We study environmental and social (E&S) disclosures in annual reports. Using the word embedding model to examine over 210,000 annual reports from 24,271 public firms in 30 international countries/regions between 2001 and 2020, we create an E&S dictionary that allows us to document trends in annual report E&S disclosure. Specifically, we find: (1) increases in length and boilerplate language and (2) decreases in specificity. Our results also suggest that E&S disclosure quality improves after the adoption of voluntary ESG reporting frameworks but deteriorates after disclosure mandates. Our findings provide systematic evidence on the evolution of E&S disclosure in annual reports over the past two decades and shed light on how voluntary and mandatory standards have shaped global E&S reporting practices.

We develop a state‐of‐the‐art fraud prediction model using a machine learning approach. We demonstrate the value of combining domain knowledge and machine learning methods in model building. We select our model input based on existing accounting theories, but we differ from prior accounting research by using raw accounting numbers rather than financial ratios. We employ one of the most powerful machine learning methods, ensemble learning, rather than the commonly used method of logistic regression. To assess the performance of fraud prediction models, we introduce a new performance evaluation metric commonly used in ranking problems that is more appropriate for the fraud prediction task. Starting with an identical set of theory‐motivated raw accounting numbers, we show that our new fraud prediction model outperforms two benchmark models by a large margin: the Dechow et al. logistic regression model based on financial ratios, and the Cecchini et al. support‐vector‐machine model with a financial kernel that maps raw accounting numbers into a broader set of ratios.