A STUDY ON DATA MINING FOR TAX COMPLIANCE

ABSTRACT Data mining has emerged as a powerful analytical tool for improving tax compliance and strengthening revenue administration systems. With the rapid growth of digital transactions and large volumes of taxpayer data, traditional auditing methods are often insufficient to detect complex patterns of tax evasion and fraud. Data mining techniques such as classification, clustering, association rule mining, and anomaly detection help tax authorities analyse vast datasets efficiently and identify suspicious behaviours, underreporting of income, and irregular filing patterns. By using predictive models and risk-based assessment systems, tax departments can prioritize high-risk cases, reduce manual workload, and enhance transparency in tax administration. Data mining also supports better policy formulation by identifying trends in taxpayer behaviour and compliance levels across different sectors. However, the effective implementation of data mining requires strong data governance, privacy protection, and technical expertise to ensure ethical and secure use of taxpayer information.