Machine Learning in Tax Audits

Machine Learning in Tax Audits refers to the application of artificial intelligence techniques that enable systems to learn from data and improve their performance in analyzing tax-related information without being explicitly programmed for each task.

In the context of tax audits, machine learning algorithms analyze vast amounts of financial data to identify patterns, anomalies, or potential areas of non-compliance. For example, these systems can evaluate historical audit data to predict which tax returns may be more likely to be flagged for audit based on specific risk factors. By automating data analysis, machine learning enhances the efficiency and accuracy of audits, allowing tax professionals to focus on higher-risk areas and improve overall compliance.