An attributed network features learning method for over-indebtedness prediction

Abstract

Over-indebtedness represents a financial anomaly and is widely regarded as an early indicator of financial distress. The recent advancements in machine learning techniques have enabled more accurate prediction of over-indebtedness. While existing forecasting models have contributed to mitigating the negative impacts of over-indebtedness, they typically fail to account for the influence of external factors on corporate debt decisions, which consequently limits their predictive accuracy. In response, this paper introduces a novel prediction model for over-indebtedness based on an attributed network feature learning approach for early warning. Building on previous research, the proposed model incorporates external information, such as interlocking directorate networks and product competition networks, as additional data sources for feature construction. By leveraging descriptive analytics and deep attributed network embedding methods, the model captures both individual and external features from social network data. To optimize the model’s performance, a generative classifier—specifically, the locally-weighted Expectation Maximization method for Naïve Bayes learning—is employed to handle the network-based features. The experimental results demonstrate that the proposed model performs effectively and offers valuable insights for integrating external information into financial prediction models.