An experimental comparison of classification algorithms for imbalanced credit scoring data sets I Brown, C Mues Expert systems with applications 39 (3), 3446-3453, 2012 | 908 | 2012 |
Benchmarking regression algorithms for loss given default modeling G Loterman, I Brown, D Martens, C Mues, B Baesens International Journal of Forecasting 28 (1), 161-170, 2012 | 249 | 2012 |
Exposure at default models with and without the credit conversion factor ENC Tong, C Mues, I Brown, LC Thomas European Journal of Operational Research 252 (3), 910-920, 2016 | 51 | 2016 |
Machine learning interpretability for a stress scenario generation in credit scoring based on counterfactuals AC Bueff, M Cytryński, R Calabrese, M Jones, J Roberts, J Moore, I Brown Expert Systems with Applications 202, 117271, 2022 | 29 | 2022 |
Developing credit risk models using SAS enterprise miner and SAS/STAT: Theory and applications I Brown SAS Institute Inc., 2014 | 15 | 2014 |
Regression model development for credit card exposure at default (EAD) using SAS/STAT and SAS Enterprise Miner 5.3 I Brown SAS Global Forum, 2011 | 7 | 2011 |
An experimental comparison of classification techniques for imbalanced credit scoring data sets using SAS̉ Enterprise Miner I Brown Proceedings of SAS Global Forum, 2012 | 5 | 2012 |
Basel II compliant credit risk modelling: model development for imbalanced credit scoring data sets, loss given default (LGD) and exposure at default (EAD) ILJ Brown University of Southampton, 2012 | 4 | 2012 |
Benchmarking state-of-the-art regression algorithms for loss given default modelling G Loterman, I Brown, D Martens, C Mues, B Baesens Credit Scoring and Credit Control XI, 26-28, 2009 | 4 | 2009 |
Regression model development for Exposure at Default (EAD) I Brown, C Mues, L Thomas Proceedings of the 24th European Conference on Operational Research, 2010 | 1 | 2010 |
Mastering Marketing Data Science: A Comprehensive Guide for Today's Marketers I Brown John Wiley & Sons, 2024 | | 2024 |