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Journal of the Operational Research Society Volume 71, 2020 - Issue 5
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Articles

Identification of credit risk based on cluster analysis of account behaviours

Maha BakobenDepartment of Mathematics, Imperial College London, London, UK; ;Statistics Department, King Abdulaziz University, Jeddah, Saudi Arabia; Correspondence[email protected]
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Tony BellottiDepartment of Mathematics, Imperial College London, London, UK; View further author information
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Niall AdamsDepartment of Mathematics, Imperial College London, London, UK; ;Heilbronn Institute for Mathematical Research, University of Bristol, Bristol, UKView further author information
Pages 775-783 | Received 04 Oct 2016, Accepted 29 Jan 2019, Published online: 20 Apr 2019

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Viani Biatat Djeundje & Jonathan Crook. (2023) Sensitivity of stress testing metrics to estimation risk, account behaviour and volatility for credit defaults. Journal of the Operational Research Society 74:7, pages 1763-1774.
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Jacopo Giacomelli. (2023) Parametric estimation of latent default frequency in credit insurance. Journal of the Operational Research Society 74:1, pages 330-350.
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Articles from other publishers (15)

Jiaxing Wang, Guoquan Liu, Xiaobo Xu & Xinjie Xing. (2024) Credit risk prediction for small and medium enterprises utilizing adjacent enterprise data and a relational graph attention network. Journal of Management Science and Engineering 9:2, pages 177-192.
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Diego Barragán Garnica. (2023) Patrones de comportamiento de clientes con tarjetas de crédito de consumo con deterioro de calificación por riesgo utilizando K-means. ODEON:22, pages 7-37.
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Kangshuai Guo, Shichao Luo, Ming Liang, Zhongjian Zhang, Huabin Yang, Yan Wang & Yingjie Zhou. (2023) Credit Default Prediction on Time-Series Behavioral Data Using Ensemble Models. Credit Default Prediction on Time-Series Behavioral Data Using Ensemble Models.
Wenfeng Zhang, Ming K. Lim, Mei Yang, Xingzhi Li & Du Ni. (2023) Using deep learning to interpolate the missing data in time-series for credit risks along supply chain. Industrial Management & Data Systems 123:5, pages 1401-1417.
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Aimin Li, Zhiyong Li & Anthony Bellotti. (2023) Predicting loss given default of unsecured consumer loans with time-varying survival scores. Pacific-Basin Finance Journal 78, pages 101949.
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Chrysovalantis Gaganis, Panagiota Papadimitri, Fotios Pasiouras & Menelaos Tasiou. (2022) Social traits and credit card default: a two-stage prediction framework. Annals of Operations Research.
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Tsan-Ming Choi. (2022) Achieving economic sustainability: operations research for risk analysis and optimization problems in the blockchain era. Annals of Operations Research.
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Pablo Vilas, Laura Andreu & José Luis Sarto. (2022) Cluster analysis to validate the sustainability label of stock indices: An analysis of the inclusion and exclusion processes in terms of size and ESG ratings. Journal of Cleaner Production 330, pages 129862.
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Ralf Kellner, Maximilian Nagl & Daniel Rösch. (2022) Opening the black box – Quantile neural networks for loss given default prediction. Journal of Banking & Finance 134, pages 106334.
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Jing Gao, Wenjun Sun & Xin Sui. (2021) Research on Default Prediction for Credit Card Users Based on XGBoost-LSTM Model. Discrete Dynamics in Nature and Society 2021, pages 1-13.
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Eva Kalinová. (2021) Artificial Intelligence for Cluster Analysis: Case Study of Transport Companies in Czech Republic. Journal of Risk and Financial Management 14:9, pages 411.
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Hongxiang Li, Ao Feng, Bin Lin, Houcheng Su, Zixi Liu, Xuliang Duan, Haibo Pu & Yifei Wang. (2021) A novel method for credit scoring based on feature transformation and ensemble model. PeerJ Computer Science 7, pages e579.
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Yoga Suhas Kuruba Manjunath & Rasha F. Kashef. (2021) Distributed clustering using multi-tier hierarchical overlay super-peer peer-to-peer network architecture for efficient customer segmentation. Electronic Commerce Research and Applications 47, pages 101040.
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Hua SongHua Song. 2021. Smart Supply Chain Finance. Smart Supply Chain Finance 587 616 .
Marc Gürtler & Marvin Zöllner. (2022) Clustering Meets Machine Learning: Increasing the Accuracy of Advanced Tree-Based Methods in LGD Estimation. SSRN Electronic Journal.
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