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Stijn Viaene

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

    Sorry, no citations of working papers recorded.

Articles

  1. Philip Rogiers & Stijn Viaene & Jan Leysen, 2020. "The digital future of internal staffing: A vision for transformational electronic human resource management," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 27(4), pages 182-196, October.

    Cited by:

    1. Nur Muhammaditya & Sudarsono Hardjosoekarto & One Herwantoko & Yulia Gita Fany & Mahari Is Subangun, 2022. "Institutional Divergence of Digital Item Bank Management in Bureaucratic Hybridization: An Application of SSM Based Multi-Method," Systemic Practice and Action Research, Springer, vol. 35(4), pages 527-553, August.

  2. Mertens, Willem & Recker, Jan & Kummer, Tyge-F. & Kohlborn, Thomas & Viaene, Stijn, 2016. "Constructive deviance as a driver for performance in retail," Journal of Retailing and Consumer Services, Elsevier, vol. 30(C), pages 193-203.

    Cited by:

    1. Ulrich Matthias König & Alexander Linhart & Maximilian Röglinger, 2019. "Why do business processes deviate? Results from a Delphi study," Business Research, Springer;German Academic Association for Business Research, vol. 12(2), pages 425-453, December.
    2. Mortimer, Gary & Fazal-e-Hasan, Syed Muhammad & Strebel, Judi, 2021. "Examining the consequences of customer-oriented deviance in retail," Journal of Retailing and Consumer Services, Elsevier, vol. 58(C).
    3. Hao Ji & Jin Yan, 2023. "Why does counterproductive work behavior lead to pro-social rule breaking? The roles of impression management motives and leader-liking," Asia Pacific Journal of Management, Springer, vol. 40(4), pages 1323-1339, December.
    4. Mertens, Willem & Recker, Jan, 2020. "How store managers can empower their teams to engage in constructive deviance: Theory development through a multiple case study," Journal of Retailing and Consumer Services, Elsevier, vol. 52(C).
    5. Tekmen, Esra Erenler & Kaptangil, Kerem, 2022. "The Determinants of Constructive Deviant Behaviour of Frontline Tourism Employees: An Exploration with Perceived Supervisory Support and Intrinsic Motivation," Journal of Tourism, Sustainability and Well-being, Cinturs - Research Centre for Tourism, Sustainability and Well-being, University of Algarve, vol. 10(1), pages 58-74.
    6. Yanyan Lv & Xiaoguang Liu & Guomin Li & Yongrok Choi, 2020. "Managerial Pro-Social Rule Breaking in the Chinese Organizational Context: Conceptualization, Scale Development, and Double-Edged Sword Effect on Employees’ Sustainable Organizational Identification," Sustainability, MDPI, vol. 12(17), pages 1-23, August.
    7. Syed Muhammad Fazal-e-Hasan & Gary Mortimer & Ian Lings & Harjit Sekhon & Kerry Howell, 2021. "Managing Relationships: Insights from a Student Gratitude Model," Research in Higher Education, Springer;Association for Institutional Research, vol. 62(1), pages 98-119, February.
    8. Tierney, Kieran D. & Oswald Karpen, Ingo & Westberg, Kate, 2022. "Brand meaning and institutional work: The light and dark sides of service employee practices," Journal of Business Research, Elsevier, vol. 151(C), pages 244-256.
    9. Mertens, Willem & Recker, Jan, 2020. "Can constructive deviance be empowered? A multi-level field study in Australian supermarkets," Journal of Retailing and Consumer Services, Elsevier, vol. 54(C).

  3. Viaene, Stijn & Danneels, Lieselot, 2015. "Driving digital: welcome to the ExConomy," Journal of Financial Perspectives, EY Global FS Institute, vol. 3(3), pages 182-187.

    Cited by:

    1. Lieselot Danneels & Stijn Viaene, 2022. "Identifying Digital Transformation Paradoxes," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 64(4), pages 483-500, August.

  4. Viaene, Stijn & Ayuso, Mercedes & Guillen, Montserrat & Van Gheel, Dirk & Dedene, Guido, 2007. "Strategies for detecting fraudulent claims in the automobile insurance industry," European Journal of Operational Research, Elsevier, vol. 176(1), pages 565-583, January.

    Cited by:

    1. Mercedes Ayuso(universitat de Barcelona) & Miguel Santolino(Universitat de Barcelona), 2009. "Individual prediction of automobile bodily injury claims liabilities," Working Papers in Economics 220, Universitat de Barcelona. Espai de Recerca en Economia.
    2. Yiting Xing & Ling Li & Zhuming Bi & Marzena Wilamowska‐Korsak & Li Zhang, 2013. "Operations Research (OR) in Service Industries: A Comprehensive Review," Systems Research and Behavioral Science, Wiley Blackwell, vol. 30(3), pages 300-353, May.
    3. Urbina, Jilber & Guillén, Montserrat, 2013. "An application of capital allocation principles to operational risk," MPRA Paper 75726, University Library of Munich, Germany, revised Dec 2013.
    4. Yankol-Schalck, Meryem, 2022. "The value of cross-data set analysis for automobile insurance fraud detection," Research in International Business and Finance, Elsevier, vol. 63(C).
    5. Samuel Antwi & Xicang Zhao, 2012. "National Health Insurance; Claims; Logistic Regression;Odds Ratio; Ghana," International Journal of Business and Social Research, LAR Center Press, vol. 2(7), pages 139-147, December.
    6. Galeotti, Marcello & Rabitti, Giovanni & Vannucci, Emanuele, 2020. "An evolutionary approach to fraud management," European Journal of Operational Research, Elsevier, vol. 284(3), pages 1167-1177.
    7. Bermúdez, Ll. & Pérez, J.M. & Ayuso, M. & Gómez, E. & Vázquez, F.J., 2008. "A Bayesian dichotomous model with asymmetric link for fraud in insurance," Insurance: Mathematics and Economics, Elsevier, vol. 42(2), pages 779-786, April.
    8. Mercedes Ayuso & Miguel Santolino, 2008. "Forecasting the maximum compensation offer in the automobile BI claims negotiation proces," IREA Working Papers 200807, University of Barcelona, Research Institute of Applied Economics, revised May 2008.
    9. Denisa BANULESCU-RADU & Meryem YANKOL-SCHALCK, 2021. "Fraud detection in the era of Machine Learning: a household insurance case," LEO Working Papers / DR LEO 2904, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    10. Wang, Xiaofang & Zhuang, Jun, 2011. "Balancing congestion and security in the presence of strategic applicants with private information," European Journal of Operational Research, Elsevier, vol. 212(1), pages 100-111, July.
    11. Haupt, Johannes & Bender, Benedict & Fabian, Benjamin & Lessmann, Stefan, 2018. "Robust identification of email tracking: A machine learning approach," European Journal of Operational Research, Elsevier, vol. 271(1), pages 341-356.
    12. Jing Ai & Patrick L. Brockett & Linda L. Golden & Montserrat Guillén, 2013. "A Robust Unsupervised Method for Fraud Rate Estimation," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 80(1), pages 121-143, March.
    13. Ming-Jyh Wang & Chieh-Hua Wen & Lawrence W Lan, 2010. "Modelling Different Types of Bundled Automobile Insurance Choice Behaviour: The Case of Taiwan*," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 35(2), pages 290-308, April.
    14. 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.
    15. Samuel Antwi & Xicang Zhao, 2012. "National Health Insurance; Claims; Logistic Regression;Odds Ratio; Ghana," International Journal of Business and Social Research, MIR Center for Socio-Economic Research, vol. 2(7), pages 139-147, December.

  5. Viaene, Stijn & Dedene, Guido, 2005. "Cost-sensitive learning and decision making revisited," European Journal of Operational Research, Elsevier, vol. 166(1), pages 212-220, October.

    Cited by:

    1. 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).
    2. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.
    3. Elena Ivona DUMITRESCU & Sullivan HUE & Christophe HURLIN & Sessi TOKPAVI, 2020. "Machine Learning or Econometrics for Credit Scoring: Let’s Get the Best of Both Worlds," LEO Working Papers / DR LEO 2839, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    4. Perera, H. Niles & Hurley, Jason & Fahimnia, Behnam & Reisi, Mohsen, 2019. "The human factor in supply chain forecasting: A systematic review," European Journal of Operational Research, Elsevier, vol. 274(2), pages 574-600.
    5. De Bock, Koen W. & Coussement, Kristof & Lessmann, Stefan, 2020. "Cost-sensitive business failure prediction when misclassification costs are uncertain: A heterogeneous ensemble selection approach," European Journal of Operational Research, Elsevier, vol. 285(2), pages 612-630.
    6. Glady, Nicolas & Baesens, Bart & Croux, Christophe, 2009. "Modeling churn using customer lifetime value," European Journal of Operational Research, Elsevier, vol. 197(1), pages 402-411, August.
    7. Haupt, Johannes & Bender, Benedict & Fabian, Benjamin & Lessmann, Stefan, 2018. "Robust identification of email tracking: A machine learning approach," European Journal of Operational Research, Elsevier, vol. 271(1), pages 341-356.
    8. 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.
    9. Koen W. de Bock & Kristof Coussement & Stefan Lessmann, 2020. "Cost-sensitive business failure prediction when misclassification costs are uncertain: A heterogeneous ensemble selection approach," Post-Print hal-02863245, HAL.

  6. Stijn Viaene & Guido Dedene, 2004. "Insurance Fraud: Issues and Challenges," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 29(2), pages 313-333, April.

    Cited by:

    1. Baumberg, Ben, 2016. "Benefit `myths'? The accuracy and inaccuracy of public beliefs about the benefits system," LSE Research Online Documents on Economics 103512, London School of Economics and Political Science, LSE Library.
    2. Sungkwol Park & Xiaoyong Zheng & Roderick M. Rejesus & Barry K. Goodwin, 2022. "Somebody's watching me! Impacts of the spot check list program in U.S. crop insurance," American Journal of Agricultural Economics, John Wiley & Sons, vol. 104(3), pages 921-946, May.
    3. Engström, Per & Hesselius, Patrik, 2007. "The information method - theory and application," Working Paper Series 2007:17, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    4. Nils Mahlow & Joël Wagner, 2016. "Evolution of Strategic Levers in Insurance Claims Management: An Industry Survey," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 19(2), pages 197-223, September.
    5. Viaene, Stijn & Ayuso, Mercedes & Guillen, Montserrat & Van Gheel, Dirk & Dedene, Guido, 2007. "Strategies for detecting fraudulent claims in the automobile insurance industry," European Journal of Operational Research, Elsevier, vol. 176(1), pages 565-583, January.
    6. Lu-Ming Tseng & Yue-Min Kang, 2015. "Managerial Authority, Turnover Intention and Medical Insurance Claims Adjusters’ Recommendations for Claim Payments," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 40(2), pages 334-352, April.
    7. Renee Flasher & Melvin A. Lamboy-Ruiz, 2019. "Impact of Enforcement on Healthcare Billing Fraud: Evidence from the USA," Journal of Business Ethics, Springer, vol. 157(1), pages 217-229, June.
    8. Pierre Picard, 2012. "Economic Analysis of Insurance Fraud," Working Papers hal-00725561, HAL.
    9. Lu-Ming Tseng & Yue-Min Kang, 2014. "The influences of sales compensations, management stringency and ethical evaluations on product recommendations made by insurance brokers," Journal of Financial Regulation and Compliance, Emerald Group Publishing Limited, vol. 22(1), pages 26-42, February.
    10. Galeotti, Marcello & Rabitti, Giovanni & Vannucci, Emanuele, 2020. "An evolutionary approach to fraud management," European Journal of Operational Research, Elsevier, vol. 284(3), pages 1167-1177.
    11. Reurink, Arjan, 2016. "Financial fraud: A literature review," MPIfG Discussion Paper 16/5, Max Planck Institute for the Study of Societies.
    12. Emmanuel Laffort & Nicolas Dufour, 2021. "Prise en compte de la fraude dans les organisations : comment libérer la parole ?," Post-Print hal-03336041, HAL.
    13. Haithem Zourrig & Jeongsoo Park, 2019. "The effects of cultural tightness and perceived unfairness on Japanese consumers’ attitude towards insurance fraud: the mediating effect of rationalization," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 24(1), pages 21-30, June.
    14. Jing Ai & Patrick L. Brockett & Linda L. Golden & Montserrat Guillén, 2013. "A Robust Unsupervised Method for Fraud Rate Estimation," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 80(1), pages 121-143, March.
    15. Ming-Jyh Wang & Chieh-Hua Wen & Lawrence W Lan, 2010. "Modelling Different Types of Bundled Automobile Insurance Choice Behaviour: The Case of Taiwan*," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 35(2), pages 290-308, April.
    16. Jill M. Bisco & Kathleen A. McCullough & Charles M. Nyce, 2019. "Postclaim Underwriting And The Verification Of Insured Information: Evidence From The Life Insurance Industry," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 86(1), pages 7-38, March.
    17. Warren, Danielle E. & Schweitzer, Maurice E., 2021. "When weak sanctioning systems work: Evidence from auto insurance industry fraud investigations," Organizational Behavior and Human Decision Processes, Elsevier, vol. 166(C), pages 68-83.
    18. Lu-Ming Tseng & Wen-Pin Su, 2014. "Insurance Salespeople's Attitudes towards Collusion: The Case of Taiwan’s Car Insurance Industry," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 39(1), pages 25-41, January.
    19. Arash Rashidian & Hossein Joudaki & Taryn Vian, 2012. "No Evidence of the Effect of the Interventions to Combat Health Care Fraud and Abuse: A Systematic Review of Literature," PLOS ONE, Public Library of Science, vol. 7(8), pages 1-8, August.
    20. Emmanuel Laffort & Nicolas Dufour, 2020. "External Fraud Risk Management seen from Luhmann’s Systemic Perspective and a Tentative Reading of Healthcare Insurance Companies’ Measures through this Perspective," Post-Print hal-03336033, HAL.

  7. B Baesens & T Van Gestel & S Viaene & M Stepanova & J Suykens & J Vanthienen, 2003. "Benchmarking state-of-the-art classification algorithms for credit scoring," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(6), pages 627-635, June.

    Cited by:

    1. Dangxing Chen & Weicheng Ye & Jiahui Ye, 2022. "Interpretable Selective Learning in Credit Risk," Papers 2209.10127, arXiv.org.
    2. Ali Namaki & Reza Eyvazloo & Shahin Ramtinnia, 2023. "A systematic review of early warning systems in finance," Papers 2310.00490, arXiv.org.
    3. Kaposty, Florian & Kriebel, Johannes & Löderbusch, Matthias, 2020. "Predicting loss given default in leasing: A closer look at models and variable selection," International Journal of Forecasting, Elsevier, vol. 36(2), pages 248-266.
    4. T Bellotti & J Crook, 2009. "Credit scoring with macroeconomic variables using survival analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(12), pages 1699-1707, December.
    5. Anna Stelzer, 2019. "Predicting credit default probabilities using machine learning techniques in the face of unequal class distributions," Papers 1907.12996, arXiv.org.
    6. Hoffmann, F. & Baesens, B. & Mues, C. & Van Gestel, T. & Vanthienen, J., 2007. "Inferring descriptive and approximate fuzzy rules for credit scoring using evolutionary algorithms," European Journal of Operational Research, Elsevier, vol. 177(1), pages 540-555, February.
    7. G Verstraeten & D Van den Poel, 2005. "The impact of sample bias on consumer credit scoring performance and profitability," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(8), pages 981-992, August.
    8. A. Prinzie & D. Van Den Poel, 2005. "Constrained optimization of data-mining problems to improve model performance: A direct-marketing application," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/298, Ghent University, Faculty of Economics and Business Administration.
    9. Emmanuel Flachaire & Gilles Hacheme & Sullivan Hu'e & S'ebastien Laurent, 2022. "GAM(L)A: An econometric model for interpretable Machine Learning," Papers 2203.11691, arXiv.org.
    10. Mark Schreiner, 2015. "A Comparison of Two Simple, Low-Cost Ways for Local, Pro-Poor Organizations to Measure the Poverty of Their Participants," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 124(2), pages 537-569, November.
    11. Bravo, Cristián & Maldonado, Sebastián & Weber, Richard, 2013. "Granting and managing loans for micro-entrepreneurs: New developments and practical experiences," European Journal of Operational Research, Elsevier, vol. 227(2), pages 358-366.
    12. B. Baesens & T. Van Gestel & M. Stepanova & D. Van Den Poel, 2004. "Neural Network Survival Analysis for Personal Loan Data," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/281, Ghent University, Faculty of Economics and Business Administration.
    13. Rais Ahmad Itoo & A. Selvarasu & José António Filipe, 2015. "Loan Products and Credit Scoring by Commercial Banks (India)," International Journal of Finance, Insurance and Risk Management, International Journal of Finance, Insurance and Risk Management, vol. 5(1), pages 851-851.
    14. Teply, Petr & Polena, Michal, 2020. "Best classification algorithms in peer-to-peer lending," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    15. Pawełek Barbara, 2019. "Extreme Gradient Boosting Method In The Prediction Of Company Bankruptcy," Statistics in Transition New Series, Polish Statistical Association, vol. 20(2), pages 155-171, June.
    16. Debaere, Steven & Coussement, Kristof & De Ruyck, Tom, 2018. "Multi-label classification of member participation in online innovation communities," European Journal of Operational Research, Elsevier, vol. 270(2), pages 761-774.
    17. S M Finlay, 2006. "Predictive models of expenditure and over-indebtedness for assessing the affordability of new consumer credit applications," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(6), pages 655-669, June.
    18. Shian-Chang Huang & Cheng-Feng Wu & Chei-Chang Chiou & Meng-Chen Lin, 2022. "Intelligent FinTech Data Mining by Advanced Deep Learning Approaches," Computational Economics, Springer;Society for Computational Economics, vol. 59(4), pages 1407-1422, April.
    19. S M Finlay, 2008. "Towards profitability: a utility approach to the credit scoring problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(7), pages 921-931, July.
    20. Laura Auria & Rouslan A. Moro, 2008. "Support Vector Machines (SVM) as a Technique for Solvency Analysis," Discussion Papers of DIW Berlin 811, DIW Berlin, German Institute for Economic Research.
    21. Barbara Pawełek, 2019. "Extreme Gradient Boosting Method In The Prediction Of Company Bankruptcy," Transport Problems, Silesian University of Technology, Faculty of Transport, vol. 14(2), pages 155-171, June.
    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. Véronique Van Vlasselaer & Tina Eliassi-Rad & Leman Akoglu & Monique Snoeck & Bart Baesens, 2017. "GOTCHA! Network-Based Fraud Detection for Social Security Fraud," Management Science, INFORMS, vol. 63(9), pages 3090-3110, September.
    24. Robin Gubela & Artem Bequé & Stefan Lessmann & Fabian Gebert, 2019. "Conversion Uplift in E-Commerce: A Systematic Benchmark of Modeling Strategies," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(03), pages 747-791, May.
    25. Martens, David & Baesens, Bart & Van Gestel, Tony & Vanthienen, Jan, 2007. "Comprehensible credit scoring models using rule extraction from support vector machines," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1466-1476, December.
    26. Mirko Moscatelli & Simone Narizzano & Fabio Parlapiano & Gianluca Viggiano, 2019. "Corporate default forecasting with machine learning," Temi di discussione (Economic working papers) 1256, Bank of Italy, Economic Research and International Relations Area.
    27. B Baesens & C Mues & D Martens & J Vanthienen, 2009. "50 years of data mining and OR: upcoming trends and challenges," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 16-23, May.
    28. Loterman, Gert & Brown, Iain & Martens, David & Mues, Christophe & Baesens, Bart, 2012. "Benchmarking regression algorithms for loss given default modeling," International Journal of Forecasting, Elsevier, vol. 28(1), pages 161-170.
    29. TOBBACK, Ellen & MARTENS, David, 2017. "Retail credit scoring using fine-grained payment data," Working Papers 2017011, University of Antwerp, Faculty of Business and Economics.
    30. Carlos Serrano-Cinca & Begoña Gutiérrez-Nieto & Nydia M. Reyes, 2013. "A Social Approach to Microfinance Credit Scoring," Working Papers CEB 13-013, ULB -- Universite Libre de Bruxelles.
    31. 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.
    32. E Lima & C Mues & B Baesens, 2009. "Domain knowledge integration in data mining using decision tables: case studies in churn prediction," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(8), pages 1096-1106, August.
    33. Verbeke, Wouter & Dejaeger, Karel & Martens, David & Hur, Joon & Baesens, Bart, 2012. "New insights into churn prediction in the telecommunication sector: A profit driven data mining approach," European Journal of Operational Research, Elsevier, vol. 218(1), pages 211-229.
    34. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.
    35. Dumitrescu, Elena & Hué, Sullivan & Hurlin, Christophe & Tokpavi, Sessi, 2022. "Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects," European Journal of Operational Research, Elsevier, vol. 297(3), pages 1178-1192.
    36. Liu, Wanan & Fan, Hong & Xia, Meng, 2023. "Tree-based heterogeneous cascade ensemble model for credit scoring," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1593-1614.
    37. Gestel, Tony Van & Baesens, Bart & Suykens, Johan A.K. & Van den Poel, Dirk & Baestaens, Dirk-Emma & Willekens, Marleen, 2006. "Bayesian kernel based classification for financial distress detection," European Journal of Operational Research, Elsevier, vol. 172(3), pages 979-1003, August.
    38. Michael Doumpos & Constantin Zopounidis, 2007. "Model combination for credit risk assessment: A stacked generalization approach," Annals of Operations Research, Springer, vol. 151(1), pages 289-306, April.
    39. Bart H. L. Overes & Michel Wel, 2023. "Modelling Sovereign Credit Ratings: Evaluating the Accuracy and Driving Factors using Machine Learning Techniques," Computational Economics, Springer;Society for Computational Economics, vol. 61(3), pages 1273-1303, March.
    40. Chengbin Wang & Kuangnan Fang & Chenlu Zheng & Hechao Xu & Zewei Li, 2021. "Credit scoring of micro and small entrepreneurial firms in China," International Entrepreneurship and Management Journal, Springer, vol. 17(1), pages 29-43, March.
    41. Gubela, Robin & Bequé, Artem & Gebert, Fabian & Lessmann, Stefan, 2018. "Conversion uplift in e-commerce: A systematic benchmark of modeling strategies," IRTG 1792 Discussion Papers 2018-062, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    42. 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.
    43. Verbraken, Thomas & Bravo, Cristián & Weber, Richard & Baesens, Bart, 2014. "Development and application of consumer credit scoring models using profit-based classification measures," European Journal of Operational Research, Elsevier, vol. 238(2), pages 505-513.
    44. 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.
    45. Henri Fraisse & Matthias Laporte, 2021. "Return on Investment on AI: The Case of Capital Requirement," Working papers 809, Banque de France.
    46. Finlay, Steven, 2010. "Credit scoring for profitability objectives," European Journal of Operational Research, Elsevier, vol. 202(2), pages 528-537, April.
    47. Koen W. de Bock, 2017. "The best of two worlds: Balancing model strength and comprehensibility in business failure prediction using spline-rule ensembles," Post-Print hal-01588059, HAL.
    48. Elena Ivona DUMITRESCU & Sullivan HUE & Christophe HURLIN & Sessi TOKPAVI, 2020. "Machine Learning or Econometrics for Credit Scoring: Let’s Get the Best of Both Worlds," LEO Working Papers / DR LEO 2839, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    49. Tong, Edward N.C. & Mues, Christophe & Thomas, Lyn, 2013. "A zero-adjusted gamma model for mortgage loan loss given default," International Journal of Forecasting, Elsevier, vol. 29(4), pages 548-562.
    50. Anton Gerunov, 2023. "Modern Approaches To Forecasting Firm Default Rates Over The Short To Medium Term: An Application To A Panel Of Polish Companies," Yearbook of the Faculty of Economics and Business Administration, Sofia University, Faculty of Economics and Business Administration, Sofia University St Kliment Ohridski - Bulgaria, vol. 22(1), pages 5-15, October.
    51. Okumu Argan Wekesa & Mwalili Samuel & Mwita Peter, 2012. "Modelling Credit Risk for Personal Loans Using Product-Limit Estimator," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 3(1), pages 22-32, January.
    52. Lisa Crosato & Caterina Liberati & Marco Repetto, 2021. "Look Who's Talking: Interpretable Machine Learning for Assessing Italian SMEs Credit Default," Papers 2108.13914, arXiv.org, revised Sep 2021.
    53. Zhibin Niu & Runlin Li & Junqi Wu & Dawei Cheng & Jiawan Zhang, 2020. "iConViz: Interactive Visual Exploration of the Default Contagion Risk of Networked-Guarantee Loans," Papers 2006.09542, arXiv.org, revised Aug 2020.
    54. Jian Shi & Benlian Xu, 2016. "Credit Scoring by Fuzzy Support Vector Machines with a Novel Membership Function," JRFM, MDPI, vol. 9(4), pages 1-10, November.
    55. Fraisse, Henri & Laporte, Matthias, 2022. "Return on investment on artificial intelligence: The case of bank capital requirement," Journal of Banking & Finance, Elsevier, vol. 138(C).
    56. Agustin Pérez-Martín & Agustin Pérez-Torregrosa & Alejandro Rabasa & Marta Vaca, 2020. "Feature Selection to Optimize Credit Banking Risk Evaluation Decisions for the Example of Home Equity Loans," Mathematics, MDPI, vol. 8(11), pages 1-16, November.
    57. Sullivan Hué, 2022. "GAM(L)A: An econometric model for interpretable machine learning," French Stata Users' Group Meetings 2022 19, Stata Users Group.
    58. 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.
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    Cited by:

    1. Raduna, Daniela Viviana & Roman, Mihai Daniel, 2011. "Risk aversion influence on insurance market," MPRA Paper 37725, University Library of Munich, Germany, revised 01 Feb 2012.
    2. Quiggin, John & Chambers, Robert G., 2005. "Bargaining power and efficiency in insurance contracts," Risk and Sustainable Management Group Working Papers 151182, University of Queensland, School of Economics.
    3. Boonen, Tim J., 2016. "Nash equilibria of Over-The-Counter bargaining for insurance risk redistributions: The role of a regulator," European Journal of Operational Research, Elsevier, vol. 250(3), pages 955-965.
    4. Li Sanxi & Yao Dongmin & Xiao Hao, 2013. "Contract Bargaining with a Risk-Averse Agent," The B.E. Journal of Theoretical Economics, De Gruyter, vol. 13(1), pages 285-301, November.
    5. Zhou, Rui & Li, Johnny Siu-Hang & Tan, Ken Seng, 2015. "Modeling longevity risk transfers as Nash bargaining problems: Methodology and insights," Economic Modelling, Elsevier, vol. 51(C), pages 460-472.
    6. Huang, Rachel J. & Huang, Yi-Chieh & Tzeng, Larry Y., 2013. "Insurance bargaining under ambiguity," Insurance: Mathematics and Economics, Elsevier, vol. 53(3), pages 812-820.
    7. Giuseppe Attanasi & Laura Concina & Caroline Kamaté & Valentina Rotondi, 2020. "Firm’s protection against disasters: are investment and insurance substitutes or complements?," Theory and Decision, Springer, vol. 88(1), pages 121-151, February.

  9. Baesens, Bart & Viaene, Stijn & Van den Poel, Dirk & Vanthienen, Jan & Dedene, Guido, 2002. "Bayesian neural network learning for repeat purchase modelling in direct marketing," European Journal of Operational Research, Elsevier, vol. 138(1), pages 191-211, April.

    Cited by:

    1. Bilal Zorić, Alisa, 2015. "Case Study in Banking Using Neural Networks," Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference (2015), Kotor, Montengero, in: Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Kotor, Montengero, 10-11 September 2015, pages 251-257, IRENET - Society for Advancing Innovation and Research in Economy, Zagreb.
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    4. Chou, Ping & Chuang, Howard Hao-Chun & Chou, Yen-Chun & Liang, Ting-Peng, 2022. "Predictive analytics for customer repurchase: Interdisciplinary integration of buy till you die modeling and machine learning," European Journal of Operational Research, Elsevier, vol. 296(2), pages 635-651.
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    7. B. Baesens & T. Van Gestel & M. Stepanova & D. Van Den Poel, 2004. "Neural Network Survival Analysis for Personal Loan Data," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/281, Ghent University, Faculty of Economics and Business Administration.
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    19. K. Coussement & D. van den Poel, 2009. "Improving customer attrition prediction by integrating emotions from client/company interaction emails and evaluating multiple classifiers," Post-Print halshs-00581595, HAL.
    20. Baumgartner, Bernhard & Hruschka, Harald, 2005. "Allocation of catalogs to collective customers based on semiparametric response models," European Journal of Operational Research, Elsevier, vol. 162(3), pages 839-849, May.
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    23. Gubela, Robin M. & Lessmann, Stefan & Jaroszewicz, Szymon, 2020. "Response transformation and profit decomposition for revenue uplift modeling," European Journal of Operational Research, Elsevier, vol. 283(2), pages 647-661.
    24. B Baesens & T Van Gestel & S Viaene & M Stepanova & J Suykens & J Vanthienen, 2003. "Benchmarking state-of-the-art classification algorithms for credit scoring," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(6), pages 627-635, June.
    25. Opher Etzion & Amit Fisher & Segev Wasserkrug, 2005. "e-CLV: A Modeling Approach for Customer Lifetime Evaluation in e-Commerce Domains, with an Application and Case Study for Online Auction," Information Systems Frontiers, Springer, vol. 7(4), pages 421-434, December.
    26. 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.
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    29. Nadarajah, Saralees & Kotz, Samuel, 2009. "Models for purchase frequency," European Journal of Operational Research, Elsevier, vol. 192(3), pages 1014-1026, February.
    30. Aslan Lotfi & Zhengrui Jiang & Ali Lotfi & Dipak C. Jain, 2023. "Estimating Life Cycle Sales of Technology Products with Frequent Repeat Purchases: A Fractional Calculus-Based Approach," Information Systems Research, INFORMS, vol. 34(2), pages 409-422, June.
    31. B. Larivière & D. Van Den Poel, 2004. "Predicting Customer Retention and Profitability by Using Random Forests and Regression Forests Techniques," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/282, Ghent University, Faculty of Economics and Business Administration.
    32. Buckinx, Wouter & Van den Poel, Dirk, 2005. "Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting," European Journal of Operational Research, Elsevier, vol. 164(1), pages 252-268, July.
    33. Crone, Sven F. & Lessmann, Stefan & Stahlbock, Robert, 2006. "The impact of preprocessing on data mining: An evaluation of classifier sensitivity in direct marketing," European Journal of Operational Research, Elsevier, vol. 173(3), pages 781-800, September.
    34. 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.
    35. M. Ballings & D. Van Den Poel, 2012. "The Relevant Length of Customer Event History for Churn Prediction: How long is long enough?," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/804, Ghent University, Faculty of Economics and Business Administration.
    36. Seret, Alex & Verbraken, Thomas & Versailles, Sébastien & Baesens, Bart, 2012. "A new SOM-based method for profile generation: Theory and an application in direct marketing," European Journal of Operational Research, Elsevier, vol. 220(1), pages 199-209.
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