With the rapid development of big data,big data has been more and more applied in all walks of life.Under the big data environment,massive big data provides convenience for regional tax risk control and strategic deci...With the rapid development of big data,big data has been more and more applied in all walks of life.Under the big data environment,massive big data provides convenience for regional tax risk control and strategic decision-making but also increases the difficulty of data supervision and management.By analyzing the status quo of big data and tax risk management,this paper finds many problems and puts forward effective countermeasures for tax risk supervision and strategic management by using big data.展开更多
In this paper, we consider the dual risk model in which periodic taxation are paid according to a loss-carry-forward system and dividends are paid under a threshold strategy. We give an analytical approach to derive t...In this paper, we consider the dual risk model in which periodic taxation are paid according to a loss-carry-forward system and dividends are paid under a threshold strategy. We give an analytical approach to derive the expression of gδ(u) (i.e. the Laplace transform of the first upper exit time). We discuss the expected discounted tax payments for this model and obtain its corresponding integro-differential equations. Finally, for Erlang (2) inter-innovation distribution, closedform expressions for the expected discounted tax payments are given.展开更多
In this paper we consider the Markov-dependent risk model with tax payments in which the claim occurrence, the claim amount as well as the tax rate are controlled by an irreducible discrete-time Markov chain. Systems ...In this paper we consider the Markov-dependent risk model with tax payments in which the claim occurrence, the claim amount as well as the tax rate are controlled by an irreducible discrete-time Markov chain. Systems of integro-differential equations satisfied by the expected discounted tax payments and the non-ruin probability in terms of the ruin probabilities under the Markov-dependent risk model without tax are established. The analytical solutions of the systems of integro-differential equations are also obtained by the iteration method.展开更多
The digital economy has become an important driving force for the growth of fiscal revenue in various countries.Tax planning is essential for the cost accounting of PPP projects,reducing corporate tax burdens,and incr...The digital economy has become an important driving force for the growth of fiscal revenue in various countries.Tax planning is essential for the cost accounting of PPP projects,reducing corporate tax burdens,and increasing company value.This paper adopts a case analysis method,taking the smart highway PPP project in Guizhou Province as an example.Through statistical analysis,it is found that the value-for-money and big data of the PPP project affects tax planning,the project’s value-added tax input and output items have time mismatches,and enterprises Income tax payment imbalance.In the context of the digital economy,the tax planning of China’s PPP projects can be further improved:digital transformation and big data to prevent tax risks caused by value for money evaluation;based on digital technology to improve the value-added tax deduction chain,and digital communication platforms to alleviate time mismatch of value-added tax;use big data to monitor and balance project portfolio investment;improve the level of digital skills of financial personnel.展开更多
Tax risk behavior causes serious loss of fiscal revenue,damages the country’s public infrastructure,and disturbs the market economic order of fair competition.In recent years,tax risk detection,driven by information ...Tax risk behavior causes serious loss of fiscal revenue,damages the country’s public infrastructure,and disturbs the market economic order of fair competition.In recent years,tax risk detection,driven by information technology such as data mining and artificial intelligence,has received extensive attention.To promote the high-quality development of tax risk detection methods,this paper provides the first comprehensive overview and summary of existing tax risk detection methods worldwide.More specifi-cally,it first discusses the causes and negative impacts of tax risk behaviors,along with the development of tax risk detection.It then focuses on data-mining-based tax risk detection methods utilized around the world.Based on the different principles employed by the algorithms,existing risk detection methods can be divided into two categories:relationship-based and non-relationship-based.A total of 14 risk detection methods are identified,and each method is thoroughly explored and analyzed.Finally,four major technical bottlenecks of current data-driven tax risk detection methods are analyzed and discussed,including the difficulty of integrating and using fiscal and tax fragmented knowledge,unexplainable risk detection results,the high cost of risk detection algorithms,and the reliance of existing algorithms on labeled information.After investigating these issues,it is concluded that knowledge-guided and datadriven big data knowledge engineering will be the development trend in the field of tax risk in the future;that is,the gradual transition of tax risk detection from informatization to intelligence is the future development direction.展开更多
文摘With the rapid development of big data,big data has been more and more applied in all walks of life.Under the big data environment,massive big data provides convenience for regional tax risk control and strategic decision-making but also increases the difficulty of data supervision and management.By analyzing the status quo of big data and tax risk management,this paper finds many problems and puts forward effective countermeasures for tax risk supervision and strategic management by using big data.
文摘In this paper, we consider the dual risk model in which periodic taxation are paid according to a loss-carry-forward system and dividends are paid under a threshold strategy. We give an analytical approach to derive the expression of gδ(u) (i.e. the Laplace transform of the first upper exit time). We discuss the expected discounted tax payments for this model and obtain its corresponding integro-differential equations. Finally, for Erlang (2) inter-innovation distribution, closedform expressions for the expected discounted tax payments are given.
基金Supported by the National Natural Science Foundation of China(11401498)the Fundamental Research Funds for the Central Universities(WUT:2015IVA066)
文摘In this paper we consider the Markov-dependent risk model with tax payments in which the claim occurrence, the claim amount as well as the tax rate are controlled by an irreducible discrete-time Markov chain. Systems of integro-differential equations satisfied by the expected discounted tax payments and the non-ruin probability in terms of the ruin probabilities under the Markov-dependent risk model without tax are established. The analytical solutions of the systems of integro-differential equations are also obtained by the iteration method.
基金This paper is based on a research project financially supported by“Research on Cultivation of Big Data Thinking and Application Ability of University Undergraduates:Based on the Perspective of Digital Economy(GZJG20200203)”supported by Guizhou University of Finance and Economics“Teaching Quality and Teaching Reform Project(2019)”,entitled“Research on Teaching Reform of Property Insurance Courses under the Background of Big Data(2019JGZZC07)”supported by“Research on Legal Risks of Multinational Financial Leasing:Based on the‘One Belt One Road’Initiative(HB19FX022)”.
文摘The digital economy has become an important driving force for the growth of fiscal revenue in various countries.Tax planning is essential for the cost accounting of PPP projects,reducing corporate tax burdens,and increasing company value.This paper adopts a case analysis method,taking the smart highway PPP project in Guizhou Province as an example.Through statistical analysis,it is found that the value-for-money and big data of the PPP project affects tax planning,the project’s value-added tax input and output items have time mismatches,and enterprises Income tax payment imbalance.In the context of the digital economy,the tax planning of China’s PPP projects can be further improved:digital transformation and big data to prevent tax risks caused by value for money evaluation;based on digital technology to improve the value-added tax deduction chain,and digital communication platforms to alleviate time mismatch of value-added tax;use big data to monitor and balance project portfolio investment;improve the level of digital skills of financial personnel.
基金supported by the Key Research and Development Project in Shaanxi Province (2023GXLH-024)the National Natural Science Foundation of China (62250009,62002282,62037001,and 62192781).
文摘Tax risk behavior causes serious loss of fiscal revenue,damages the country’s public infrastructure,and disturbs the market economic order of fair competition.In recent years,tax risk detection,driven by information technology such as data mining and artificial intelligence,has received extensive attention.To promote the high-quality development of tax risk detection methods,this paper provides the first comprehensive overview and summary of existing tax risk detection methods worldwide.More specifi-cally,it first discusses the causes and negative impacts of tax risk behaviors,along with the development of tax risk detection.It then focuses on data-mining-based tax risk detection methods utilized around the world.Based on the different principles employed by the algorithms,existing risk detection methods can be divided into two categories:relationship-based and non-relationship-based.A total of 14 risk detection methods are identified,and each method is thoroughly explored and analyzed.Finally,four major technical bottlenecks of current data-driven tax risk detection methods are analyzed and discussed,including the difficulty of integrating and using fiscal and tax fragmented knowledge,unexplainable risk detection results,the high cost of risk detection algorithms,and the reliance of existing algorithms on labeled information.After investigating these issues,it is concluded that knowledge-guided and datadriven big data knowledge engineering will be the development trend in the field of tax risk in the future;that is,the gradual transition of tax risk detection from informatization to intelligence is the future development direction.
