Bank credit ratings, assigned by agencies like Standard & Poor's, Moody's, and Fitch, evaluate a bank's financial health based on factors such as asset quality, profitability, and market position (White 2010). These ratings are critical indicators of a bank's ability to repay debt and significantly influence economic players: for businesses, they affect borrowing costs and market trust; for economies, they impact financial system stability. Sudden rating changes can trigger volatile capital flows and market fluctuations, influencing economic growth and financial stability. During financial market instability, predicting bank credit ratings, especially for the upcoming quarter, becomes crucial. These predictions provide the data needed for informed decision-making, prompt regulatory adjustments, and the protection of investors and the public. The 2023 bankruptcy of Silicon V alley Bank (SVB), which triggered collapses like those of Signature Bank and First Republic Bank, underscores the resulting financial turmoil (Aharon et al. 2023). Graph neural networks (GNNs) have become a pivotal technology in financial risk prediction, particularly excelling in node classification and link prediction tasks (Wu et al. 2022). These models effectively leverage edge information to represent the propagation of financial risk within networks.