Optimized Machine Learning Models for Detecting Income Tax Fraud

Abstract --- Traditional tax default detection methods struggle to identify subtle patterns and inconsistencies in large dataset. These older techniques lack the ability to uncover complex relationships between variables. In contrast, machine learning a more effective solution by automatically analyzing tax return to spot irregularity, such as miss matches in income reporting, unusual expensive-to-income ratios, or suspicious detection. These models improve over time by learning for new data and adapting to evolving fraud taxes, making them for superior to traditional systems. To achieve the best results, it is cruel to use high-quality and labeled data and perform essential pre-processing steps, such as handling missing values standardizing features and encoding character variables. Machine learning methods, such as decision tree random forest, gradient boosting, can enhance moral performance. Additionally, fine-tuning hyper parameters and applying cross validation ensure the model performance well on unseen data, avoiding over fitting.This approach helps tax authorities efficiently detect fraud by automatically flagging suspicious returns further reviews, improving audit speed and overall accuracy. Keywords: Tax fraud detection, machine learning, data pres-processing, anomaly detection, decision tree, random forest, model performance, improving accuracy, gradient-boosting.