National University of Singapore Business School, Singapore, Singapore.
India Institute of Technology Kharagpur, Kharagpur, India.
PLoS One. 2022 Jun 9;17(6):e0268965. doi: 10.1371/journal.pone.0268965. eCollection 2022.
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.
我们使用来自印度一家大型零售商的丰富交易数据和一份贿赂支付数据集，使用贝叶斯定律（法医分析中常用的工具）指导的经验度量来训练随机森林和 XGBoost 模型。我们围绕 2016 年印度废钞事件进行评估，该事件影响了印度合法货币纸币的分布，发现仅使用 2016 年之前或之后的数据进行训练和测试数据的模型的 F1 分数范围在 90%左右，这表明这些模型和贝叶斯定律标准包含了检测贿赂支付的有意义的信息。然而，在一个制度环境下训练的模型在另一个制度环境下进行测试的性能则急剧下降到低于 10%，这突出了在受到制度转变影响的环境中使用金融数据分析时制度设置的作用。
