In government financial management, budget preparation is a crucial link. However, traditional budget preparation methods present numerous efficiency bottlenecks.

– Firstly, information collection is difficult. Government departments are numerous, and their business scope is extensive. Accurately collecting budget requirements and relevant data from each department requires significant time and human resources. According to statistics, the average time spent on collecting information for budget preparation in the industry accounts for 30% – 45% of the entire budget preparation cycle, and for some larger, more complex government agencies, this proportion can even reach 50%.

Furthermore, the accuracy and timeliness of information are difficult to guarantee. Data between different departments may have inconsistent standards or be outdated, leading budget preparation staff to spend a lot of effort on data verification and adjustment.

– Secondly, the budget preparation process is cumbersome. From budget submission, review, and adjustment to final approval, it often involves multiple levels and stages, and each stage can experience delays and communication breakdowns. Taking a government project budget preparation involving a listed company as an example, from the project department submitting a budget application, to the finance department's review, and then to the approval by the superior competent authority, the process involved at least 5 stages and took a total of 3 months. During this process, any delay in a single stage can affect the progress of the entire budget preparation.

– Furthermore, there is a lack of effective forecasting and analysis tools. Traditional budget preparation primarily relies on manual experience and simple spreadsheets, making it difficult to accurately forecast and analyze complex economic situations and business changes. For instance, in budget transparency applications in the education sector, due to the lack of scientific forecasting models, education departments find it difficult to accurately estimate the impact of factors such as changes in student numbers and the need for updates to teaching facilities on the budget. This results in budget preparation that is either too conservative, failing to meet actual needs, or too aggressive, leading to wasted funds.

– Finally, budget preparation is disconnected from actual execution. After budget preparation is completed, during actual execution, various unforeseen factors such as policy changes or emergencies may necessitate frequent budget adjustments. Traditional budget preparation methods lack flexibility and dynamic adjustment mechanisms, causing the gap between budget preparation and actual execution to widen. According to surveys, approximately 40% – 55% of government budgets in the industry require significant adjustments during execution, which not only increases the difficulty of budget management but also diminishes the authority and enforceability of the budget.

The application of blockchain technology in government financial management presents new opportunities for enhancing budget transparency, but it also poses certain challenges, making it a double-edged sword.

– On one hand, blockchain technology possesses characteristics such as decentralization, immutability, and traceability, which can effectively enhance budget transparency. By recording data from various stages of budget preparation, execution, and auditing on the blockchain, data sharing and transparency are achieved, allowing anyone to view and verify the authenticity and integrity of the data. For example, in a government financial project involving a unicorn company, blockchain technology was used to establish a budget transparency platform, enabling the public to query information such as project budget allocation and fund usage in real-time, thereby effectively enhancing government financial transparency and credibility. Furthermore, blockchain technology can enable the automatic execution of smart contracts, reducing human intervention and improving the efficiency and accuracy of budget execution.

– On the other hand, blockchain technology also presents some potential risks and issues. Firstly, the security and stability of the technology itself need improvement. While blockchain technology offers high security, it is not absolutely secure, and risks such as hacker attacks and data breaches still exist. Should a security vulnerability appear in a blockchain system, it could lead to the alteration and loss of budget data, with severe consequences for government financial management. Secondly, the application cost of blockchain technology is relatively high. The development, deployment, and maintenance of blockchain technology require substantial funding and technical support, which may be difficult for government departments with limited financial resources to bear. Moreover, the standardization and regulation of blockchain technology are still relatively low, and compatibility issues may exist between different blockchain platforms, which also poses certain difficulties for government financial management.

– Finally, the application of blockchain technology may challenge traditional government financial management models and legal regulations. The decentralized nature of blockchain technology conflicts to some extent with traditional centralized government management models, requiring government departments to undertake corresponding institutional reforms and legal adjustments. At the same time, the application of blockchain technology may also involve legal issues such as data privacy protection and intellectual property, necessitating strengthened supervision and regulation by government departments.

In government financial management, introducing a human-machine collaborative governance model is an effective way to enhance budget transparency and management efficiency.

– The human-machine collaborative governance model refers to combining artificial intelligence technology with human wisdom and experience to achieve intelligent and efficient government financial management. On one hand, artificial intelligence technology can provide scientific decision support for government departments by analyzing and mining large volumes of budget data. For example, using big data analysis technology, historical budget data can be analyzed to identify patterns and issues in budget preparation and execution, providing references for government departments to formulate budget policies and adjust budget plans. On the other hand, human wisdom and experience still play an irreplaceable role in government financial management. Government staff can evaluate and adjust the decision support provided by artificial intelligence technology based on actual circumstances, ensuring the rationality and feasibility of decisions.

– In budget transparency applications in the education sector, the human-machine collaborative governance model can play a significant role. For example, artificial intelligence technology can be used to monitor and analyze education department budget data in real-time, promptly identifying anomalies in budget execution, such as budget overruns or idle funds. Concurrently, education department staff can take appropriate measures to adjust and address issues based on the early warning information provided by AI technology, ensuring the rational use of the budget. Furthermore, the human-machine collaborative governance model can enhance communication and interaction efficiency between education departments and the public. By establishing an online platform, the public can easily query education department budget information and offer opinions and suggestions. Education department staff can utilize artificial intelligence technology to analyze and organize public opinions and suggestions, promptly responding to public concerns and improving the quality of services and credibility of education departments.

– However, the application of the human-machine collaborative governance model also faces some challenges. Firstly, the development of artificial intelligence technology is not yet fully mature, and it has certain errors and limitations. When government departments use AI technology, they need to carefully evaluate and verify the decision support it provides, avoiding blind reliance. Secondly, the human-machine collaborative governance model requires government staff to possess a certain level of information technology literacy and data analysis skills. Government departments need to strengthen the training and education of their staff to improve their ability and proficiency in using artificial intelligence technology. Finally, the application of the human-machine collaborative governance model requires the establishment of corresponding systems and regulations to ensure the appropriate use of artificial intelligence technology and the secure protection of data.

In government financial management, the issue of data silos severely impacts budget transparency and management efficiency, concealing significant true costs.

– Firstly, data silos lead to redundant information collection and entry, wasting significant human, material, and financial resources. Due to the inability to share data between different departments, they often have to collect and enter the same or similar data independently, which not only increases staff workload but also frequently results in data inconsistencies. Taking a government project involving a startup company as an example, both the project department and the finance department need to collect basic project information and fund usage. Due to the existence of data silos, the two departments must collect and enter data separately, leading to low work efficiency and difficulty in ensuring data accuracy. According to statistics, the cost waste caused by redundant data collection and entry in the industry accounts for approximately 15% – 25% of the total budget preparation cost.

– Secondly, data silos affect the scientific nature and accuracy of decision-making. When formulating budget policies and adjusting budget plans, government departments need to comprehensively consider information and data from various aspects. However, due to the existence of data silos, data between different departments cannot be shared and integrated in a timely manner, making it difficult for government departments to fully understand the actual situation, and decisions made may be biased. For example, when formulating budget policies in the education sector, education departments need information on student numbers, teaching facilities, teaching staff, and many other aspects. If this information is scattered across different departments and cannot be shared and integrated in a timely manner, education departments will find it difficult to formulate scientific and reasonable budget policies, potentially leading to wasted or insufficient educational resources.

– Furthermore, data silos increase the difficulty and cost of data management and maintenance. Because data is dispersed across different systems and departments, government departments must spend significant time and effort on data management and maintenance, including data backup, data recovery, and data security. Concurrently, data formats and standards may be inconsistent between different systems and departments, requiring data conversion and integration, which further increases the difficulty and cost of data management and maintenance.

– Finally, data silos affect communication and interaction between government departments and the public. Public attention to government financial budgets is growing, with a desire for timely access to relevant budget information. However, due to the existence of data silos, government departments find it difficult to fully, accurately, and timely disclose budget information to the public, leading to a less in-depth understanding of government financial budgets by the public, and impacting public trust and support for the government.

In government financial management, the application of smart contracts offers new means to enhance budget transparency and management efficiency, but it also presents certain trust traps that require attention.

– A smart contract is an automated contract based on blockchain technology, characterized by immutability and automatic execution. In government financial management, smart contracts can be used in various stages such as budget preparation, execution, and auditing, achieving automated and intelligent budget management. For example, in the budget execution phase, smart contracts can automatically execute fund disbursements and other operations according to budget preparation requirements and conditions, reducing human intervention and improving the efficiency and accuracy of budget execution.

– However, the trust traps of smart contracts are primarily manifested in the following aspects. Firstly, smart contract code may contain vulnerabilities. Smart contracts are written in code, and if the code contains vulnerabilities, it could be exploited by hackers or maliciously used, leading to the loss of budget funds. For instance, the 2016 The DAO incident occurred because of a vulnerability in the smart contract code, which was exploited by hackers, resulting in the theft of approximately $60 million worth of Ethereum. Secondly, the execution of smart contracts relies on the accuracy of external data. Smart contract execution is triggered by changes in external data; if external data is inaccurate or tampered with, it could lead to incorrect execution of the smart contract. For example, in the budget execution phase, a smart contract needs to disburse funds based on project progress and completion status. If the data regarding project progress and completion is inaccurate or tampered with, it could lead to the smart contract incorrectly disbursing funds.

– Finally, the legal status and regulatory mechanisms for smart contracts are still unclear. Smart contracts are an emerging technology, and their legal status and regulatory mechanisms vary across different countries and regions. In government financial management, the application of smart contracts must comply with relevant laws, regulations, and supervisory requirements. However, there is currently a lack of clear legal provisions and regulatory mechanisms, which introduces certain risks and uncertainties to the application of smart contracts.

Therefore, when applying smart contracts in government financial management, it is necessary to strengthen code auditing and security testing of smart contracts to ensure their code is secure and reliable. Concurrently, robust external data verification and regulatory mechanisms need to be established to ensure the accuracy and reliability of external data. Furthermore, it is essential to strengthen legal research and regulation concerning smart contracts, clarify their legal status and regulatory requirements, and provide a sound legal environment and regulatory safeguards for their application.