Knowledge and Understanding
By the end of the course, students will have acquired a solid understanding of the main statistical techniques used both nationally and internationally to estimate tax risk and control tax evasion. The course will cover classification and regression models, group analysis segmentation models, and will also reference modern machine learning models, discussing their pros and cons. Additionally, the course will explore AI tools used by tax authorities at various stages of the tax procedure, emphasizing the need to balance the general interest of raising revenue with the protection of taxpayers' rights.

Applying Knowledge and Understanding
Students will be able to apply the statistical models studied to analyze tax risk and detect potential cases of tax evasion. They will also learn how to interpret statistical outputs resulting from the application of complex models, converting them into actionable insights for tax assessments. A thorough understanding of the legal requirements for tax assessment methods and tax compliance procedures will enable students to use these analytical tools effectively.

Making Judgements
Students will develop the ability to critically evaluate the reliability and appropriateness of various statistical models and algorithms used in automated tax decision-making. They will be able to identify risks related to potential biases or misuse of AI technologies, proposing improvements that consider the balance between public interest in revenue collection and the protection of taxpayers' rights.

Communication Skills
Students will be able to clearly and effectively communicate the results of statistical analyses and critical evaluations, both to technical and non-technical audiences. They will be capable of presenting the legal and operational implications of using automated tools in the tax context, with particular focus on transparency and accountability in decision-making.

Learning Skills
The course will foster an analytical and interdisciplinary mindset, encouraging students to independently study new statistical techniques and keep up-to-date with the evolving legal and technological landscape in tax matters. Students will be encouraged to continue their learning in a critical and reflective manner, preparing them for professional contexts or research in the field.