Detecting and Analyzing Anomalies in City Expenditure Time Series

This paper presents an enhanced approach for detecting and analyzing anomalies in public expenditure time series. The method combines statistical techniques and machine learning models to identify unusual spending behaviors and to rank expenditure items according to both the frequency of anomalies and the monetary impact involved. The statistically generated anomalies produce suspicion alerts of potential fraud, with the objective of prioritizing cases and directing human audit efforts more effectively. In this extended version, we introduce a finer-grained analysis that examines spending patterns at a more detailed level within the governmental budget structure, allowing the detection of irregularities that may not be visible under more aggregated views. The approach is validated on a real-world dataset comprising more than one million city expenditure records from the state of Minas Gerais, and the results demonstrate its ability to reveal irregularities that may remain hidden under higher levels of aggregation.