PREDICTIVE ANALYTICS FOR TAX EVASION DETECTION IN INDIA

ABSTRACT This research explores the application of predictive analytics to enhance tax evasion detection in India, addressing a critical challenge in modern tax administration. The study develops a robust machine learning framework that integrates both supervised classification and unsupervised anomaly detection methods to analyse diverse taxpayer data—including historical tax returns, audit outcomes, and third-party financial records. Traditional methods, predominantly manual and rule-based, have proven insufficient in capturing the complexity of evolving evasion strategies. By leveraging advanced algorithms such as logistic regression, decision trees, random forests, and neural networks, alongside anomaly detection techniques, the proposed model is designed to identify subtle discrepancies in taxpayer behaviour that may indicate fraudulent activity.