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A Hybrid Model of AdaBoost and Back-Propagation Neural Network for Credit Scoring

  • Feng ShenEmail author
  • Xingchao Zhao
  • Dao Lan
  • Limei Ou
Conference paper
Part of the Lecture Notes on Multidisciplinary Industrial Engineering book series (LNMUINEN)

Abstract

Owing to the development of internet finance in China, credit scoring is growing into one of the most important issues in the field of financial risk management. Quantitative credit scoring models are widely used tools for credit risk assessment in financial institutions. In this study, an AdaBoost algorithm model based on back-propagation neural network for credit scoring with high accuracy and efficiency is proposed. We first illustrate the basic concepts of back-propagation neural network and AdaBoost algorithm and propose a hybrid model of AdaBoost and back-propagation neural network, then two real-world credit data sets are selected to demonstrate the effectiveness and feasibility of the proposed model. The results show that the proposed model can get higher accuracy compared to other classifiers listed in this study.

Keywords

Credit scoring AdaBoost model Back-propagation neural network 

Notes

Acknowledgements

This research was supported by the project of Research Center for System Sciences and Enterprise Development (Grant No.Xq16C03).

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.School of FinanceSouthwestern University of Finance and EconomicsChengduPeople’s Republic of China
  2. 2.Southwestern University of Finance and EconomicsChengduPeople’s Republic of China

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