Transforming Public Administration: AI-Driven Tools for Summarizing Corruption Cases in Federal Education

The fight against corruption in public institutions, particularly in universities and federal institutes, requires efficient tools for auditing and monitoring administrative processes. This paper presents a tool designed to analyze corruption cases by identifying key elements within these processes, such as classification, damage, and motivation, while observing recurring patterns. The tool leverages Natural Language Processing (NLP) techniques and Large Language Models (LLMs) to automate the extraction and analysis of relevant information. The results demonstrate that the solution can significantly reduce the time required for audits while enhancing accuracy in detecting irregularities.