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In today’s data-driven era, the accuracy and forward-looking prediction of university financial data are of vital significance for the rational allocation of educational resources and strategic planning. However, the financial data of colleges and universities come from various sources, have complex structure, and are scattered in different database systems, which brings great challenges to data integration and prediction. In order to predict university financial data more accurately, a research on university financial data integration forecast based on deep learning is put forward. Firstly, the Ontology method is used to integrate university financial data. Through data preprocessing, a shared vocabulary is constructed, and the semantic information of college finance is expressed by means of formal ontology, and the ontology attributes of key concepts are extracted. In addition, MC algorithm is used for security processing of integrated data to ensure the security of data in distributed database, and redundant processing of integrated data to generate unified XML format integrated data. Next, with the help of deep belief network in deep learning, feature extraction and dimensionality reduction are carried out on the integrated university financial data. Then, the university financial data prediction model based on these characteristics is constructed, and the university financial data integrated prediction based on deep learning is realized. The experimental results show that the proposed method not only achieves remarkable results in data integration, but also performs well in prediction. This research not only provides a new solution for university financial data forecasting, but also has important significance in theory and practice. In theory, it enriches the theoretical framework of deep learning and data integration. In practice, by improving the accuracy of financial data forecast, the proposed method can help universities to better allocate resources and make strategic planning, and promote the sustainable development of education.

The economic and social development in the digital era has put forward new requirements for the cross-border flow of financial data. Financial data are an important carrier of financial information. Due to the sensitivity of financial data, its cross-border flow involves citizens’ personal privacy, the interests of financial institutions and even national financial security. In practice, if there is no effective regulation on the cross-border flow of financial data, it will not only be difficult to explore the potential value of financial data, but also lead to various risks. At present, the United States and Europe adopt different regulatory models for the cross-border flow of financial data. In view of the problems existing in the supervision of cross-border capital flow in our country, this paper puts forward some policy suggestions for improving the existing cross-border capital flow monitoring system in our country. The system takes main supervision and off-site monitoring as the two main lines, sets up the framework of China’s cross-border capital flow monitoring institutions and off-site monitoring content framework, and constructs China’s cross-border capital flow monitoring index system on the basis of learning from international and domestic experience. The results show that cross-border capital flow has a significant effect on bank risk-taking. Considering the heterogeneity of cross-border capital, it is found that capital outflow and portfolio investment have a greater impact on bank risk-taking. A good economic development environment will not only bring profits to banks but also reduce their default risk.

This paper is based on fuzzy clustering algorithm on financial data, design of financial data mining system and analysis of financial data, analysis of financial data on the financial decision-making mechanism, from financial data how to enhance the information base of the forecast, financial data how to improve the pertinence of decision-making, financial data how to build a new competitive advantage, financial data how to promote the dynamic decision-making of four, through the analysis of data in financial decision-making specific implementation cases, focusing on the real-life problems faced by the management and the effect of financial data platform to solve problems; finally, through this paper, we hope to provide reference and reference for other similar enterprises to apply financial data for financial decision-making. This paper first describes the theory of financial data, analyzes the mechanism of financial data and financial decision-making, how financial data enhance the information base of forecasting, how financial data improve the pertinence of decision-making, how financial data build a new competitive advantage, how financial data promote dynamic decision-making four dimensions to summarize.

The complexity and frequent fluctuations of economic data pose a significant challenge to forecasting studies. In order to predict financial data more accurately, we build a fusion model for predicting financial data based on the idea of decomposition-recombination by combining the Sooty Tern Optimization Algorithm (STOA), Variational Mode Decomposition (VMD), Support Vector Machine (SVM) and Back Propagation Neural Network (BPNN). Firstly, after finding the optimal parameters of the VMD by the STOA, adjust the parameters of the VMD and decompose the financial data, remove the residual from the series, and constitute the remaining necessary information within the financial data into new series modeled by applying the SVM. Finally, calculate the error between the predicted and actual values. Notably, the residual is treated specially in the modeling. A Double-layer BPNN model is used to establish a mechanism to increase the sensitivity of the model fluctuations. The influence factor series and the residual are introduced as input variables to the BPNN to establish a mapping relationship between them and the error series. The results show that the model improves data utilization through five experiments, solves the problem of the VMD insensitivity to fluctuations, and improves the prediction accuracy of financial time series effectively.

We use minimum relative entropy (MRE) methods to estimate univariate probability density functions for a varied set of financial and economic variables, including S&P500 index returns, individual stock returns, power price returns and a number of housing-related economic variables. Some variables have fat tail distributions, others have finite support. Some variables have point masses in their distributions and others have multimodal distributions. We indicate specifically how the MRE approach can be tailored to the stylized facts of the variables that we consider and benchmark the MRE approach against alternative approaches. We find, for a number of variables, that the MRE approach outperforms the benchmark methods.

Cloud security in finance is considered as the key importance, taking account of the aspect of critical data stored over cloud spaces within organizations all around the globe. They are chiefly relying on cloud computing to accelerate their business profitability and scale up their business processes with enhanced productivity coming through flexible work environments offered in cloud-run working systems. Hence, there is a prerequisite to contemplate cloud security in the entire financial service sector. Moreover, the main issue challenged by privacy and security is the presence of diverse chances to attack the sensitive data by cloud operators, which leads to double the user’s anxiety on the stored data. For solving this problem, the main intent of this paper is to develop an intelligent privacy preservation approach for data stored in the cloud sector, mainly the financial data. The proposed privacy preservation model involves two main phases: (a) data sanitization and (b) data restoration. In the sanitization process, the sensitive data is hidden, which prevents sensitive information from leaking on the cloud side. Further, the normal as well as the sensitive data is stored in a cloud environment. For the sanitization process, a key should be generated that depends on the new meta-heuristic algorithm called crossover improved-lion algorithm (CI-LA), which is inspired by the lion’s unique social behavior. During data restoration, the same key should be used for effectively restoring the original data. Here, the optimal key generation is done in such a way that the objective model involves the degree of modification, hiding rate, and information preservation rate, which effectively enhance the cyber security performance in the cloud.