Deep Learning for Corporate Governance: Predicting Financial Distress and Fraud Using Transformer-Based Models on Board Networks

Abstract

The crossroads of deep learning and corporate governance is a paradigm shift in financial risk analysis, which goes beyond the conventional ratio models and modifies the multifaceted relationship relationships of board organizations and corporate networks. In this article, the authors provide a robust set of predictors of financial distress and fraud based on transformer-based designs of the board network data. We introduce a new methodological framework that combines graph neural networks with attention mechanisms to model director interlocks, committee structures, and measures of governance quality as high-dimensional relational features. The framework employs advanced econometric methods such as difference-in-differences with continuous treatment, propensity score weighting with neural network propensity estimation, and panel VAR with impulse response functions to create a causal identification. Empirical evidence on a decade of board-level data shows that transformer models have better predictive accuracy than conventional methods and that area under the curve (AUC) gains are 12-18 points in predicting financial distress and 22-28 points in predicting fraud. The cognitive interpretability module establishes the board independence, audit committee expertise and the network centrality of directors as the most important determinants of firm resilience. These results indicate that the application of algorithmic governance based on the use of deep learning can improve transparency, reduce agency risks, and give regulators decision-support systems to conduct active risk monitoring.