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Credit Risk Analysis using Machine and Deep learning models

Citations

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Cited by:

  1. Eduard Sariev & Guido Germano, 2020. "Bayesian regularized artificial neural networks for the estimation of the probability of default," Quantitative Finance, Taylor & Francis Journals, vol. 20(2), pages 311-328, February.
  2. Hang Miao & Kui Zhao & Zhun Wang & Linbo Jiang & Quanhui Jia & Yanming Fang & Quan Yu, 2020. "Intelligent Credit Limit Management in Consumer Loans Based on Causal Inference," Papers 2007.05188, arXiv.org.
  3. Dan Wang & Zhi Chen & Ionut Florescu, 2021. "A Sparsity Algorithm with Applications to Corporate Credit Rating," Papers 2107.10306, arXiv.org.
  4. Theuri, Joseph & Olukuru, John, 2022. "The impact of Artficial Intelligence and how it is shaping banking," KBA Centre for Research on Financial Markets and Policy Working Paper Series 61, Kenya Bankers Association (KBA).
  5. Apostolos Ampountolas & Titus Nyarko Nde & Paresh Date & Corina Constantinescu, 2021. "A Machine Learning Approach for Micro-Credit Scoring," Risks, MDPI, vol. 9(3), pages 1-20, March.
  6. Sanjay Kumar & Rafeeq Ahmed & Salil Bharany & Mohammed Shuaib & Tauseef Ahmad & Elsayed Tag Eldin & Ateeq Ur Rehman & Muhammad Shafiq, 2022. "Exploitation of Machine Learning Algorithms for Detecting Financial Crimes Based on Customers’ Behavior," Sustainability, MDPI, vol. 14(21), pages 1-24, October.
  7. Martin Leo & Suneel Sharma & K. Maddulety, 2019. "Machine Learning in Banking Risk Management: A Literature Review," Risks, MDPI, vol. 7(1), pages 1-22, March.
  8. Salima Smiti & Makram Soui, 2020. "Bankruptcy Prediction Using Deep Learning Approach Based on Borderline SMOTE," Information Systems Frontiers, Springer, vol. 22(5), pages 1067-1083, October.
  9. Roman P. Bulyga & Alexey A. Sitnov & Liudmila V. Kashirskaya & Irina V. Safonova, 2020. "Transparency of credit institutions," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 7(4), pages 3158-3172, June.
  10. Dominique Guegan & Bertrand K. Hassani, 2019. "Risk Measurement," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-02119256, HAL.
  11. Jeremy Fouliard & Michael Howell & Hélène Rey & Vania Stavrakeva, 2020. "Answering the Queen: Machine Learning and Financial Crises," NBER Working Papers 28302, National Bureau of Economic Research, Inc.
  12. Nenad Milojević & Srdjan Redzepagic, 2021. "Prospects of Artificial Intelligence and Machine Learning Application in Banking Risk Management," Journal of Central Banking Theory and Practice, Central bank of Montenegro, vol. 10(3), pages 41-57.
  13. Kim, A. & Yang, Y. & Lessmann, S. & Ma, T. & Sung, M.-C. & Johnson, J.E.V., 2020. "Can deep learning predict risky retail investors? A case study in financial risk behavior forecasting," European Journal of Operational Research, Elsevier, vol. 283(1), pages 217-234.
  14. Canto, José Augusto & Silva, Amélia Cristina Ferreira & Leite, Gabriela & Machado-Santos, Carlos, 2019. "Insolvency prediction for Portuguese agro-industrial SME: Tree Bagging Methodology," Agricultural Economics Review, Greek Association of Agricultural Economists, vol. 0(Issue 2).
  15. Paritosh Navinchandra Jha & Marco Cucculelli, 2021. "A New Model Averaging Approach in Predicting Credit Risk Default," Risks, MDPI, vol. 9(6), pages 1-15, June.
  16. Revathi Bhuvaneswari & Antonio Segalini, 2020. "Determining Secondary Attributes for Credit Evaluation in P2P Lending," Papers 2006.13921, arXiv.org.
  17. José Américo Pereira Antunes, 2021. "To supervise or to self-supervise: a machine learning based comparison on credit supervision," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-21, December.
  18. Kolesnikova, A. & Yang, Y. & Lessmann, S. & Ma, T. & Sung, M.-C. & Johnson, J.E.V., 2019. "Can Deep Learning Predict Risky Retail Investors? A Case Study in Financial Risk Behavior Forecasting," IRTG 1792 Discussion Papers 2019-023, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
  19. Anastasios Petropoulos & Vasilis Siakoulis & Evaggelos Stavroulakis & Aristotelis Klamargias, 2019. "A robust machine learning approach for credit risk analysis of large loan level datasets using deep learning and extreme gradient boosting," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Are post-crisis statistical initiatives completed?, volume 49, Bank for International Settlements.
  20. Salomey Osei & Berthine Nyunga Mpinda & Jules Sadefo-Kamdem & Jeremiah Fadugba, 2021. "Accuracies of some Learning or Scoring Models for Credit Risk Measurement," Working Papers hal-03194081, HAL.
  21. Ajitha Kumari Vijayappan Nair Biju & Ann Susan Thomas & J Thasneem, 2024. "Examining the research taxonomy of artificial intelligence, deep learning & machine learning in the financial sphere—a bibliometric analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(1), pages 849-878, February.
  22. Huei-Wen Teng & Michael Lee, 2019. "Estimation Procedures of Using Five Alternative Machine Learning Methods for Predicting Credit Card Default," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 22(03), pages 1-27, September.
  23. Hossein Hassani & Xu Huang & Emmanuel Silva & Mansi Ghodsi, 2020. "Deep Learning and Implementations in Banking," Annals of Data Science, Springer, vol. 7(3), pages 433-446, September.
  24. Parisa Golbayani & Ionuc{t} Florescu & Rupak Chatterjee, 2020. "A comparative study of forecasting Corporate Credit Ratings using Neural Networks, Support Vector Machines, and Decision Trees," Papers 2007.06617, arXiv.org.
  25. Marc Andreas Schmitt, 2022. "Deep Learning in Business Analytics: A Clash of Expectations and Reality," Papers 2205.09337, arXiv.org.
  26. Alexis Bogroff & Dominique Guégan, 2019. "Artificial Intelligence, Data, Ethics. An Holistic Approach for Risks and Regulation," Working Papers 2019: 19, Department of Economics, University of Venice "Ca' Foscari".
  27. Ștefan Ionescu & Nora Chiriță & Ionuț Nica & Camelia Delcea, 2023. "An Analysis of Residual Financial Contagion in Romania’s Banking Market for Mortgage Loans," Sustainability, MDPI, vol. 15(15), pages 1-32, August.
  28. Yuta Tanoue & Satoshi Yamashita & Hideaki Nagahata, 2020. "Comparison study of two-step LGD estimation model with probability machines," Risk Management, Palgrave Macmillan, vol. 22(3), pages 155-177, September.
  29. Marc Schmitt, 2022. "Deep Learning vs. Gradient Boosting: Benchmarking state-of-the-art machine learning algorithms for credit scoring," Papers 2205.10535, arXiv.org.
  30. Keerthana Sivamayil & Elakkiya Rajasekar & Belqasem Aljafari & Srete Nikolovski & Subramaniyaswamy Vairavasundaram & Indragandhi Vairavasundaram, 2023. "A Systematic Study on Reinforcement Learning Based Applications," Energies, MDPI, vol. 16(3), pages 1-23, February.
  31. Ionuț Nica & Daniela Blană Alexandru & Simona Liliana Paramon Crăciunescu & Ștefan Ionescu, 2021. "Automated Valuation Modelling: Analysing Mortgage Behavioural Life Profile Models Using Machine Learning Techniques," Sustainability, MDPI, vol. 13(9), pages 1-27, May.
  32. Chen, Shunqin & Guo, Zhengfeng & Zhao, Xinlei, 2021. "Predicting mortgage early delinquency with machine learning methods," European Journal of Operational Research, Elsevier, vol. 290(1), pages 358-372.
  33. Gunnarsson, Björn Rafn & vanden Broucke, Seppe & Baesens, Bart & Óskarsdóttir, María & Lemahieu, Wilfried, 2021. "Deep learning for credit scoring: Do or don’t?," European Journal of Operational Research, Elsevier, vol. 295(1), pages 292-305.
  34. Roy Cerqueti & Francesca Pampurini & Annagiulia Pezzola & Anna Grazia Quaranta, 2022. "Dangerous liasons and hot customers for banks," Review of Quantitative Finance and Accounting, Springer, vol. 59(1), pages 65-89, July.
  35. A. R. Provenzano & D. Trifir`o & A. Datteo & L. Giada & N. Jean & A. Riciputi & G. Le Pera & M. Spadaccino & L. Massaron & C. Nordio, 2020. "Machine Learning approach for Credit Scoring," Papers 2008.01687, arXiv.org.
  36. Sarat Chandra Nayak & Bijan Bihari Misra, 2019. "A chemical-reaction-optimization-based neuro-fuzzy hybrid network for stock closing price prediction," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-34, December.
  37. Dimitrios Nikolaidis & Michalis Doumpos, 2022. "Credit Scoring with Drift Adaptation Using Local Regions of Competence," SN Operations Research Forum, Springer, vol. 3(4), pages 1-28, December.
  38. Xu Chen & Chunhong Liu & Changchun Gao & Yao Jiang, 2021. "Mechanism Underlying the Formation of Virtual Agglomeration of Creative Industries: Theoretical Analysis and Empirical Research," Sustainability, MDPI, vol. 13(4), pages 1-21, February.
  39. Alexis Bogroff & Dominique Guegan, 2019. "Artificial Intelligence, Data, Ethics: An Holistic Approach for Risks and Regulation," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-02181597, HAL.
  40. Jillian M. Clements & Di Xu & Nooshin Yousefi & Dmitry Efimov, 2020. "Sequential Deep Learning for Credit Risk Monitoring with Tabular Financial Data," Papers 2012.15330, arXiv.org.
  41. Anastasios Petropoulos & Vasilis Siakoulis & Evaggelos Stavroulakis & Aristotelis Klamargias, 2019. "A robust machine learning approach for credit risk analysis of large loan-level datasets using deep learning and extreme gradient boosting," IFC Bulletins chapters, in: Bank for International Settlements (ed.), The use of big data analytics and artificial intelligence in central banking, volume 50, Bank for International Settlements.
  42. Golbayani, Parisa & Florescu, Ionuţ & Chatterjee, Rupak, 2020. "A comparative study of forecasting corporate credit ratings using neural networks, support vector machines, and decision trees," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
  43. Irving Fisher Committee, 2019. "The use of big data analytics and artificial intelligence in central banking," IFC Bulletins, Bank for International Settlements, number 50.
  44. Seyyide Doğan & Yasin Büyükkör & Murat Atan, 2022. "A comparative study of corporate credit ratings prediction with machine learning," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 32(1), pages 25-47.
  45. Lars Ole Hjelkrem & Petter Eilif de Lange, 2023. "Explaining Deep Learning Models for Credit Scoring with SHAP: A Case Study Using Open Banking Data," JRFM, MDPI, vol. 16(4), pages 1-19, April.
  46. K. S. Naik, 2021. "Predicting Credit Risk for Unsecured Lending: A Machine Learning Approach," Papers 2110.02206, arXiv.org.
  47. Yaseen Ghulam & Kamini Dhruva & Sana Naseem & Sophie Hill, 2018. "The Interaction of Borrower and Loan Characteristics in Predicting Risks of Subprime Automobile Loans," Risks, MDPI, vol. 6(3), pages 1-21, September.
  48. Lars Ole Hjelkrem & Petter Eilif de Lange & Erik Nesset, 2022. "The Value of Open Banking Data for Application Credit Scoring: Case Study of a Norwegian Bank," JRFM, MDPI, vol. 15(12), pages 1-15, December.
  49. Alexis Bogroff & Dominique Guegan, 2019. "Artificial Intelligence, Data, Ethics: An Holistic Approach for Risks and Regulation," Post-Print halshs-02181597, HAL.
  50. Li-Chen Cheng & Wei-Ting Lu & Benjamin Yeo, 2023. "Predicting abnormal trading behavior from internet rumor propagation: a machine learning approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-23, December.
  51. Amirhosein Mosavi & Yaser Faghan & Pedram Ghamisi & Puhong Duan & Sina Faizollahzadeh Ardabili & Ely Salwana & Shahab S. Band, 2020. "Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics," Mathematics, MDPI, vol. 8(10), pages 1-42, September.
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