Tracking disclosure change trajectories for financial fraud detection

The global economic disruption brought by COVID-19 crisis can set a stage for the prevalence of financial statement frauds, which jeopardize the efficient functioning of capital markets. In this paper, we propose a nuanced method to detect frauds by tracking granular changes in disclosures over time. Specifically, we first align paragraphs between consecutive disclosures by their similarities. This alignment can be solved as an optimization-based matching problem. Then we identify three types of changed contents: recurrent, newly added, and deleted contents. For each type, we measure the changes in terms of fraud-relevant linguistics features, such as sentiment and uncertainties. Further, we formulate a firm's Management Discussion and Analysis change trajectory over years as a multivariate time series composed of these granular metrics. We implement a deep learning model to predict frauds using the change trajectory as an input. Extensive experiments demonstrate that our model significantly outperforms benchmark models, and its performance increases with the length of the change trajectory. Moreover, we found specific types of changes are strongly associated with frauds, including weak modal or reward words in newly added or deleted contents. Our work provides an optimization-based method to define change trajectories and trace information mutation in narratives. Finally, our study contributes to the fraud detection literature with a new predictive signal-disclosure change trajectories with an effective deep learning architecture.