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However, existing models often fall short in accurately assessing and managing these risks. They often rely on historical financial data alone, which fails to account for sudden market fluctuations or unforeseen external events, leading to suboptimal risk assessments. Recognizing the paramount importance of time series analysis in financial risk prediction, we introduce a novel approach to the ABC-Attention-GRU combination model. This innovative model leverages the strengths of Artificial Bee Colony (ABC), the attention mechanism, and Gated Recurrent Unit (GRU) to enhance predictive accuracy and robustness. In our experiments, the ABC-Attention-GRU model consistently outperformed state-of-the-art methods across various financial datasets. 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