A user guide of CART and random forests with applications in FinTech and InsurTech

Abstract In the realm of financial data analytics, machine learning techniques, particularly classification and regression trees (CARTs) and random forests, have shown remarkable efficiency. This article serves as a user guide for these methods, with an emphasis on their applicability and effectiveness in analyzing datasets in FinTech and InsurTech. In particular, we present several numerical examples and empirical studies, and demonstrate their superiority in handling data with a variety of input features, offering insights into their potential applications in the industries.

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Publication Details

Journal
Japanese Journal of Statistics and Data Science
Published
2024-08-19
DOI
https://doi.org/10.1007/s42081-024-00258-x
Citations
3
Primary Topic
Data Mining Algorithms and Applications
Type
article
Field-Weighted Citation Impact
2.34

Funders

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article

A user guide of CART and random forests with applications in FinTech and InsurTech

Yongzhao Chen, Sheung Chi Phillip Yam, Ka Chun Cheung, Ross Zhengyao Sun
3 citations
Japanese Journal of Statistics and Data Science
Data Mining Algorithms and Applications
2.34
article

A user guide of CART and random forests with applications in FinTech and InsurTech

Yongzhao Chen, Sheung Chi Phillip Yam, Ka Chun Cheung, Ross Zhengyao Sun
article en
3 citations

Abstract

Abstract In the realm of financial data analytics, machine learning techniques, particularly classification and regression trees (CARTs) and random forests, have shown remarkable efficiency. This article serves as a user guide for these methods, with an emphasis on their applicability and effectiveness in analyzing datasets in FinTech and InsurTech. In particular, we present several numerical examples and empirical studies, and demonstrate their superiority in handling data with a variety of input features, offering insights into their potential applications in the industries.

Japanese Journal of Statistics and Data ScienceVol. 7(2)
Chinese University of Hong Kong (HK), Hang Seng University of Hong Kong (HK), University of Hong Kong (HK)
San Diego Supercomputer Center, Hang Seng University of Hong Kong, Deutscher Akademischer Austauschdienst, University Grants Committee, Chinese University of Hong Kong
Openalex Percentile: Top 9%
Data Mining Algorithms and Applications
2.34
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A user guide of CART and random forests with applications in FinTech and InsurTech — Yongzhao Chen, Sheung Chi Phillip Yam, et al. · Japanese Journal of Statistics and Data Science (2024) | TGRS Research Map | TGRS