Employee tax payment subject prediction method and device, equipment, storage medium and product

By constructing and training a neural network model, the problems of low efficiency, low accuracy, and high cost in predicting employee taxpayers are solved, achieving automated prediction and risk control, and making it suitable for complex enterprise environments.

CN122453543APending Publication Date: 2026-07-24CHINA MERCHANTS BANK
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Patent Information

Application Number
CN202610643493.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-05-11
Publication Date
2026-07-24

AI Technical Summary

Technical Problem

Existing technologies for predicting employee taxpayers are inefficient, inaccurate, costly, and unresponsive. Especially when a company has many subsidiaries and tax policies are complex and changeable, hard-coding methods require frequent code updates, resulting in high maintenance costs, while manual processing methods are inefficient, error-prone, and unable to cover all possible combinations.

Method used

A neural network model based on historical employee transfer data is used to build and train a training dataset. A four-layer neural network model is used to predict taxpayers. The model is trained by combining the cross-entropy loss function and the Adam optimizer, and the predicted taxpayers are output. The model is updated regularly to adapt to policy changes and organizational restructuring.

Benefits of technology

It enables automatic prediction of changes in employee taxpaying entities, reduces manual intervention, improves processing efficiency, lowers system maintenance costs, adapts to complex corporate organizational structures and tax policy changes, and balances automation with risk control.

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Abstract

The invention discloses an employee tax payment subject prediction method and device, equipment, a storage medium and a product, and relates to the technical field of artificial intelligence and data processing, and the method comprises the steps: obtaining new employee transfer information when new employee transfer occurs; performing feature extraction on the new employee transfer information to obtain feature data of transferred employees; and inputting the feature data of the mobilized employees into a pre-constructed and trained tax payment subject prediction model to obtain a predicted tax payment subject, the tax payment subject prediction model being constructed and trained based on employee historical mobilized data. Manual intervention can be reduced, the processing efficiency is improved, policy changes and organization structure adjustment are adapted, the system maintenance cost is reduced, and the adaptability is high; a model output result is used as a reference, business personnel can check and confirm final judgment, and automation and risk control are both considered.
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