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  • Open Access

    ARTICLE

    MSEs Credit Risk Assessment Model Based on Federated Learning and Feature Selection

    Zhanyang Xu1, Jianchun Cheng1,*, Luofei Cheng1, Xiaolong Xu1,2, Muhammad Bilal3

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5573-5595, 2023, DOI:10.32604/cmc.2023.037287 - 29 April 2023

    Abstract Federated learning has been used extensively in business innovation scenarios in various industries. This research adopts the federated learning approach for the first time to address the issue of bank-enterprise information asymmetry in the credit assessment scenario. First, this research designs a credit risk assessment model based on federated learning and feature selection for micro and small enterprises (MSEs) using multi-dimensional enterprise data and multi-perspective enterprise information. The proposed model includes four main processes: namely encrypted entity alignment, hybrid feature selection, secure multi-party computation, and global model updating. Secondly, a two-step feature selection algorithm based… More >

  • Open Access

    ARTICLE

    Estimating Loss Given Default Based on Beta Regression

    Jamil J. Jaber1,2, Noriszura Ismail2, Siti Norafidah Mohd Ramli2, Baker Albadareen2, Nawaf N. Hamadneh3,*

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 3329-3344, 2021, DOI:10.32604/cmc.2021.014509 - 28 December 2020

    Abstract Loss given default (LGD) is a key parameter in credit risk management to calculate the required regulatory minimum capital. The internal ratings-based (IRB) approach under the Basel II allows institutions to determine the loss given default (LGD) on their own. In this study, we have estimated LGD for a credit portfolio data by using beta regression with precision parameter (∅) and mean parameter (μ). The credit portfolio data was obtained from a banking institution in Jordan; for the period of January 2010 until December 2014. In the first stage, we have used the “outstanding amount”… More >

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