Please use this identifier to cite or link to this item: https://doi.org/10.1371/journal.pone.0268965
Title: Using forensic analytics and machine learning to detect bribe payments in regime-switching environments: Evidence from the India demonetization
Authors: Charoenwong, Ben 
Reddy, Pooja
Keywords: Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
BENFORDS LAW
FRAUD
Issue Date: 1-Jan-2022
Publisher: PUBLIC LIBRARY SCIENCE
Citation: Charoenwong, Ben, Reddy, Pooja (2022-01-01). Using forensic analytics and machine learning to detect bribe payments in regime-switching environments: Evidence from the India demonetization. PLOS ONE 17 (6). ScholarBank@NUS Repository. https://doi.org/10.1371/journal.pone.0268965
Abstract: 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.
Source Title: PLOS ONE
URI: https://scholarbank.nus.edu.sg/handle/10635/231085
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0268965
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