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Fraud detection is necessary for any financial system. However, the way of committing frauds and also for detecting them have evolved considerably in the lasts years, mainly due the development of new technologies. Therefore, fraud detection via statistical schemes has become an important tool to reduce the chances of frauds. In this paper, we present a study case applied to the tax collection per month of the Federal Patrimony Department (SPU). In this study case, we analyze some of the current methods for fraud detection, as Rule-Based Systems and Neural Networks classifiers, and propose the use of Neural Networks predictors for detecting fraud in time series data of the SPU.

Business Intelligence (BI) systems are crucial for assisting the decision making processes of private and governmental institutions. The Human Resources Auditing Department (CGAUD) of the Brazilian Ministry of Planning, Budget and Management (MP) has been developing its own BI for auditing the payroll of all federal employees since 2010. Given that the monthly payroll is approximately 12.5 billion reais, the initial version of the proposed BI system in 2012 was able to audit approximately 1.5 billion reais. In this paper, we propose an improved BI system, which can deal with an increased volume of data, a greater amount of monitoring trails and a higher granularity of the final reports. As consequence, the total audit value has increased to approximately 5 billion reais. In addition, our new BI system has incorporated a Reimbursement Tracking System for monitoring the payroll of federal employees who have to reimburse the Brazilian government. Around 4.5 million reais are automatically monthly tracked by our new BI system. Our proposed BI system has been validated using the real environment of the MP and the results are compared to the previous BI system.