大数据征信算法的可解释性研究

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

大数据征信算法的可解释性研究. / Wu, Hui; Han, Haiting; Qu, Xiuwei; Sun, Shengli.

In: CREDIT REFERENCE , Vol. 2020, No. 5, 2020, p. 44-51.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Wu, H, Han, H, Qu, X & Sun, S 2020, '大数据征信算法的可解释性研究', CREDIT REFERENCE , vol. 2020, no. 5, pp. 44-51.

APA

Wu, H., Han, H., Qu, X., & Sun, S. (2020). 大数据征信算法的可解释性研究. CREDIT REFERENCE , 2020(5), 44-51.

Vancouver

Wu H, Han H, Qu X, Sun S. 大数据征信算法的可解释性研究. CREDIT REFERENCE . 2020;2020(5):44-51.

Author

Wu, Hui ; Han, Haiting ; Qu, Xiuwei ; Sun, Shengli. / 大数据征信算法的可解释性研究. In: CREDIT REFERENCE . 2020 ; Vol. 2020, No. 5. pp. 44-51.

Bibtex

@article{23b25c3352e5439e9cc1dc979081f1a5,
title = "大数据征信算法的可解释性研究",
abstract = "In view of the problems of low transparency and non-interpretability caused by the widespread use of black-box bigdata credit evaluation techniques such as integrated learning and deep learning in credit investigation, a credit evaluation model interpretation method based on propensity score was proposed. The general framework can be used to conduct explanatory analysis on the black box model of big data credit investigation, so as to make it meet the KYC and KYB requirements in the financial field, and improve the applicability of machine learning, deep learning and other algorithms in the field of credit investigation.",
author = "Hui Wu and Haiting Han and Xiuwei Qu and Shengli Sun",
year = "2020",
language = "Kinesisk",
volume = "2020",
pages = "44--51",
journal = "CREDIT REFERENCE ",
issn = "1674-747X",
number = "5",

}

RIS

TY - JOUR

T1 - 大数据征信算法的可解释性研究

AU - Wu, Hui

AU - Han, Haiting

AU - Qu, Xiuwei

AU - Sun, Shengli

PY - 2020

Y1 - 2020

N2 - In view of the problems of low transparency and non-interpretability caused by the widespread use of black-box bigdata credit evaluation techniques such as integrated learning and deep learning in credit investigation, a credit evaluation model interpretation method based on propensity score was proposed. The general framework can be used to conduct explanatory analysis on the black box model of big data credit investigation, so as to make it meet the KYC and KYB requirements in the financial field, and improve the applicability of machine learning, deep learning and other algorithms in the field of credit investigation.

AB - In view of the problems of low transparency and non-interpretability caused by the widespread use of black-box bigdata credit evaluation techniques such as integrated learning and deep learning in credit investigation, a credit evaluation model interpretation method based on propensity score was proposed. The general framework can be used to conduct explanatory analysis on the black box model of big data credit investigation, so as to make it meet the KYC and KYB requirements in the financial field, and improve the applicability of machine learning, deep learning and other algorithms in the field of credit investigation.

M3 - Tidsskriftartikel

VL - 2020

SP - 44

EP - 51

JO - CREDIT REFERENCE

JF - CREDIT REFERENCE

SN - 1674-747X

IS - 5

ER -

ID: 244807502