Detecting Tax Fraud

The process of reviewing tax returns is labor-intensive. Tax agents need to perform these reviews with efficiency and efficacy to maximize tax revenue and enforce tax law compliance. Thus, the accurate identification of fraudulent tax returns is critically important.

The digitization of many government services has resulted in an abundance of information. By training a Machine Learning (ML) model using historical tax data, tax returns can be classified as either fraudulent or compliant with the law. These models can serve as a decision support system, aiding tax agents in classifying tax returns.

The implementation of responsible AI ensures ethical and fair practices when dealing with sensitive financial data. It promotes transparency, reduces potential bias in predictions, and ensures interpretability of the ML models, thereby fostering fair and lawful tax practices.

Significantly, AI use in such high-risk sectors is regulated under the EU AI Act. The Act emphasizes lawful and transparent data processing, the protection of individuals’ rights, and the need for robust risk management systems.

Modulos offers a Responsible AI platform, facilitating the creation of effective, ethical, and compliant ML models. It ensures that the predictive tools used in detecting tax fraud are not only data-driven but also ethically sound and compliant with AI regulations.

The platform can also be deployed on-premise, making it highly suitable for the analysis of potentially sensitive data. With Modulos, tax agencies can confidently detect fraudulent tax returns, knowing their strategies are not only effective but also ethically responsible and compliant with necessary regulatory frameworks.