Audit lead selection and yield prediction from historical tax data using artificial neural networks

• Published in: PloS one Vol. 17; no. 11; p. e0278121
• Main Authors: Chan, Trevor, Tan, Cheng-En, Tagkopoulos, Ilias
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 30.11.2022; Public Library of Science (PLoS)
• Subjects: Accuracy; Algorithms; Artificial neural networks; Biology and Life Sciences; Classification; Commerce; Computer and Information Sciences; Data mining; Datasets; Decision trees; Evaluation; Feature selection; Fraud prevention; Hypotheses; Information returns; Machine learning; Mental Recall; Methods; Neural networks; Neural Networks, Computer; Physical Sciences; Research and Analysis Methods; Restaurants; Social Sciences; Statistical significance; Support vector machines; Tax assessment; Tax auditing; Tax audits; Tax revenue estimating; Technology application; United States; United States > US; Minnesota; California
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0278121

Tax audits are a crucial process adopted in all tax departments to ensure tax compliance and fairness. Traditionally, tax audit leads have been selected based on empirical rules and randomization methods, which are not adaptive, may miss major cases and can introduce bias. Here, we present an audit lead tool based on artificial neural networks that have been trained and evaluated on an integrated dataset of 93,413 unique tax records from 8,647 restaurant businesses over 10 years in the Northern California, provided by the California Department of Tax and Fee Administration (CDTFA). The tool achieved a 40.1% precision and 58.7% recall (F1-score of 0.42) on classifying positive audit leads, and the corresponding regressor provided estimated audit gains (MAE of $155,490). Finally, we evaluated the statistical significance of various empirical rules for use in lead selection, with two out of five being supported by the data. This work demonstrates how data can be leveraged for creating evidence-based models of audit selection and validating empirical hypotheses, resulting in higher audit yields and more fair audit selection processes.