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Recent developments in consumer credit risk assessment

Citations

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Cited by:

  1. Singh, Ramendra Pratap & Singh, Ramendra & Mishra, Prashant, 2021. "Does managing customer accounts receivable impact customer relationships, and sales performance? An empirical investigation," Journal of Retailing and Consumer Services, Elsevier, vol. 60(C).
  2. Jonathan K. Budd & Peter G. Taylor, 2015. "Calculating optimal limits for transacting credit card customers," Papers 1506.05376, arXiv.org, revised Aug 2015.
  3. Charitou, Andreas & Dionysiou, Dionysia & Lambertides, Neophytos & Trigeorgis, Lenos, 2013. "Alternative bankruptcy prediction models using option-pricing theory," Journal of Banking & Finance, Elsevier, vol. 37(7), pages 2329-2341.
  4. Anna Stelzer, 2019. "Predicting credit default probabilities using machine learning techniques in the face of unequal class distributions," Papers 1907.12996, arXiv.org.
  5. Nikita Kozodoi & Johannes Jacob & Stefan Lessmann, 2021. "Fairness in Credit Scoring: Assessment, Implementation and Profit Implications," Papers 2103.01907, arXiv.org, revised Jun 2022.
  6. Dimitris Andriosopoulos & Michalis Doumpos & Panos M. Pardalos & Constantin Zopounidis, 2019. "Computational approaches and data analytics in financial services: A literature review," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(10), pages 1581-1599, October.
  7. Zha, Yong & Wang, Yuting & Li, Quan & Yao, Wenying, 2022. "Credit offering strategy and dynamic pricing in the presence of consumer strategic behavior," European Journal of Operational Research, Elsevier, vol. 303(2), pages 753-766.
  8. Martin Rezac & Frantisek Rezac, 2011. "How to Measure the Quality of Credit Scoring Models," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 61(5), pages 486-507, November.
  9. Kriebel, Johannes & Stitz, Lennart, 2022. "Credit default prediction from user-generated text in peer-to-peer lending using deep learning," European Journal of Operational Research, Elsevier, vol. 302(1), pages 309-323.
  10. Bastos, Joao, 2007. "Credit scoring with boosted decision trees," MPRA Paper 8034, University Library of Munich, Germany.
  11. Tang, Xinyin & Feng, Chong & Zhu, Jianping & He, Minna, 2022. "How Can We Learn from Borrowers’ Online Behaviors? The Signal Effect of Borrowers’ Platform Involvement on Their Credit Risk," SocArXiv qga8j, Center for Open Science.
  12. Salihu, Armend & Shehu, Visar, 2020. "A Review of Algorithms for Credit Risk Analysis," Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference (2020), Virtual Conference, in: Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Virtual Conference, 10-12 September 2020, pages 134-146, IRENET - Society for Advancing Innovation and Research in Economy, Zagreb.
  13. Do, Hung Xuan & Rösch, Daniel & Scheule, Harald, 2018. "Predicting loss severities for residential mortgage loans: A three-step selection approach," European Journal of Operational Research, Elsevier, vol. 270(1), pages 246-259.
  14. Liu, Yi & Yang, Menglong & Wang, Yudong & Li, Yongshan & Xiong, Tiancheng & Li, Anzhe, 2022. "Applying machine learning algorithms to predict default probability in the online credit market: Evidence from China," International Review of Financial Analysis, Elsevier, vol. 79(C).
  15. Silvia Figini & Mario Maggi, 2014. "Performance of credit risk prediction models via proper loss functions," DEM Working Papers Series 064, University of Pavia, Department of Economics and Management.
  16. Katarzyna Stąpor & Tomasz Smolarczyk & Piotr Fabian, 2016. "Heteroscedastic Discriminant Analysis Combined With Feature Selection For Credit Scoring," Statistics in Transition New Series, Polish Statistical Association, vol. 17(2), pages 265-280, June.
  17. Medina-Olivares, Victor & Lindgren, Finn & Calabrese, Raffaella & Crook, Jonathan, 2023. "Joint models of multivariate longitudinal outcomes and discrete survival data with INLA: An application to credit repayment behaviour," European Journal of Operational Research, Elsevier, vol. 310(2), pages 860-873.
  18. Li, Aimin & Li, Zhiyong & Bellotti, Anthony, 2023. "Predicting loss given default of unsecured consumer loans with time-varying survival scores," Pacific-Basin Finance Journal, Elsevier, vol. 78(C).
  19. 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.
  20. Gregory Gadzinski & Alessio Castello, 2022. "Combining white box models, black box machines and human interventions for interpretable decision strategies," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 17(3), pages 598-627, May.
  21. Barbara CAVALLETTI & Corrado LAGAZIO & Daniela VANDONE, 2008. "Il credito al consumo in Italia: benessere economico o fragilita’ finanziaria?," Departmental Working Papers 2008-24, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
  22. Finlay, Steven, 2011. "Multiple classifier architectures and their application to credit risk assessment," European Journal of Operational Research, Elsevier, vol. 210(2), pages 368-378, April.
  23. Fernando A. F. Ferreira & Ieva Meidutė-Kavaliauskienė & Edmundas K. Zavadskas & Marjan S. Jalali & Sandra M. J. Catarino, 2019. "A Judgment-Based Risk Assessment Framework for Consumer Loans," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 7-33, January.
  24. Merike Kukk, 2019. "Debt repayment problems: short-term and long-term implications for spending," Review of Economics of the Household, Springer, vol. 17(2), pages 715-740, June.
  25. Joël Bessis, 2009. "Risk Management in Banking," Post-Print hal-00494876, HAL.
  26. 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.
  27. Luisa ANDERLONI & Daniela VANDONE, 2010. "The profitability of the consumer credit industry: evidence from Europe," Departmental Working Papers 2010-24, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
  28. Cheng Few Lee, 2020. "Financial econometrics, mathematics, statistics, and financial technology: an overall view," Review of Quantitative Finance and Accounting, Springer, vol. 54(4), pages 1529-1578, May.
  29. Li, Quan & Zha, Yong & Dong, Yu, 2023. "Subsidize or Not: The Competition of Credit Card and Online Credit in Platform-based Supply Chain System," European Journal of Operational Research, Elsevier, vol. 305(2), pages 644-658.
  30. Lobna Abid & Afif Masmoudi & Sonia Zouari-Ghorbel, 2018. "The Consumer Loan’s Payment Default Predictive Model: an Application of the Logistic Regression and the Discriminant Analysis in a Tunisian Commercial Bank," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 9(3), pages 948-962, September.
  31. Arno Botha & Conrad Beyers & Pieter de Villiers, 2019. "A procedure for loss-optimising default definitions across simulated credit risk scenarios," Papers 1907.12615, arXiv.org, revised Feb 2021.
  32. Haithem Awijen & Younes Ben Zaied & Ahmed Imran Hunjra, 2023. "Systematic and Unsystematic Determinants of Sectoral Risk Default Interconnectedness," Computational Economics, Springer;Society for Computational Economics, vol. 62(2), pages 561-587, August.
  33. Fout, Hamilton & Li, Grace & Palim, Mark & Pan, Ying, 2020. "Credit risk of low income mortgages," Regional Science and Urban Economics, Elsevier, vol. 80(C).
  34. Cuiqing Jiang & Zhao Wang & Ruiya Wang & Yong Ding, 2018. "Loan default prediction by combining soft information extracted from descriptive text in online peer-to-peer lending," Annals of Operations Research, Springer, vol. 266(1), pages 511-529, July.
  35. H-V Seow, 2010. "Question selection responding to information on customers from heterogeneous populations to select offers that maximize expected profit," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(3), pages 443-454, March.
  36. 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.
  37. Stąpor Katarzyna & Smolarczyk Tomasz & Fabian Piotr, 2016. "Heteroscedastic Discriminant Analysis Combined with Feature Selection for Credit Scoring," Statistics in Transition New Series, Polish Statistical Association, vol. 17(2), pages 265-280, June.
  38. Guotai Chi & Zhipeng Zhang, 2017. "Multi Criteria Credit Rating Model for Small Enterprise Using a Nonparametric Method," Sustainability, MDPI, vol. 9(10), pages 1-23, October.
  39. Finlay, Steven, 2010. "Credit scoring for profitability objectives," European Journal of Operational Research, Elsevier, vol. 202(2), pages 528-537, April.
  40. Mehmood, Mian Saqib & Sheraz, Iram & Mehmood, Asif & G. Mujtaba, Bahaudin, 2017. "Empirical Examination for Operational and Credit Risk Perspective – A Case of Commercial Banks of Pakistan," MPRA Paper 80491, University Library of Munich, Germany.
  41. Rasa Kanapickiene & Renatas Spicas, 2019. "Credit Risk Assessment Model for Small and Micro-Enterprises: The Case of Lithuania," Risks, MDPI, vol. 7(2), pages 1-23, June.
  42. Martin Řezáč, 2011. "Advanced empirical estimate of information value for credit scoring models," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 59(2), pages 267-274.
  43. Fernando A. F. Ferreira & Ronald W. Spahr & Irina F. M. D. Gavancha & Amali Çipi, 2013. "Readjusting trade-offs among criteria in internal ratings of credit-scoring: an empirical essay of risk analysis in mortgage loans," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 14(4), pages 715-740, September.
  44. Akkoç, Soner, 2012. "An empirical comparison of conventional techniques, neural networks and the three stage hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) model for credit scoring analysis: The case of Turkish cred," European Journal of Operational Research, Elsevier, vol. 222(1), pages 168-178.
  45. Maria Rocha Sousa & João Gama & Elísio Brandão, 2013. "Introducing time-changing economics into credit scoring," FEP Working Papers 513, Universidade do Porto, Faculdade de Economia do Porto.
  46. Trivedi, Shrawan Kumar, 2020. "A study on credit scoring modeling with different feature selection and machine learning approaches," Technology in Society, Elsevier, vol. 63(C).
  47. Jonathan Crook & Tony Bellotti, 2010. "Time varying and dynamic models for default risk in consumer loans," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(2), pages 283-305, April.
  48. Logan Ewanchuk & Christoph Frei, 2019. "Recent Regulation in Credit Risk Management: A Statistical Framework," Risks, MDPI, vol. 7(2), pages 1-19, April.
  49. Minati Rath & Hema Date, 2023. "Adaptive Modelling Approach for Row-Type Dependent Predictive Analysis (RTDPA): A Framework for Designing Machine Learning Models for Credit Risk Analysis in Banking Sector," Papers 2311.10799, arXiv.org.
  50. Fang, Fang & Chen, Yuanyuan, 2019. "A new approach for credit scoring by directly maximizing the Kolmogorov–Smirnov statistic," Computational Statistics & Data Analysis, Elsevier, vol. 133(C), pages 180-194.
  51. Lee, Yongwoong & Rösch, Daniel & Scheule, Harald, 2016. "Accuracy of mortgage portfolio risk forecasts during financial crises," European Journal of Operational Research, Elsevier, vol. 249(2), pages 440-456.
  52. A?da Kammoun & Imen Triki, 2016. "Credit Scoring Models for a Tunisian Microfinance Institution: Comparison between Artificial Neural Network and Logistic Regression," Review of Economics & Finance, Better Advances Press, Canada, vol. 6, pages 61-78, February.
  53. Dong-Her Shih & Ting-Wei Wu & Po-Yuan Shih & Nai-An Lu & Ming-Hung Shih, 2022. "A Framework of Global Credit-Scoring Modeling Using Outlier Detection and Machine Learning in a P2P Lending Platform," Mathematics, MDPI, vol. 10(13), pages 1-13, June.
  54. Angilella, Silvia & Mazzù, Sebastiano, 2015. "The financing of innovative SMEs: A multicriteria credit rating model," European Journal of Operational Research, Elsevier, vol. 244(2), pages 540-554.
  55. Piccoli, Pedro, 2022. "Valuating consumer credit portfolios," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 3(3).
  56. Stefan Lessmann & Stefan Voß, 2010. "Customer-Centric Decision Support," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 2(2), pages 79-93, April.
  57. José Willer Prado & Valderí Castro Alcântara & Francisval Melo Carvalho & Kelly Carvalho Vieira & Luiz Kennedy Cruz Machado & Dany Flávio Tonelli, 2016. "Multivariate analysis of credit risk and bankruptcy research data: a bibliometric study involving different knowledge fields (1968–2014)," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(3), pages 1007-1029, March.
  58. Singh, Shweta & Murthi, B.P.S. & Steffes, Erin, 2013. "Developing a measure of risk adjusted revenue (RAR) in credit cards market: Implications for customer relationship management," European Journal of Operational Research, Elsevier, vol. 224(2), pages 425-434.
  59. Hofer, Vera, 2015. "Adapting a classification rule to local and global shift when only unlabelled data are available," European Journal of Operational Research, Elsevier, vol. 243(1), pages 177-189.
  60. Lee, Yongwoong & Poon, Ser-Huang, 2014. "Forecasting and decomposition of portfolio credit risk using macroeconomic and frailty factors," Journal of Economic Dynamics and Control, Elsevier, vol. 41(C), pages 69-92.
  61. 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.
  62. Seifert, Daniel & Seifert, Ralf W. & Protopappa-Sieke, Margarita, 2013. "A review of trade credit literature: Opportunities for research in operations," European Journal of Operational Research, Elsevier, vol. 231(2), pages 245-256.
  63. Eid, Nourhan & Maltby, Josephine & Talavera, Oleksandr, 2016. "Income Rounding and Loan Performance in the Peer-to-Peer Market," MPRA Paper 72852, University Library of Munich, Germany.
  64. K Rajaratnam & P Beling & G Overstreet, 2010. "Scoring decisions in the context of economic uncertainty," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(3), pages 421-429, March.
  65. Tomasz Smolarczyk & Katarzyna Stąpor & Piotr Fabian, 2016. "Heteroscedastic Discriminant Analysis Combined With Feature Selection For Credit Scoring," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 17(2), pages 265-280, June.
  66. Bellotti, Tony & Mues, Christophe, 2016. "EditorialAuthor-Name: Crook, Jonathan," European Journal of Operational Research, Elsevier, vol. 249(2), pages 395-396.
  67. Raffaella Calabrese, 2012. "Improving Classifier Performance Assessment of Credit Scoring Models," Working Papers 201204, Geary Institute, University College Dublin.
  68. Wang, Kai & Zhao, Ruiqing & Peng, Jin, 2018. "Trade credit contracting under asymmetric credit default risk: Screening, checking or insurance," European Journal of Operational Research, Elsevier, vol. 266(2), pages 554-568.
  69. Raffaella Calabrese & Galina Andreeva & Jake Ansell, 2019. "“Birds of a Feather” Fail Together: Exploring the Nature of Dependency in SME Defaults," Risk Analysis, John Wiley & Sons, vol. 39(1), pages 71-84, January.
  70. Vasilios Giannopoulos & Eleftherios Aggelopoulos, 2019. "Predicting SME loan delinquencies during recession using accounting data and SME characteristics: The case of Greece," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 26(2), pages 71-82, April.
  71. Andrew R. Sanderford & George A. Overstreet & Peter A. Beling & Kanshukan Rajaratnam, 2015. "Energy-efficient homes and mortgage risk: crossing the chasm at last?," Environment Systems and Decisions, Springer, vol. 35(1), pages 157-168, March.
  72. Barbara Cavalletti & Corrado Lagazio & Elena Lagomarsino & Daniela Vandone, 2020. "Consumer Debt and Financial Fragility: Evidence from Italy," Journal of Consumer Policy, Springer, vol. 43(4), pages 747-765, December.
  73. Naveed Chehrazi & Thomas A. Weber, 2015. "Dynamic Valuation of Delinquent Credit-Card Accounts," Management Science, INFORMS, vol. 61(12), pages 3077-3096, December.
  74. Lützenkirchen, Kristina & Rösch, Daniel & Scheule, Harald, 2014. "Asset portfolio securitizations and cyclicality of regulatory capital," European Journal of Operational Research, Elsevier, vol. 237(1), pages 289-302.
  75. Lai, Wan-Ni, 2016. "Evaluating the sovereign and household credit risk in Singapore: A contingent claims approach," Research in International Business and Finance, Elsevier, vol. 37(C), pages 435-447.
  76. Huseyin Ince & Bora Aktan, 2009. "A comparison of data mining techniques for credit scoring in banking: A managerial perspective," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 10(3), pages 233-240, March.
  77. Lessmann, Stefan & Baesens, Bart & Seow, Hsin-Vonn & Thomas, Lyn C., 2015. "Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research," European Journal of Operational Research, Elsevier, vol. 247(1), pages 124-136.
  78. Martin Řezáč, 2015. "ESIS2: Information Value Estimator for Credit Scoring Models," Computational Economics, Springer;Society for Computational Economics, vol. 45(2), pages 303-322, February.
  79. 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".
  80. Surjaningsih, Ndari & Werdaningtyas, Hesti & Rahman, Faizal & Falaqh, Romadhon, 2022. "Predicting Household Resilience Before and During Pandemic with Classifier Algorithms," OSF Preprints w5q9g, Center for Open Science.
  81. P Beling & G Overstreet & K Rajaratnam, 2010. "Estimation error in regulatory capital requirements: theoretical implications for consumer bank profitability," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(3), pages 381-392, March.
  82. Lessmann, Stefan & Voß, Stefan, 2009. "A reference model for customer-centric data mining with support vector machines," European Journal of Operational Research, Elsevier, vol. 199(2), pages 520-530, December.
  83. Marcello Pagnini & Paola Rossi & Valerio Vacca & Lucia dalla Pellegrina & Serena Frazzoni & Zeno Rotondi & Andrea Vezzulli, 2017. "Access to Credit for Small Innovative Businesses," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 46(3), pages 411-458, November.
  84. Ekaterina V. Orlova, 2021. "Methodology and Models for Individuals’ Creditworthiness Management Using Digital Footprint Data and Machine Learning Methods," Mathematics, MDPI, vol. 9(15), pages 1-28, August.
  85. Kozodoi, Nikita & Jacob, Johannes & Lessmann, Stefan, 2022. "Fairness in credit scoring: Assessment, implementation and profit implications," European Journal of Operational Research, Elsevier, vol. 297(3), pages 1083-1094.
  86. Shoghi , Amirhossein, 2019. "Debt Collection Industry: Machine Learning Approach," Journal of Money and Economy, Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, vol. 14(4), pages 453-473, October.
  87. 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.
  88. Weidong Guo & Zach Zhizhong Zhou, 2022. "A comparative study of combining tree‐based feature selection methods and classifiers in personal loan default prediction," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1248-1313, September.
  89. Arno Botha & Conrad Beyers & Pieter de Villiers, 2020. "Simulation-based optimisation of the timing of loan recovery across different portfolios," Papers 2009.11064, arXiv.org, revised Apr 2021.
  90. Crone, Sven F. & Finlay, Steven, 2012. "Instance sampling in credit scoring: An empirical study of sample size and balancing," International Journal of Forecasting, Elsevier, vol. 28(1), pages 224-238.
  91. Michael Bucker & Gero Szepannek & Alicja Gosiewska & Przemyslaw Biecek, 2020. "Transparency, Auditability and eXplainability of Machine Learning Models in Credit Scoring," Papers 2009.13384, arXiv.org.
  92. Wolter, Marcus & Rösch, Daniel, 2014. "Cure events in default prediction," European Journal of Operational Research, Elsevier, vol. 238(3), pages 846-857.
  93. João A. Bastos, 2022. "Predicting Credit Scores with Boosted Decision Trees," Forecasting, MDPI, vol. 4(4), pages 1-11, November.
  94. Tomáš Vaněk, 2016. "Economic Adjustment of Default Probabilities," European Journal of Business Science and Technology, Mendel University in Brno, Faculty of Business and Economics, vol. 2(2), pages 122-130.
  95. repec:syb:wpbsba:03/2013 is not listed on IDEAS
  96. Liu, Fan & Hua, Zhongsheng & Lim, Andrew, 2015. "Identifying future defaulters: A hierarchical Bayesian method," European Journal of Operational Research, Elsevier, vol. 241(1), pages 202-211.
  97. Dendramis, Y. & Tzavalis, E. & Adraktas, G., 2018. "Credit risk modelling under recessionary and financially distressed conditions," Journal of Banking & Finance, Elsevier, vol. 91(C), pages 160-175.
  98. Tomáš Vaněk & David Hampel, 2017. "The Probability of Default Under IFRS 9: Multi-period Estimation and Macroeconomic Forecast," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 65(2), pages 759-776.
  99. Hussein A. Abdou & John Pointon, 2011. "Credit Scoring, Statistical Techniques And Evaluation Criteria: A Review Of The Literature," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 18(2-3), pages 59-88, April.
  100. Barbara Cavalletti & Corrado Lagazio & Daniela Vandone & Elena Lagomarsino, 2012. "The role of financial position on consumer indebted-ness. An empirical analysis in Italy," DEP - series of economic working papers 8/2012, University of Genoa, Research Doctorate in Public Economics.
  101. Shuang Zhu & R. Pace, 2014. "Modeling Spatially Interdependent Mortgage Decisions," The Journal of Real Estate Finance and Economics, Springer, vol. 49(4), pages 598-620, November.
  102. Naveed Chehrazi & Peter W. Glynn & Thomas A. Weber, 2019. "Dynamic Credit-Collections Optimization," Management Science, INFORMS, vol. 67(6), pages 2737-2769, June.
  103. repec:cup:judgdm:v:17:y:2022:i:3:p:598-627 is not listed on IDEAS
  104. Rasa Kanapickienė & Tomas Kanapickas & Audrius Nečiūnas, 2023. "Bankruptcy Prediction for Micro and Small Enterprises Using Financial, Non-Financial, Business Sector and Macroeconomic Variables: The Case of the Lithuanian Construction Sector," Risks, MDPI, vol. 11(5), pages 1-33, May.
  105. Jun-ya Gotoh & Akiko Takeda & Rei Yamamoto, 2014. "Interaction between financial risk measures and machine learning methods," Computational Management Science, Springer, vol. 11(4), pages 365-402, October.
  106. Fitzpatrick, Trevor & Mues, Christophe, 2016. "An empirical comparison of classification algorithms for mortgage default prediction: evidence from a distressed mortgage market," European Journal of Operational Research, Elsevier, vol. 249(2), pages 427-439.
  107. L C Thomas, 2010. "Consumer finance: challenges for operational research," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(1), pages 41-52, January.
  108. He, Ping & Hua, Zhongsheng & Liu, Zhixin, 2015. "A quantification method for the collection effect on consumer term loans," Journal of Banking & Finance, Elsevier, vol. 57(C), pages 17-26.
  109. Tong, Edward N.C. & Mues, Christophe & Thomas, Lyn C., 2012. "Mixture cure models in credit scoring: If and when borrowers default," European Journal of Operational Research, Elsevier, vol. 218(1), pages 132-139.
  110. Ming-Chin Hung & Yung-Kang Ching & Shih-Kuei Lin, 2021. "Impact of COVID-19 on the Robustness of the Probability of Default Estimation Model," Mathematics, MDPI, vol. 9(23), pages 1-13, November.
  111. Pierluigi Bologna & Maddalena Galardo, 2022. "Calibrating the countercyclical capital buffer for Italy," Questioni di Economia e Finanza (Occasional Papers) 679, Bank of Italy, Economic Research and International Relations Area.
  112. Yu Xia & Ta Xu & Ming-Xia Wei & Zhen-Ke Wei & Lian-Jie Tang, 2023. "Predicting Chain’s Manufacturing SME Credit Risk in Supply Chain Finance Based on Machine Learning Methods," Sustainability, MDPI, vol. 15(2), pages 1-18, January.
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