Bayesian ensemble assessment for credit scoring

Haojie Chen; Minghui Jiang; Xue Wang

doi:10.1109/IEIS.2017.8078596

Bayesian ensemble assessment for credit scoring

Haojie Chen
, Minghui Jiang
, Xue Wang

Chinese Academy of Social Sciences
School of Management, Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Scopus citations

Abstract

Credit scoring is explored to assess default risk of consumer behaviors for financial institutions, banks in particular. The advanced Bayesian algorithm is proposed for credit assessment. The new trial ensembles logistic regression analysis (LRA), cluster and MLP-NN in Bayesian approach as an advanced classifier. The investigation contain evidence that Bayesian ensemble technique optimizes LRA, cluster and MLP-NN consequence in credit assessment. Simultaneously, Bayesian approach creates positive advantage on algorithm accuracy and fitness for credit evaluation, nevertheless, balances and decreases classified error ratio (ER).

Original language	English
Title of host publication	4th International Conference on Industrial Economics System and Industrial Security Engineering, IEIS 2017
Editors	Menggang Li, Runtong Zhang, Guowei Hua, Liang Zhao
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538609958
DOIs	https://doi.org/10.1109/IEIS.2017.8078596
State	Published - 20 Oct 2017
Externally published	Yes
Event	4th International Conference on Industrial Economics System and Industrial Security Engineering, IEIS 2017 - Kyoto, Japan Duration: 24 Jul 2017 → 27 Jul 2017

Publication series

Name	4th International Conference on Industrial Economics System and Industrial Security Engineering, IEIS 2017

Conference

Conference	4th International Conference on Industrial Economics System and Industrial Security Engineering, IEIS 2017
Country/Territory	Japan
City	Kyoto
Period	24/07/17 → 27/07/17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 12 Responsible Consumption and Production

Keywords

Bayesian approach
Cluster
LRA
MLP-NN
credit scoring

Access to Document

10.1109/IEIS.2017.8078596

Cite this

Chen, H., Jiang, M., & Wang, X. (2017). Bayesian ensemble assessment for credit scoring. In M. Li, R. Zhang, G. Hua, & L. Zhao (Eds.), 4th International Conference on Industrial Economics System and Industrial Security Engineering, IEIS 2017 Article 8078596 (4th International Conference on Industrial Economics System and Industrial Security Engineering, IEIS 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IEIS.2017.8078596

Chen, Haojie ; Jiang, Minghui ; Wang, Xue. / Bayesian ensemble assessment for credit scoring. 4th International Conference on Industrial Economics System and Industrial Security Engineering, IEIS 2017. editor / Menggang Li ; Runtong Zhang ; Guowei Hua ; Liang Zhao. Institute of Electrical and Electronics Engineers Inc., 2017. (4th International Conference on Industrial Economics System and Industrial Security Engineering, IEIS 2017).

@inproceedings{76fea6f5b6a44afcb9bb1de43a12071f,

title = "Bayesian ensemble assessment for credit scoring",

abstract = "Credit scoring is explored to assess default risk of consumer behaviors for financial institutions, banks in particular. The advanced Bayesian algorithm is proposed for credit assessment. The new trial ensembles logistic regression analysis (LRA), cluster and MLP-NN in Bayesian approach as an advanced classifier. The investigation contain evidence that Bayesian ensemble technique optimizes LRA, cluster and MLP-NN consequence in credit assessment. Simultaneously, Bayesian approach creates positive advantage on algorithm accuracy and fitness for credit evaluation, nevertheless, balances and decreases classified error ratio (ER).",

keywords = "Bayesian approach, Cluster, LRA, MLP-NN, credit scoring",

author = "Haojie Chen and Minghui Jiang and Xue Wang",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 4th International Conference on Industrial Economics System and Industrial Security Engineering, IEIS 2017 ; Conference date: 24-07-2017 Through 27-07-2017",

year = "2017",

month = oct,

day = "20",

doi = "10.1109/IEIS.2017.8078596",

language = "英语",

series = "4th International Conference on Industrial Economics System and Industrial Security Engineering, IEIS 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

editor = "Menggang Li and Runtong Zhang and Guowei Hua and Liang Zhao",

booktitle = "4th International Conference on Industrial Economics System and Industrial Security Engineering, IEIS 2017",

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}

Chen, H, Jiang, M & Wang, X 2017, Bayesian ensemble assessment for credit scoring. in M Li, R Zhang, G Hua & L Zhao (eds), 4th International Conference on Industrial Economics System and Industrial Security Engineering, IEIS 2017., 8078596, 4th International Conference on Industrial Economics System and Industrial Security Engineering, IEIS 2017, Institute of Electrical and Electronics Engineers Inc., 4th International Conference on Industrial Economics System and Industrial Security Engineering, IEIS 2017, Kyoto, Japan, 24/07/17. https://doi.org/10.1109/IEIS.2017.8078596

Bayesian ensemble assessment for credit scoring. / Chen, Haojie; Jiang, Minghui; Wang, Xue.
4th International Conference on Industrial Economics System and Industrial Security Engineering, IEIS 2017. ed. / Menggang Li; Runtong Zhang; Guowei Hua; Liang Zhao. Institute of Electrical and Electronics Engineers Inc., 2017. 8078596 (4th International Conference on Industrial Economics System and Industrial Security Engineering, IEIS 2017).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Jiang, Minghui

AU - Wang, Xue

PY - 2017/10/20

Y1 - 2017/10/20

N2 - Credit scoring is explored to assess default risk of consumer behaviors for financial institutions, banks in particular. The advanced Bayesian algorithm is proposed for credit assessment. The new trial ensembles logistic regression analysis (LRA), cluster and MLP-NN in Bayesian approach as an advanced classifier. The investigation contain evidence that Bayesian ensemble technique optimizes LRA, cluster and MLP-NN consequence in credit assessment. Simultaneously, Bayesian approach creates positive advantage on algorithm accuracy and fitness for credit evaluation, nevertheless, balances and decreases classified error ratio (ER).

AB - Credit scoring is explored to assess default risk of consumer behaviors for financial institutions, banks in particular. The advanced Bayesian algorithm is proposed for credit assessment. The new trial ensembles logistic regression analysis (LRA), cluster and MLP-NN in Bayesian approach as an advanced classifier. The investigation contain evidence that Bayesian ensemble technique optimizes LRA, cluster and MLP-NN consequence in credit assessment. Simultaneously, Bayesian approach creates positive advantage on algorithm accuracy and fitness for credit evaluation, nevertheless, balances and decreases classified error ratio (ER).

KW - Bayesian approach

KW - Cluster

KW - LRA

KW - MLP-NN

KW - credit scoring

UR - https://www.scopus.com/pages/publications/85039970449

U2 - 10.1109/IEIS.2017.8078596

DO - 10.1109/IEIS.2017.8078596

M3 - 会议稿件

AN - SCOPUS:85039970449

T3 - 4th International Conference on Industrial Economics System and Industrial Security Engineering, IEIS 2017

BT - 4th International Conference on Industrial Economics System and Industrial Security Engineering, IEIS 2017

A2 - Li, Menggang

A2 - Zhang, Runtong

A2 - Hua, Guowei

A2 - Zhao, Liang

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 4th International Conference on Industrial Economics System and Industrial Security Engineering, IEIS 2017

Y2 - 24 July 2017 through 27 July 2017

ER -

Chen H, Jiang M, Wang X. Bayesian ensemble assessment for credit scoring. In Li M, Zhang R, Hua G, Zhao L, editors, 4th International Conference on Industrial Economics System and Industrial Security Engineering, IEIS 2017. Institute of Electrical and Electronics Engineers Inc. 2017. 8078596. (4th International Conference on Industrial Economics System and Industrial Security Engineering, IEIS 2017). doi: 10.1109/IEIS.2017.8078596

Bayesian ensemble assessment for credit scoring

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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