An Integrated Multimodal Attention-Based Approach for Bank Stress Test Prediction

Since the financial crisis in late 2008-2009, several global regulatory authorities have mandated stress-testing exercises to evaluate the potential capital shortfalls & systemic impacts that large banks may face during adverse economic conditions. Thus, having the ability to analyze economic conditions & banking performance profiles together to determine relationships among their respective features may provide insights for stresstesting tasks. In this paper, we propose an Integrated Multimodal Bank Stress Test Prediction (IMBSTP) model framework consisting of a two-stages; (1) economic conditions estimator to approximate joint representation among the exogenous factors using generative models, (2) bank capital & loss forecaster to project stress-test measures based on dimensional & temporal features selected from the exogenous economic conditions & banking performance profiles using a dual-attention recurrent neural network. Extensive experimentation is performed on historical economic conditions & consolidated financial statements of U.S. bank holdings companies to show the effectiveness of our approach when compared to state-of-the-art baseline methods.