IDEAS home Printed from https://ideas.repec.org/r/nbr/nberwo/21305.html
   My bibliography  Save this item

Risk and Risk Management in the Credit Card Industry

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Carl Remlinger & Bri`ere Marie & Alasseur Cl'emence & Joseph Mikael, 2021. "Expert Aggregation for Financial Forecasting," Papers 2111.15365, arXiv.org, revised Jul 2023.
  2. Wosnitza, Jan Henrik, 2022. "Calibration alternatives to logistic regression and their potential for transferring the dispersion of discriminatory power into uncertainties of probabilities of default," Discussion Papers 04/2022, Deutsche Bundesbank.
  3. Costello, Anna M. & Down, Andrea K. & Mehta, Mihir N., 2020. "Machine + man: A field experiment on the role of discretion in augmenting AI-based lending models," Journal of Accounting and Economics, Elsevier, vol. 70(2).
  4. Victor DeMiguel & Javier Gil-Bazo & Francisco J. Nogales & André A. P. Santos, 2021. "Can Machine Learning Help to Select Portfolios of Mutual Funds?," Working Papers 1245, Barcelona School of Economics.
  5. Chi Ming Chen & Geoffrey Kwok Fai Tso & Kaijian He, 2024. "Quantum Optimized Cost Based Feature Selection and Credit Scoring for Mobile Micro-financing," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 919-950, February.
  6. Dominique Guegan & Peter Martey Addo & Bertrand Hassani, 2018. "Credit Risk Analysis Using Machine and Deep Learning Models," Post-Print halshs-01835164, HAL.
  7. Yan Zhang & Peter Trubey, 2019. "Machine Learning and Sampling Scheme: An Empirical Study of Money Laundering Detection," Computational Economics, Springer;Society for Computational Economics, vol. 54(3), pages 1043-1063, October.
  8. Yuan Gao & Biao Jiang & Jietong Zhou, 2023. "Financial Distress Prediction For Small And Medium Enterprises Using Machine Learning Techniques," Papers 2302.12118, arXiv.org.
  9. Nicola Branzoli & Ilaria Supino, 2020. "FinTech credit: a critical review of empirical research," Questioni di Economia e Finanza (Occasional Papers) 549, Bank of Italy, Economic Research and International Relations Area.
  10. Tsuji, Chikashi, 2020. "Correlation and spillover effects between the US and international banking sectors: New evidence and implications for risk management," International Review of Financial Analysis, Elsevier, vol. 70(C).
  11. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
  12. Li, Jing-Ping & Mirza, Nawazish & Rahat, Birjees & Xiong, Deping, 2020. "Machine learning and credit ratings prediction in the age of fourth industrial revolution," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
  13. 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.
  14. Nakagawa, Kei & Sakemoto, Ryuta, 2022. "Cryptocurrency network factors and gold," Finance Research Letters, Elsevier, vol. 46(PB).
  15. Acheampong, Albert & Elshandidy, Tamer, 2021. "Does soft information determine credit risk? Text-based evidence from European banks," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 75(C).
  16. 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.
  17. Haskamp, Ulrich, 2017. "Improving the forecasts of European regional banks' profitability with machine learning algorithms," Ruhr Economic Papers 705, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
  18. García-Céspedes, Rubén & Moreno, Manuel, 2022. "The generalized Vasicek credit risk model: A Machine Learning approach," Finance Research Letters, Elsevier, vol. 47(PA).
  19. Doumpos, Michalis & Zopounidis, Constantin & Gounopoulos, Dimitrios & Platanakis, Emmanouil & Zhang, Wenke, 2023. "Operational research and artificial intelligence methods in banking," European Journal of Operational Research, Elsevier, vol. 306(1), pages 1-16.
  20. 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.
  21. Eamon Duede & Victor Zhorin, 2016. "Convergence of Economic Growth and the Great Recession as Seen From a Celestial Observatory," Papers 1604.04312, arXiv.org, revised Aug 2016.
  22. Henri Fraisse & Matthias Laporte, 2021. "Return on Investment on AI: The Case of Capital Requirement," Working papers 809, Banque de France.
  23. Alois Weigand, 2019. "Machine learning in empirical asset pricing," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 33(1), pages 93-104, March.
  24. 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.
  25. Peter Martey Addo & Dominique Guegan & Bertrand Hassani, 2018. "Credit Risk Analysis Using Machine and Deep Learning Models," Risks, MDPI, vol. 6(2), pages 1-20, April.
  26. Jan Svanberg & Tohid Ardeshiri & Isak Samsten & Peter Öhman & Presha E. Neidermeyer & Tarek Rana & Natalia Semenova & Mats Danielson, 2022. "Corporate governance performance ratings with machine learning," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(1), pages 50-68, January.
  27. Alonso-Robisco, Andrés & Carbó, José Manuel, 2022. "Can machine learning models save capital for banks? Evidence from a Spanish credit portfolio," International Review of Financial Analysis, Elsevier, vol. 84(C).
  28. Oleksandr Melnychenko, 2020. "Is Artificial Intelligence Ready to Assess an Enterprise’s Financial Security?," JRFM, MDPI, vol. 13(9), pages 1-19, August.
  29. Justin Sirignano & Apaar Sadhwani & Kay Giesecke, 2016. "Deep Learning for Mortgage Risk," Papers 1607.02470, arXiv.org, revised Mar 2018.
  30. Pan, Shuiyang & Long, Suwan(Cheng) & Wang, Yiming & Xie, Ying, 2023. "Nonlinear asset pricing in Chinese stock market: A deep learning approach," International Review of Financial Analysis, Elsevier, vol. 87(C).
  31. Ting Sun & Miklos A. Vasarhelyi, 2018. "Predicting credit card delinquencies: An application of deep neural networks," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 25(4), pages 174-189, October.
  32. Philip Ndikum, 2020. "Machine Learning Algorithms for Financial Asset Price Forecasting," Papers 2004.01504, arXiv.org.
  33. Königstorfer, Florian & Thalmann, Stefan, 2020. "Applications of Artificial Intelligence in commercial banks – A research agenda for behavioral finance," Journal of Behavioral and Experimental Finance, Elsevier, vol. 27(C).
  34. Fitzpatrick, Trevor & Mues, Christophe, 2021. "How can lenders prosper? Comparing machine learning approaches to identify profitable peer-to-peer loan investments," European Journal of Operational Research, Elsevier, vol. 294(2), pages 711-722.
  35. 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.
  36. Andrés Alonso & José Manuel Carbó, 2021. "Understanding the performance of machine learning models to predict credit default: a novel approach for supervisory evaluation," Working Papers 2105, Banco de España.
  37. Richard Chamboko & Jorge Miguel Bravo, 2020. "A Multi-State Approach to Modelling Intermediate Events and Multiple Mortgage Loan Outcomes," Risks, MDPI, vol. 8(2), pages 1-29, June.
  38. Krivorotov, George, 2023. "Machine learning-based profit modeling for credit card underwriting - implications for credit risk," Journal of Banking & Finance, Elsevier, vol. 149(C).
  39. Jianhua Jiang & Xianqiu Meng & Yang Liu & Huan Wang, 2022. "An Enhanced TSA-MLP Model for Identifying Credit Default Problems," SAGE Open, , vol. 12(2), pages 21582440221, April.
  40. Jian Huang & Junyi Chai & Stella Cho, 2020. "Deep learning in finance and banking: A literature review and classification," Frontiers of Business Research in China, Springer, vol. 14(1), pages 1-24, December.
  41. Uddin, Godwin, 2021. "Financial system regulation in a pandemic: Evidence from Nigeria," MPRA Paper 108052, University Library of Munich, Germany.
  42. Chen, Dangxing & Ye, Jiahui & Ye, Weicheng, 2023. "Interpretable selective learning in credit risk," Research in International Business and Finance, Elsevier, vol. 65(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. Nguyen, Quyen & Diaz-Rainey, Ivan & Kuruppuarachchi, Duminda, 2021. "Predicting corporate carbon footprints for climate finance risk analyses: A machine learning approach," Energy Economics, Elsevier, vol. 95(C).
  45. Dominique Guegan & Peter Martey Addo & Bertrand Hassani, 2018. "Credit Risk Analysis Using Machine and Deep Learning Models," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01835164, HAL.
  46. Runshan Fu & Yan Huang & Param Vir Singh, 2020. "Crowd, Lending, Machine, and Bias," Papers 2008.04068, arXiv.org.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.