This research addresses challenges in taxpayer risk assessment faced by financial departments worldwide. Outdated algorithms and a lack of standardized metrics hinder accurate identification of risky taxpayers. To combat this, the study introduces an innovative approach utilizing Large Language Models (LLMs). It involves integrating taxpayer data into templates to create comprehensive natural-language profiles, which are then used to fine-tune LLMs for precise risk predictions. Comparative evaluations show superior performance over traditional methods, revealing nuanced insights often missed by older algorithms. This approach not only enhances accuracy but also deepens understanding of taxpayer behavior, aiding informed decision-making. Embracing LLMs promises improved fiscal governance amid evolving financial landscapes, highlighting the necessity of modernizing taxpayer risk assessment methods in governmental financial departments.
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