Using forensic analytics and machine learning to detect bribe payments in regime-switching environments: Evidence from the India demonetization

• Published in: PloS one Vol. 17; no. 6; p. e0268965
• Main Authors: Charoenwong, Ben, Reddy, Pooja
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 09.06.2022; Public Library of Science (PLoS)
• Subjects: Analysis; Bribery; Computer and Information Sciences; Datasets; Empirical analysis; Financial statements; Forensic science; Fraud prevention; Information management; Investigations; Legislation; Machine learning; Mathematical analysis; Medicine and Health Sciences; Modelling; Payments; People and Places; Performance evaluation; Physical Sciences; Research and Analysis Methods; Rupees; Social Sciences; India; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0268965

We use a rich set of transaction data from a large retailer in India and a dataset on bribe payments to train random forest and XGBoost models using empirical measures guided by Benford’s Law, a commonly used tool in forensic analytics. We evaluate the performance around the 2016 Indian Demonetization, which affects the distribution of legal tender notes in India, and find that models using only pre-2016 data or post-2016 data for both training and testing data had F1 score ranges around 90%, suggesting that these models and Benford’s law criteria contain meaningful information for detecting bribe payments. However, the performance for models trained in one regime and tested in another falls dramatically to less than 10%, highlighting the role of the institutional setting when using financial data analytics in an environment subject to regime shifts.