Mapping Machine Learning Research Networks in Financial Risk Prediction

Financial risk forecasting is a more and more relevant topic in contemporary financial systems, since the correct anticipation of credit defaults, bankruptcy, and systemic vulnerabilities is essential for sound decision-making. Classical statistical models have been the cornerstone of risk prediction for decades; however, they are likely to fail in capturing the complex and nonlinear relationships of financial data. The latest developments in machine learning brought forth new methods with increased predictability and response to massive, disparate data. The existing science literature best illustrates a shift away from risk models towards evidence-based approaches involving artificial intelligence, but the subtle understanding of the intellectual process engaged is still poorly investigated. This work fills this void through the use of a network-oriented bibliometric methodology integrating co-citation analysis, bibliographic coupling, and author keyword co-occurrence analysis. The research explores root works, reveals upcoming thematic clusters, and analyzes shifting paradigms affecting financial risk prediction methods. Major findings indicate a systematic intellectual environment where classical models remain the gold standard, and ensemble learning and deep learning techniques are being applied increasingly to enhance risk estimation. Regulatory requirements as well as model transparency also gain increasing attention. These results hold deep implications for both theoretical and practical applications because they provide fresh insights into the dynamic between old and new methods and thus open doors to scalable and interpretable models of risk evaluation.