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<p>Accounting information processing automation improves efficiency and reduces errors as transaction volumes and complexity increase. Traditional accounting systems’ rule-based algorithms lack financial intelligence. Deep learning system NeuroLedger-Net automates accounting using neural networks. The goal is to construct a self-learning anomaly detection and risk classification system with little operator participation. The proposed solution employs LSTM networks for sequential transaction behaviors, Autoencoders for unsupervised anomaly detection, and attention-enhanced MLPs for transaction categorization and risk severity prediction. Use Kaggle’s public financial dataset to train and test the model’s transactional, behavioral, and system-level properties. The NeuroLedger-Net predicts risk occurrences with 96.3% accuracy and a &lt; 3% false-positive rate for anomaly detection. The model prioritizes payment, mistake, and login better with attention. The recommended method improves real-time accounting accuracy and recall by over 12% in F1-score compared to existing methods. Finally, NeuroLedger-Net automates complex accounting information procedures consistently and adaptably using scalable and intelligent technology.</p>

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Research on the automation of intelligent accounting information processing process driven by neural networks

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月4日

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  • Muqiao Cai

摘要

Accounting information processing automation improves efficiency and reduces errors as transaction volumes and complexity increase. Traditional accounting systems’ rule-based algorithms lack financial intelligence. Deep learning system NeuroLedger-Net automates accounting using neural networks. The goal is to construct a self-learning anomaly detection and risk classification system with little operator participation. The proposed solution employs LSTM networks for sequential transaction behaviors, Autoencoders for unsupervised anomaly detection, and attention-enhanced MLPs for transaction categorization and risk severity prediction. Use Kaggle’s public financial dataset to train and test the model’s transactional, behavioral, and system-level properties. The NeuroLedger-Net predicts risk occurrences with 96.3% accuracy and a < 3% false-positive rate for anomaly detection. The model prioritizes payment, mistake, and login better with attention. The recommended method improves real-time accounting accuracy and recall by over 12% in F1-score compared to existing methods. Finally, NeuroLedger-Net automates complex accounting information procedures consistently and adaptably using scalable and intelligent technology.