Using Machine Learning for Anomaly Detection in German Public Budgeting Data

Abstract

Outlier detection can assist individuals involved in the public budgeting process by enabling them to focus on specific values within the budget plan, which is particularly valuable considering the extensive nature of these plans (the 2023 German federal budget comprised 3,214 pages). Through a human-centered development approach, this study evaluates the feasibility of implementing algorithms for outlier detection in the context of public budgeting in Germany. In addition, results of the three algorithms Isolation Forest, One-Class Support Vector Machine, and Local Outlier Factor are compared. Our results reveal two insights: 1. The quality and availability of data pose fundamental challenges for outlier detection using machine learning in Germany; 2. The tested algorithms are indeed proficient in detecting certain values within the budget plan as anomalous and they exhibit a certain level of consistency. Nevertheless, computing the measure of accuracy presents difficulties due to the complexity of discerning when a value is in accordance with political intent or constitutes an error. The study overall highlights the potential of outlier detection in public budgeting while emphasizing the requirement for appropriate datasets and ongoing evaluation by the target audience.

Keywords: Outlier Detection, Public Budgeting Data, Feasibility Analysis, Human-Centered Design, Digitization of the Public Sector

DOI: 10.54941/ahfe1004520