Learning for risk classification and the importance of XBRL as a transparent but robust report standard that tax authorities can utilize. The excellent system integration resulted in the ability to expose wealthy high-risk taxpayers and high-risk industries and predict risk classification based on two-year financial statements. Moreover, this report introduces the critical importance of RCA (Risk, Current Ratio, Assets) analysis and SIC (Standard Industry Classification) utilization to generate risk classification, rank and explanation. This project utilizes financial indicators in the limited year and leaves the semantic analysis for future works because of time and hardware limitations. The possibility of predicting the possible tax debt prediction are promising Machine Learning future developments]]>
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