Financial planning method and system based on data driving and medium

Through a data-driven financial planning method, using automated scripts and neural network models, the complex problems of data dispersion and analysis in traditional financial management are solved, efficient integration and accurate analysis of financial data are achieved, and the decision-making efficiency and market adaptability of enterprises are improved.

CN119991321AInactive Publication Date: 2025-05-13INSPUR GENERSOFT CO LTD
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Patent Information

Application Number
CN202510064622.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-01-15
Publication Date
2025-05-13
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

Traditional enterprise financial management methods have problems such as data dispersion, lag in updates, and complex analysis, resulting in inefficient decision-making and increased risks.

Method used

Using a data-driven financial planning method, we automatically execute financial processes through automated execution scripts, obtain corporate financial information, count historical financial data, perform preprocessing and vector transformation, build neural network models, train and verify, and output predicted financial data for financial planning.

Benefits of technology

It realizes efficient integration and analysis of financial data, provides accurate financial information, reduces decision-making risks, and improves the company's decision-making efficiency and market adaptability.

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Abstract

The invention discloses a financial planning method and system based on data driving and a medium, and the method comprises the steps: automatically executing a financial process through an automatic execution script, and obtaining the financial information of an enterprise; counting historical financial data, preprocessing the historical financial data, and constructing a training set and a verification set, the historical financial data including reimbursement data, loan and repayment data, and profit data; obtaining financial data vectors, extracting key feature vectors in the financial data vectors, and constructing a neural network model; determining a loss function based on prediction data output by the neural network model, and adjusting model parameters according to the loss function until the neural network model converges; outputting predicted financial data through a convergent neural network model based on the latest financial data; and planning enterprise finance according to the predicted financial data. Through collection, arrangement and analysis of financial data, accurate and comprehensive information can be provided for enterprises, so that decision makers can make decisions based on objective data.
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