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2. Crowdlending: mapping the core literature and research frontiers. (2022). Ribeiro-Navarrete, Samuel ; Pieiro-Chousa, Juan ; Palacios-Marques, Daniel ; Lopez-Cabarcos, Angeles M.
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3. Profit-based credit models with lender’s attitude towards risk and loss. (2021). Natarajan, Ramachandran ; Baidoo, Edwin.
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5. Application of profit-based credit scoring models using R. (2017). Bayraci, Selcuk.
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6. “Time-to-profit scorecards for revolving credit”. (2016). Sanchez-Barrios, Luis Javier ; Ansell, Jake ; Andreeva, Galina.
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7. A profit-based scoring system in consumer credit: making acquisition decisions for credit cards. (2011). Stewart, R T.
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8. Credit scoring for profitability objectives. (2010). Finlay, Steven.
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1. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. (2022). Kübler, Dorothea ; Hakimov, Rustamdjan ; Dargnies, Marie-Pierre ; Kubler, Dorothea.
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2. Credit scoring of micro and small entrepreneurial firms in China. (2021). Li, Zewei ; Fang, Kuangnan ; Xu, Hechao ; Zheng, Chenlu ; Wang, Chengbin.
   In: International Entrepreneurship and Management Journal.
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3. Machine Learning or Econometrics for Credit Scoring: Lets Get the Best of Both Worlds. (2021). Hué, Sullivan ; Dumitrescu, Elena Ivona ; Hurlin, Christophe ; Hue, Sullivan ; Tokpavi, Sessi.
   In: Working Papers.
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4. Predicting mortgage early delinquency with machine learning methods. (2021). Guo, Zhengfeng ; Zhao, Xinlei ; Chen, Shunqin.
   In: European Journal of Operational Research.
   RePEc:eee:ejores:v:290:y:2021:i:1:p:358-372.

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5. A kernel-free double well potential support vector machine with applications. (2021). Gao, Zheming ; Fang, Shu-Cherng ; Medhin, Negash ; Luo, Jian.
   In: European Journal of Operational Research.
   RePEc:eee:ejores:v:290:y:2021:i:1:p:248-262.

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6. Modelling Sovereign Credit Ratings: Evaluating the Accuracy and Driving Factors using Machine Learning Techniques. (2021). van der Wel, Michel.
   In: Papers.
   RePEc:arx:papers:2101.12684.

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7. A Hybrid Bi-level Metaheuristic for Credit Scoring. (2020). Yildirim, Umman Mahir ; En, Doruk ; Donmez, Cem Ari.
   In: Information Systems Frontiers.
   RePEc:spr:infosf:v:22:y:2020:i:5:d:10.1007_s10796-020-10037-0.

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8. Machine Learning or Econometrics for Credit Scoring: Let’s Get the Best of Both Worlds. (2020). Hué, Sullivan ; Dumitrescu, Elena Ivona ; Hurlin, Christophe ; Hue, Sullivan ; Tokpavi, Sessi.
   In: LEO Working Papers / DR LEO.
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9. Predicting loss given default in leasing: A closer look at models and variable selection. (2020). Loderbusch, Matthias ; Kriebel, Johannes ; Kaposty, Florian.
   In: International Journal of Forecasting.
   RePEc:eee:intfor:v:36:y:2020:i:2:p:248-266.

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10. Best classification algorithms in peer-to-peer lending. (2020). Teply, Petr ; Polena, Michal.
    In: The North American Journal of Economics and Finance.
    RePEc:eee:ecofin:v:51:y:2020:i:c:s1062940818302262.

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11. iConViz: Interactive Visual Exploration of the Default Contagion Risk of Networked-Guarantee Loans. (2020). Niu, Zhibin ; Li, Runlin ; Wu, Junqi ; Zhang, Jiawan ; Cheng, Dawei.
    In: Papers.
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12. Estimation Procedures of Using Five Alternative Machine Learning Methods for Predicting Credit Card Default. (2019). Lee, Michael ; Teng, Huei-Wen.
    In: Review of Pacific Basin Financial Markets and Policies (RPBFMP).
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13. Conversion Uplift in E-Commerce: A Systematic Benchmark of Modeling Strategies. (2019). Lessmann, Stefan ; Gubela, Robin ; Gebert, Fabian ; Beque, Artem.
    In: International Journal of Information Technology & Decision Making (IJITDM).
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14. EXTREME GRADIENT BOOSTING METHOD IN THE PREDICTION OF COMPANY BANKRUPTCY. (2019). Paweek, Barbara.
    In: Statistics in Transition New Series.
    RePEc:exl:29stat:v:20:y:2019:i:2:p:155-171.

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15. EXTREME GRADIENT BOOSTING METHOD IN THE PREDICTION OF COMPANY BANKRUPTCY. (2019). Paweek, Barbara.
    In: Transport Problems.
    RePEc:exl:1trans:v:14:y:2019:i:2:p:155-171.

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16. The naive Bayes classifier for functional data. (2019). Zhang, Yi-Chen ; Sakhanenko, Lyudmila.
    In: Statistics & Probability Letters.
    RePEc:eee:stapro:v:152:y:2019:i:c:p:137-146.

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17. A prediction-driven mixture cure model and its application in credit scoring. (2019). Wang, Zhao ; Zhao, Huimin ; Jiang, Cuiqing.
    In: European Journal of Operational Research.
    RePEc:eee:ejores:v:277:y:2019:i:1:p:20-31.

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18. A new approach for credit scoring by directly maximizing the Kolmogorov–Smirnov statistic. (2019). Chen, Yuanyuan ; Fang, Fang.
    In: Computational Statistics & Data Analysis.
    RePEc:eee:csdana:v:133:y:2019:i:c:p:180-194.

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19. Corporate default forecasting with machine learning. (2019). PARLAPIANO, FABIO ; Moscatelli, Mirko ; Narizzano, Simone ; Viggiano, Gianluca.
    In: Temi di discussione (Economic working papers).
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20. Predicting credit default probabilities using machine learning techniques in the face of unequal class distributions. (2019). Stelzer, Anna.
    In: Papers.
    RePEc:arx:papers:1907.12996.

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21. Conversion uplift in e-commerce: A systematic benchmark of modeling strategies. (2018). Gebert, Fabian ; Beque, Artem ; Lessmann, Stefan ; Gubela, Robin.
    In: IRTG 1792 Discussion Papers.
    RePEc:zbw:irtgdp:2018062.

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22. Improved Credit Scoring Model Based on Bagging Neural Network. (2018). Kevri, Jasmin ; Onko, Denana ; Delihodi, Adnan.
    In: International Journal of Information Technology & Decision Making (IJITDM).
    RePEc:wsi:ijitdm:v:17:y:2018:i:06:n:s0219622018500293.

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23. Predicting Corporate Failure: The GRASP-LOGIT Model || Predicci—n de la quiebra empresarial: el modelo GRASP-LOGIT. (2018). Nuñez Letamendia, Laura ; Yusta, Silvia Casado ; Laura, Laura Author-Email ; Author-Email, Joaquin Antonio.
    In: Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration.
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24. Big Data techniques to measure credit banking risk in home equity loans. (2018). Vaca, M ; Perez-Torregrosa, A ; Perez-Martin, A.
    In: Journal of Business Research.
    RePEc:eee:jbrese:v:89:y:2018:i:c:p:448-454.

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25. Multi-label classification of member participation in online innovation communities. (2018). de Ruyck, Tom ; Debaere, Steven ; Coussement, Kristof.
    In: European Journal of Operational Research.
    RePEc:eee:ejores:v:270:y:2018:i:2:p:761-774.

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26. Improving the performance of statistical learning methods with a combined meta-heuristic for consumer credit risk assessment. (2017). Altinbas, Hazar ; Akkaya, Goktug Cenk.
    In: Risk Management.
    RePEc:pal:risman:v:19:y:2017:i:4:d:10.1057_s41283-017-0021-0.

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27. GOTCHA! Network-Based Fraud Detection for Social Security Fraud. (2017). van Vlasselaer, Veronique ; Eliassi-Rad, Tina ; Snoeck, Monique ; Akoglu, Leman ; Baesens, Bart.
    In: Management Science.
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28. The best of two worlds: Balancing model strength and comprehensibility in business failure prediction using spline-rule ensembles. (2017). de Bock, Koen W.
    In: Post-Print.
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29. Loan pricing under estimation risk. (2017). Richard, Neuberg ; Lauren, Hannah .
    In: Statistics & Risk Modeling.
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30. Retail credit scoring using fine-grained payment data. (2017). Tobback, Ellen ; Martens, David.
    In: Working Papers.
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31. On the modelling of prognosis from delinquency to normal performance on retail consumer loans. (2016). Bravo, Jorge ; Chamboko, Richard.
    In: Risk Management.
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32. Social credit: a comprehensive literature review. (2015). Yu, Lean ; Zhang, Zongyi ; Li, Xinxie ; Tang, Ling ; Kou, Gang.
    In: Financial Innovation.
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33. Large Unbalanced Credit Scoring Using Lasso-Logistic Regression Ensemble. (2015). Xu, Qingsong ; Zhou, Lifeng ; Wang, Hong.
    In: PLOS ONE.
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34. Gaining insight into student satisfaction using comprehensible data mining techniques. (2012). Goethals, Frank ; Giangreco, Antonio ; Mola, Lapo ; Dejaeger, Karel ; Baesens, Bart.
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35. Credit rating prediction using Ant Colony Optimization. (2010). Haesen, R ; de Backer, M ; Vanthienen, J ; Martens, D ; Baesens, B ; van Gestel, T.
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36. Consumer finance: challenges for operational research. (2010). Thomas, L C.
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37. Domain knowledge integration in data mining using decision tables: case studies in churn prediction. (2009). Lima, E ; Mues, C ; Baesens, B.
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38. 50 years of data mining and OR: upcoming trends and challenges. (2009). Vanthienen, J ; Martens, D ; Mues, C ; Baesens, B.
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39. Credit scoring with macroeconomic variables using survival analysis. (2009). Bellotti, T ; Crook, J.
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40. Handbook on Information Technology in Finance. (2008). .
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41. Forecasting and operational research: a review. (2008). Nikolopoulos, Konstantinos ; Crone, S F ; Fildes, R ; Syntetos, A A.
    In: Journal of the Operational Research Society.
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42. Towards profitability: a utility approach to the credit scoring problem. (2008). Finlay, S M.
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43. Model combination for credit risk assessment: A stacked generalization approach. (2007). Doumpos, Michael ; Zopounidis, Constantin.
    In: Annals of Operations Research.
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44. Predictive models of expenditure and over-indebtedness for assessing the affordability of new consumer credit applications. (2006). Finlay, S M.
    In: Journal of the Operational Research Society.
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45. Knowledge acquisition and revision using neural networks: an application to a cross-national study of brand image perception. (2006). Setiono, R ; Azcarraga, A.
    In: Journal of the Operational Research Society.
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46. Support vector machines for classifying and describing credit applicants: detecting typical and critical regions. (2005). Stecking, Ralf ; Schebesch, K B.
    In: Journal of the Operational Research Society.
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47. Neural network survival analysis for personal loan data. (2005). Van den Poel, Dirk ; Stepanova, M ; Vanthienen, J ; Baesens, B ; van Gestel, T.
    In: Journal of the Operational Research Society.
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48. The impact of sample bias on consumer credit scoring performance and profitability. (2005). Van den Poel, Dirk ; Verstraeten, G.
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49. Credit scoring of micro and small entrepreneurial firms in China. (). Li, Zewei ; Fang, Kuangnan ; Xu, Hechao ; Zheng, Chenlu ; Wang, Chengbin.
    In: International Entrepreneurship and Management Journal.
    RePEc:spr:intemj:v::y::i::d:10.1007_s11365-020-00685-8.

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50. A Hybrid Bi-level Metaheuristic for Credit Scoring. (). Yildirim, Umman Mahir ; En, Doruk ; Donmez, Cem Ari.
    In: Information Systems Frontiers.
    RePEc:spr:infosf:v::y::i::d:10.1007_s10796-020-10037-0.

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