Design and analysis of management platform based on financial big data

Traditional financial accounting will become limited by new technologies which are unable to meet the market development. In order to make financial big data generate business value and improve the information application level of financial management, aiming at the high error rate of current financial data classification system, this article adopts the fuzzy clustering algorithm to classify financial data automatically, and adopts the local outlier factor algorithm with neighborhood relation (NLOF) to detect abnormal data. In addition, a financial data management platform based on distributed Hadoop architecture is designed, which combines MapReduce framework with the fuzzy clustering algorithm and the local outlier factor (LOF) algorithm, and uses MapReduce to operate in parallel with the two algorithms, thus improving the performance of the algorithm and the accuracy of the algorithm, and helping to improve the operational efficiency of enterprise financial data processing. The comparative experimental results show that the proposed platform can achieve the best the running efficiency and the accuracy of financial data classification compared with other methods, which illustrate the effectiveness and superiority of the proposed platform.

Automated summary

In order to improve the information application level of financial management and the business value of financial big data, this article automatically classifies financial data using the fuzzy clustering algorithm and detects abnormal data using the local outlier factor (LOF) algorithm based on neighborhood relation. A financial data management platform based on distributed Hadoop architecture is designed, combining MapReduce framework with the fuzzy clustering algorithm and LOF algorithm to enhance the algorithm's performance and accuracy, thus improving operational efficiency of enterprise financial data processing. Comparative experimental results demonstrate that the proposed platform achieves the best running efficiency and financial data classification accuracy compared with other methods, illustrating the effectiveness and superiority of the platform.