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An Intelligent System for Default Identification in Commercial Bank
Journal Article

An Intelligent System for Default Identification in Commercial Bank

by Mia Anderson 1 ,  Oliver King 2  and  Qin Tsai 2
1
Colorado State University, College of Health and Human Sciences, United States
2
Queensland University of Technology, Faculty of Health, Australia
*
Author to whom correspondence should be addressed.
EMGBS  2023 2(1):3; https://doi.org/10.xxxx/xxxxxx
Received: 10 August 2023 / Accepted: 31 October 2023 / Published Online: 20 December 2023
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Abstract

Credit default identification technology usually requires a higher classification accuracy, and low rate of false positives, in view of the traditional credit default identification model based on machine learning, in dealing with high dimensional imbalance of financial transaction data on the problem of lower overall classification accuracy, along with the development of the large data, the credit transaction data of large-scale and high dimension feature, It is necessary to ensure the efficient and timely construction of the credit default recognition model. Semi-supervised learning can reduce the cost of manual labeling, and train the model in time by making full use of a large amount of unlabeled data and a small amount of labeled data, which has important research significance in the field of credit default recognition. At the same time, the traditional supervised learning model can only recognize the default behaviors that have happened before, but the default behaviors are variable, and the semi-supervised learning algorithm can recognize the unknown default behaviors. From the perspective of semi-supervised learning, DBN, as a semi-supervised deep learning framework, utilizes its feature learning ability and iForest unsupervised learning algorithm's ability to identify abnormal behaviors to propose a default identification method by use of the DBN-Iforest. This paper first introduces the experimental data, including the data source and the pretreatment process of the original data. Then the performance evaluation criteria applied to all experimental models in this paper are introduced in detail. Then the steps of DBN-iForest algorithm are described in detail. Next, algorithms for iForest optimization are introduced, including particle swarm optimization algorithm and simulated annealing optimization algorithm. Finally, through comparative analysis of experimental results at different levels, the advantages of the DBN-Iforest model are proved, the classification performance is improved, and the difficulty of data resource shortage in credit default recognition is overcome. The semi-supervised default identification method by use of the DBN-IForest fully improves the classification performance of DBN and iForest by using less label data.

Keywords: Default Identification; Semi-supervised learning; Particle Swarm Optimization; Simulated Annealing;
Copyright: © 2023 by Anderson, King and Tsai. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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APA Style
Anderson, M., King, O., & Tsai, Q. (2023). An Intelligent System for Default Identification in Commercial Bank. Economic Management & Global Business Studies, 2(1), 3. doi:10.xxxx/xxxxxx
ACS Style
Anderson, M.; King, O.; Tsai, Q. An Intelligent System for Default Identification in Commercial Bank. Economic Management & Global Business Studies, 2023, 2, 3. doi:10.xxxx/xxxxxx
AMA Style
Anderson M, King O, Tsai Q. An Intelligent System for Default Identification in Commercial Bank. Economic Management & Global Business Studies; 2023, 2(1):3. doi:10.xxxx/xxxxxx
Chicago/Turabian Style
Anderson, Mia; King, Oliver; Tsai, Qin 2023. "An Intelligent System for Default Identification in Commercial Bank" Economic Management & Global Business Studies 2, no.1:3. doi:10.xxxx/xxxxxx
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ACS Style
Anderson, M.; King, O.; Tsai, Q. An Intelligent System for Default Identification in Commercial Bank. Economic Management & Global Business Studies, 2023, 2, 3. doi:10.xxxx/xxxxxx
AMA Style
Anderson M, King O, Tsai Q. An Intelligent System for Default Identification in Commercial Bank. Economic Management & Global Business Studies; 2023, 2(1):3. doi:10.xxxx/xxxxxx
Chicago/Turabian Style
Anderson, Mia; King, Oliver; Tsai, Qin 2023. "An Intelligent System for Default Identification in Commercial Bank" Economic Management & Global Business Studies 2, no.1:3. doi:10.xxxx/xxxxxx
APA style
Anderson, M., King, O., & Tsai, Q. (2023). An Intelligent System for Default Identification in Commercial Bank. Economic Management & Global Business Studies, 2(1), 3. doi:10.xxxx/xxxxxx

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