A Decision Tree and Naive Bayes algorithm for income tax prediction

One of the concerns regarding the tax collection system is incorrect case selection. Manual selection of audit cases by auditors (whose role is to detect individual cases of tax non-compliance) based on their expert knowledge of the taxpayers' behaviour, cannot uncover all patterns of non-compliant behaviour hidden in historical data. In addition, random selection of audit cases is not focused on the highest risks. In other words, manual selection has a high opportunity cost if it is used as the sole selection method. Computational intelligence provides methods, techniques and tools, which have been taught to automatically make accurate income tax predictions based on past observations. The data were retrieved from the real time environmental situation. Application of computational intelligence methods proved to be efficient in learning a classification algorithm to classify compliant and non-compliant taxpayers. The new algorithm was evaluated and validated in empirical tests on the same dataset. Although this algorithm had the same performance measurement as Bagging, it outperformed the other existing multiple classifiers in terms of performance. This illustrates an automated system that replicates the investigative operation of human tax risk auditors.