Please use this identifier to cite or link to this item:
Title: An Investigation on Vietnamese Credit Scoring Based on Big Data Platform and Ensemble Learning
Authors: Tran, Quang Linh
Duong, Van Binh
Lam, Gia Huy
Vuong, Cong Dat
Do, Trong Hop
Keywords: Credit scoring
Big data
Ensemble learning
Feature engineering
Issue Date: Aug-2022
Publisher: Springer Nature
Abstract: The credit score is a vital indicator that can affect many aspects of people’s lives. However, evaluating credit scores is done manually, so it costs a large amount of money and time. This paper learns from disadvantages of previous research and brings some insights and empirical experiments so as to the advantages of distributed solutions for the problem of credit score in the future. The research compares some feature engineering techniques using a big data platform and ensemble learning methods to find the best solution for predicting the credit score. Since data related to customers’ financial activities grows enormously, a big data platform is necessary to handle this amount of data. In this paper, Spark which is a distributed, data processing framework, is used to save and process data. Some experiments are carried out to compare the effectiveness of feature engineering in this problem. Moreover, a comparative study about the performance of ensemble learning models is also given in this paper. A real-world Vietnamese credit scoring data set is used to develop and evaluate models. Four metrics are used to evaluate the performance of credit scoring models, namely F1-score, recall, precision, and accuracy. The results are promising with the highest accuracy of 72.9% in the combination Gradient-boosted Tree and cleaned data set with removing categorical features. This paper is a foundation for using big data platforms to handle financial data and much future research can be carried out to optimize the performance of this paper.
Description: International Conference on Intelligence of Things (ICIT 2022); Lecture Notes on Data Engineering and Communications Technologies, Vol.148; pp: 289-298
ISBN: 978-3-031-15063-0 (e)
Appears in Collections:NĂM 2022

Files in This Item:

 Sign in to read

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.