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

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.