Security design, market risk and round quotes in the treasury bond market

Nikiforov, Andrei; Pilotte, Eugene A.

doi:10.1080/13504851.2018.1527435

Cited by 4 publications

(1 citation statement)

References 9 publications

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“…In recent years, with the vigorous development of BD technology, artificial intelligence, and machine learning in engineering and academic circles, the relevant data models have developed very well and fully, and the advantages of the decision tree are its good robustness, full sample mining, high accuracy, fast implementation, fast running speed, and low implementation cost [8,9]. erefore, this paper introduces the concept of a BD-guided decision tree classification algorithm to alleviate the problem of heterogeneous data processing in traditional data processing and meet the needs of different data source storage media, introduces the scalable BD analysis model to obtain the characteristics of users' interest migration, applies the algorithm based on the decision tree algorithm model and takes the specific user data of an insurance company as an example to build application scenarios for model training and data prediction, and innovatively introduces the value rate to classify users to solve the problems faced by the company, such as long time, low efficiency, and low accuracy in processing massive user data [10,11].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Insurance Marketing Planning Based on BD-Guided Decision Tree Classification Algorithm

Long

2022

Security and Communication Networks

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The emergence and development of Chinese insurance companies are affected by their own unique national conditions. The modern marketing concept lags behind, lacks the practical experience of scientifically formulating marketing strategies, and insurance practitioners lack marketing knowledge and the ability to absorb modern marketing achievements to guide practice. Therefore, China’s insurance industry inevitably has many problems in insurance marketing. In recent years, with the rapid development of big data (BD) technology, artificial intelligence, and machine learning in engineering and academia, relevant data models have been well developed. The advantages of the decision tree are its good robustness, full sample mining, high precision, fast implementation, fast running speed, and low implementation cost. This paper studies the application of the decision tree classification algorithm under the guidance of BD in insurance marketing planning. The running results of the decision tree classification algorithm model show what factors will affect the accuracy and recall rate of customer churn decision-making. The predicted value and scoring value of users are extracted to test the model, and the results are within a reasonable range. The running time of this model is 2,320.36 s, which is more efficient than the 34 min 25 s of traditional SAS. Therefore, the model can be put into use, and it is necessary to establish a long-term and stable relationship with customers.

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Section: Introductionmentioning

confidence: 99%