Development and application of consumer credit scoring models using profit-based classification measures
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The MP measure allows selecting the optimal model and proportion of customers to be included in the campaign, potentially yielding a significant increase in profitability relative to the profit earned if statistical measures are used.
“This measure allows selecting the optimal model and proportion of customers to be included in the campaign, potentially yielding a significant increase in profitability relative to the profit earned if statistical measures are used [19].”
From Why you should stop predicting customer churn and start using uplift models (2021) · cites this paper as [19]
Section 3.1 says MP can select the model with the highest incremental profit and defines the optimal fraction of customers toward whom an action should be taken. But the paper applies these measures to consumer credit decisions, not campaign inclusion. Its profitability comparison with statistical measures in Section 4.4 concerns EMP rather than MP, so the claim conflates the measures and settings. (LLM, conf=0.88)
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Cites
An introduction to ROC analysis
Fawcett, T. (2006) · Pattern Recognition Letters · doi:10.1016/j.patrec.2005.10.010 · Cited by 21,879 · see citing works
Assessing the accuracy of prediction algorithms for classification: an overview
Baldi, P., Brunak, S., Chauvin, Y., Andersen, C. A. F., & Nielsen, H. (2000) · Bioinformatics · doi:10.1093/bioinformatics/16.5.412 · Cited by 2,111 · see citing works
Training cost-sensitive neural networks with methods addressing the class imbalance problem
Zhou, Z., & Liu, X. (2006) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2006.17 · Cited by 1,354 · see citing works
Domingos, P. (1999) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/312129.312220 · Cited by 1,309 · see citing works
Robust classification for imprecise environments
Provost, F., & Fawcett, T. (2001) · Machine Learning · doi:10.1023/a:1007601015854 · Cited by 1,250 · see citing works
Measuring classifier performance: a coherent alternative to the area under the ROC curve
Hand, D. J. (2009) · Machine Learning · doi:10.1007/s10994-009-5119-5 · Cited by 1,037 · see citing works
Benchmarking state-of-the-art classification algorithms for credit scoring
Baesens, B., Van Gestel, T., Viaene, S., Stepanova, M., Suykens, J., & Vanthienen, J. (2003) · Journal of the Operational Research Society · doi:10.1057/palgrave.jors.2601545 · Cited by 904 · see citing works
Self-citationCredit Scoring and Its Applications
Thomas, L. C., Edelman, D. B., & Crook, J. N. (2002) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9780898718317 · Cited by 674 · see citing works
Verbeke, W., Dejaeger, K., Martens, D., Hur, J., & Baesens, B. (2012) · European Journal of Operational Research · doi:10.1016/j.ejor.2011.09.031 · Cited by 462 · see citing works
Self-citationOn learning algorithm selection for classification
Ali, S., & Smith‐Miles, K. (2006) · Applied Soft Computing · doi:10.1016/j.asoc.2004.12.002 · Cited by 380 · see citing works
Risk-based pricing of interest rates for consumer loans
Edelberg, W. M. (2006) · Journal of Monetary Economics · doi:10.1016/j.jmoneco.2005.09.001 · Cited by 208 · see citing works
Bernstein, A., Provost, F. J., & Hill, S. (2005) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2005.67 · Cited by 195 · see citing works
Hernández‐Orallo, J., Flach, P. A., & Ferri, C. (2012) · Bristol Research (University of Bristol) · Cited by 181 · see citing works
Benchmarking regression algorithms for loss given default modeling
Loterman, G., Brown, I., Martens, D., Mues, C., & Baesens, B. (2011) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2011.01.006 · Cited by 180 · see citing works
Self-citationVerbraken, T., Verbeke, W., & Baesens, B. (2012) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2012.50 · Cited by 178 · see citing works
Self-citationGood practice in retail credit scorecard assessment
Hand, D. J. (2005) · Journal of the Operational Research Society · doi:10.1057/palgrave.jors.2601932 · Cited by 118 · see citing works
Credit scoring for risk managers : the handbook for lenders
Mays, E. (2004) · Medical Entomology and Zoology · Cited by 75 · see citing works
Consumer Credit Models: Pricing, Profit and Portfolios
Savvopoulos, A. (2010) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/j.1467-985x.2009.00634_12.x · Cited by 75 · see citing works
Credit scoring for profitability objectives
Finlay, S. (2009) · European Journal of Operational Research · doi:10.1016/j.ejor.2009.05.025 · Cited by 74 · see citing works
Granting and managing loans for micro-entrepreneurs: New developments and practical experiences
Bravo, C., Maldonado, S., & Weber, R. W. (2012) · European Journal of Operational Research · doi:10.1016/j.ejor.2012.10.040 · Cited by 54 · see citing works
Self-citationQuantile regression for modelling distributions of profit and loss
Somers, M., & Whittaker, J. (2006) · European Journal of Operational Research · doi:10.1016/j.ejor.2006.08.063 · Cited by 47 · see citing works
Mathematics of investment and credit
Broverman, S. A. (1991) · Cited by 44 · see citing works
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Benchmarking state-of-the-art classification algorithms for credit scoring: an update of research
Lessmann, S., Baesens, B., Seow, H.-V., & Thomas, L. C. (2015) · European Journal of Operational Research · doi:10.1016/j.ejor.2015.05.030 · Cited by 1,284 · see citing works
Self-citationWhy you should stop predicting customer churn and start using uplift models
Devriendt, F., Berrevoets, J., & Verbeke, W. (2021) · Information Sciences · doi:10.1016/j.ins.2019.12.075 · Cited by 90 · see citing works
Algorithmic decision making methods for fair credit scoring
Moldovan, D. (2023) · IEEE Access · doi:10.1109/access.2023.3286018 · Cited by 28 · see citing works
- Authors
- Verbraken, T., Bravo, C., Weber, R., & Baesens, B.
- Affiliations
- KU Leuven (BE)
- Universidad de Talca (CL)
- University of Chile (CL)
- University of Southampton (GB)
- Corresponding author
- Bravo, Cristián
- Venue
- European Journal of Operational Research
- Publisher
- Elsevier BV
- Published
- 2014-10
- Volume
- 238
- Issue
- 2
- Pages
- 505-513
- ISSN
- 0377-2217 (ISSN-L)
- OpenAlex
- W1973982793
- Cited by
- 179
- References
- 23
- Citations by year
- 2026: 12 · 2025: 15 · 2024: 16 · 2023: 17 · 2022: 25 · 2021: 22 · 2020: 19 · 2019: 20 · 2018: 14 · 2017: 10 · 2016: 3 · 2015: 4 · 2014: 1 · 2013: 1
- FWCI
- 26.94
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- European Journal of Operational Research (publishedVersion)
- ePrints Soton (University of Southampton) (acceptedVersion)
- Lirias (KU Leuven) (submittedVersion, other-oa)
- RePEc: Research Papers in Economics (submittedVersion)
- Universidad de Chile (submittedVersion, cc-by-nc-nd)
- Funders
- Fonds Wetenschappelijk Onderzoek
- Comisión Nacional de Investigación Científica y Tecnológica (FBO16, P-05-004-F)
- Topics
- Social Sciences › Business, Management and Accounting › Accounting › Financial Distress and Bankruptcy Prediction
- Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
- Social Sciences › Business, Management and Accounting › Marketing › Customer churn and segmentation
- Keywords
- consumer credit scoring, credit risk modeling, loss given default, operational income, model deployment, government loans
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