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<p>To address the challenges of multi-source heterogeneous data fusion and dynamic association modeling in regional financial risk monitoring, this study proposes a unified framework integrating spatiotemporal graph neural networks with multi-task learning. Using monthly state-level panel data covering all 50 U.S. states from 2012 to 2024 and a rolling split of 2012–2021 for training, 2022 for validation, and 2023–2024 for testing, the model achieves a one-step-ahead MAE of 0.118 and an AUC of 0.836, outperforming the compared statistical, tree-based, recurrent, and graph-based baselines within the same protocol. Ablation studies indicate that augmenting geographic adjacency with economically derived relation channels improves the median F1 score from 0.65 to 0.92, while multi-task learning yields an additional 13-percentage-point F1 gain in this experimental setting. Runtime experiments conducted in a unified implementation environment show 14.2 h of training time, approximately 35 ms inference latency, and stable performance up to a 30% simulated missing-data rate. Feature-attribution analysis is used here as an interpretive description of model behavior rather than causal proof, supporting the model's potential utility for regional financial monitoring and decision support.</p>

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Regional financial model construction based on artificial intelligence and machine learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月31日

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  • Xuan Lan

摘要

To address the challenges of multi-source heterogeneous data fusion and dynamic association modeling in regional financial risk monitoring, this study proposes a unified framework integrating spatiotemporal graph neural networks with multi-task learning. Using monthly state-level panel data covering all 50 U.S. states from 2012 to 2024 and a rolling split of 2012–2021 for training, 2022 for validation, and 2023–2024 for testing, the model achieves a one-step-ahead MAE of 0.118 and an AUC of 0.836, outperforming the compared statistical, tree-based, recurrent, and graph-based baselines within the same protocol. Ablation studies indicate that augmenting geographic adjacency with economically derived relation channels improves the median F1 score from 0.65 to 0.92, while multi-task learning yields an additional 13-percentage-point F1 gain in this experimental setting. Runtime experiments conducted in a unified implementation environment show 14.2 h of training time, approximately 35 ms inference latency, and stable performance up to a 30% simulated missing-data rate. Feature-attribution analysis is used here as an interpretive description of model behavior rather than causal proof, supporting the model's potential utility for regional financial monitoring and decision support.