Algorithmic decision making methods for fair credit scoring
Open accessAbstract
The effectiveness of machine learning in evaluating the creditworthiness of loan applicants has been demonstrated for a long time. However, there is concern that the use of automated decision-making processes may result in unequal treatment of groups or individuals, potentially leading to discriminatory outcomes. This paper seeks to address this issue by evaluating the effectiveness of 12 leading bias mitigation methods across 5 different fairness metrics, as well as assessing their accuracy and potential profitability for financial institutions. Through our analysis, we have identified the challenges associated with achieving fairness while maintaining accuracy and profitabiliy, and have highlighted both the most successful and least successful mitigation methods. Ultimately, our research serves to bridge the gap between experimental machine learning and its practical applications in the finance industry.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Implementing bias mitigation techniques enhances model fairness, leading to more impartial lending determinations.
“Implementing bias mitigation techniques enhances model fairness, leading to more impartial lending determinations [290-292].”
From AI Fairness in Data Management and Analytics: A Review on Challenges, Methodologies and Applications (2023) · cites this paper as [292]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., & Zemel, R. (2012) · Proceedings of the 3rd Innovations in Theoretical Computer Science Conference · doi:10.1145/2090236.2090255 · Cited by 3,501 · see citing works
Equality of Opportunity in Supervised Learning
Hardt, M., Price, E., & Srebro, N. (2016) · arXiv · doi:10.48550/arxiv.1610.02413 · Cited by 2,536 · see citing works
Certifying and Removing Disparate Impact
Feldman, M., Friedler, S. A., Moeller, J., Scheidegger, C., & Venkatasubramanian, S. (2015) · Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2783258.2783311 · Cited by 1,827 · see citing works
Mitigating Unwanted Biases with Adversarial Learning
Zhang, B. H., Lemoine, B., & Mitchell, M. (2018) · Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3278721.3278779 · Cited by 1,387 · see citing works
Data preprocessing techniques for classification without discrimination
Kamiran, F., & Calders, T. (2012) · Knowledge and Information Systems · doi:10.1007/s10115-011-0463-8 · Cited by 1,305 · see citing works
(2013) · Cited by 1,126 · see citing works
Inherent trade-offs in the fair determination of risk scores
Kleinberg, J., Mullainathan, S., & Raghavan, M. (2017) · Schloss Dagstuhl – Leibniz-Zentrum für Informatik · doi:10.4230/lipics.itcs.2017.43 · Cited by 943 · cited 2× · see citing works
“Generally, the literature sources agree that not all fairness criteria can be used simultaneously to evaluate the fairness of an ML process [12]–[15], as some of them contradict others.”
BackgroundInherent Trade-Offs in the Fair Determination of Risk Scores
Kleinberg, J., Mullainathan, S., & Raghavan, M. (2016) · arXiv · doi:10.48550/arxiv.1609.05807 · Cited by 941 · see citing works
AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias
Bellamy, R. K. E., Dey, K., Hind, M., Hoffman, S. C., Houde, S., Kannan, K., Lohia, P., Martino, J., Mehta, S., Mojsilović, A., et al. (2019) · IBM Journal of Research and Development · doi:10.1147/jrd.2019.2942287 · Cited by 884 · see citing works
Fairness-Aware Classifier with Prejudice Remover Regularizer
Kamishima, T., Akaho, S., Asoh, H., & Sakuma, J. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33486-3_3 · Cited by 694 · see citing works
A comparative study of fairness-enhancing interventions in machine learning
Friedler, S. A., Scheidegger, C., Venkatasubramanian, S., Choudhary, S., Hamilton, E. P., & Roth, D. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287589 · Cited by 601 · see citing works
Predictably Unequal? The Effects of Machine Learning on Credit Markets
FUSTER, A., GOLDSMITH‐PINKHAM, P., RAMADORAI, T., & WALTHER, A. (2022) · The Journal of Finance · doi:10.1111/jofi.13090 · Cited by 548 · see citing works
On Fairness and CalibrationPreprint
Pleiss, G., Raghavan, M., Wu, F., Kleinberg, J. M., & Weinberger, K. Q. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.02012 · Cited by 509 · see citing works
Building Classifiers with Independency Constraints
Calders, T., Kamiran, F., & Pechenizkiy, M. (2009) · 2009 IEEE International Conference on Data Mining Workshops · doi:10.1109/icdmw.2009.83 · Cited by 468 · see citing works
Optimized Pre-Processing for Discrimination Prevention
Calmon, F. P., Wei, D., Vinzamuri, B., Ramamurthy, K. N., & Varshney, K. R. (2017) · neural information processing systems · Cited by 459 · see citing works
Zarsky, T. (2016) · Science, Technology, & Human Values · doi:10.1177/0162243915605575 · Cited by 422 · see citing works
Classifying without discriminating
Kamiran, F., & Calders, T. (2009) · 2009 2nd International Conference on Computer, Control and Communication · doi:10.1109/ic4.2009.4909197 · Cited by 409 · see citing works
The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink
Patterson, D., Gonzalez, J., Holzle, U., Le, Q., Liang, C., Munguia, L.-M., Rothchild, D., So, D. R., Texier, M., & Dean, J. (2022) · Computer · doi:10.1109/mc.2022.3148714 · Cited by 400 · see citing works
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness
Kearns, M., Neel, S., Roth, A., & Wu, Z. S. (2017) · arXiv · doi:10.48550/arxiv.1711.05144 · Cited by 381 · cited 2× · see citing works
“from a small group), leading to discrimination in subgroups, even if group fairness can still be achieved [19].”
BackgroundMethodA Reductions Approach to Fair ClassificationPreprint
Agarwal, A., Beygelzimer, A., Dudı́k, M., Langford, J. C., & Wallach, H. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.02453 · Cited by 364 · see citing works
Fair Regression: Quantitative Definitions and Reduction-based AlgorithmsPreprint
Agarwal, A., Dudík, M., & Wu, Z. S. (2019) · International Conference on Machine Learning · doi:10.48550/arxiv.1905.12843 · Cited by 321 · see citing works
Decision Theory for Discrimination-Aware Classification
Kamiran, F., Karim, A., & Zhang, X. (2012) · 2012 IEEE 12th International Conference on Data Mining · doi:10.1109/icdm.2012.45 · Cited by 300 · see citing works
A clarification of the nuances in the fairness metrics landscape
Castelnovo, A., Crupi, R., Greco, G., Regoli, D., Penco, I., & Cosentini, A. (2021) · Scientific Reports · doi:10.1038/s41598-022-07939-1 · Cited by 283 · see citing works
A survey on datasets for fairness‐aware machine learning
Le Quy, T., Roy, A., Iosifidis, V., Zhang, W., & Ntoutsi, E. (2022) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1452 · Cited by 261 · see citing works
Deep learning for credit scoring: Do or don’t?
Gunnarsson, B. R., Broucke, S. V., Baesens, B., Óskarsdóttir, M., & Lemahieu, W. (2021) · European Journal of Operational Research · doi:10.1016/j.ejor.2021.03.006 · Cited by 256 · see citing works
Delayed Impact of Fair Machine Learning
Liu, L. T., Dean, S., Rolf, E., Simchowitz, M., & Hardt, M. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/862 · Cited by 236 · see citing works
Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees
Celis, L. E., Huang, L., Keswani, V., & Vishnoi, N. K. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287586 · Cited by 231 · see citing works
Speicher, T., Heidari, H., Grgic-Hlaca, N., Gummadi, K. P., Singla, A., Weller, A., & Zafar, M. B. (2018) · Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3219819.3220046 · Cited by 207 · see citing works
Fairness in credit scoring: Assessment, implementation and profit implications
Kozodoi, N., Jacob, J., & Lessmann, S. (2022) · European Journal of Operational Research · doi:10.1016/j.ejor.2021.06.023 · Cited by 198 · see citing works
Verbraken, T., Bravo, C., Weber, R., & Baesens, B. (2014) · European Journal of Operational Research · doi:10.1016/j.ejor.2014.04.001 · Cited by 179 · see citing works
CREDIT SCORING IN THE ERA OF BIG DATA
Hurley, M., & Adebayo, J. (2017) · bepress Legal Repository · Cited by 177 · see citing works
The problem of infra-marginality in outcome tests for discrimination
Simoiu, C., Corbett-Davies, S., & Goel, S. (2017) · The Annals of Applied Statistics · doi:10.1214/17-aoas1058 · Cited by 154 · see citing works
Hofmann, H. (1994) · UCI Machine Learning Repository · doi:10.24432/c5nc77 · Cited by 129 · see citing works
Algorithmic Fairness in Mortgage Lending: from Absolute Conditions to Relational Trade-offs
Lee, M. S. A., & Floridi, L. (2021) · Minds and Machines · doi:10.1007/s11023-020-09529-4 · Cited by 106 · see citing works
Impact of resampling methods and classification models on the imbalanced credit scoring problems
Xiao, J., Wang, Y., Chen, J., Xie, L., & Huang, J. (2021) · Information Sciences · doi:10.1016/j.ins.2021.05.029 · Cited by 67 · see citing works
Cost-sensitive learning for profit-driven credit scoring
Petrides, G., Moldovan, D., Coenen, L., Guns, T., & Verbeke, W. (2020) · Journal of the Operational Research Society · doi:10.1080/01605682.2020.1843975 · Cited by 46 · see citing works
Algorithmic fairness in credit scoring
Bono, T., Croxson, K., & Giles, A. (2021) · Oxford Review of Economic Policy · doi:10.1093/oxrep/grab020 · Cited by 43 · see citing works
Causal Modeling for Fairness in Dynamical SystemsPreprint
Creager, E., Madras, D., Pitassi, T., & Zemel, R. S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.09141 · Cited by 28 · see citing works
Facing the Challenges of Developing Fair Risk Scoring Models
Szepannek, G., & Lübke, K. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.681915 · Cited by 27 · see citing works
To do or not to do? Cost-sensitive causal classification with individual treatment effect estimates
Verbeke, W., Olaya, D., Guerry, M., & Belle, J. V. (2022) · European Journal of Operational Research · doi:10.1016/j.ejor.2022.03.049 · Cited by 27 · see citing works
Fairness by Explicability and Adversarial SHAP Learning
Hickey, J. M., Stefano, P. G. D., & Vasileiou, V. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-67664-3_11 · Cited by 26 · see citing works
The Fairness of Credit Scoring Models
Hurlin, C., Perignon, C., & Saurin, S. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3785882 · Cited by 26 · see citing works
Fair Decisions Despite Imperfect Predictions
Kilbertus, N., Gomez-Rodriguez, M., Schölkopf, B., Muandet, K., & Valera, I. (2020) · Cited by 24 · see citing works
Unpacking the Black Box: Regulating Algorithmic DecisionsPreprint
Blattner, L., Nelson, S., & Spiess, J. (2021) · RePEc: Research Papers in Economics · doi:10.25740/ry909yd9970 · Cited by 14 · see citing works
The Independence of Fairness-Aware Classifiers
Kamishima, T., Akaho, S., Asoh, H., & Sakuma, J. (2013) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2013.133 · Cited by 12 · see citing works
Stable and Fair ClassificationPreprint
Huang, L., & Vishnoi, N. K. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.07823 · Cited by 11 · see citing works
Improving Fairness of AI Systems with Lossless De-biasingPreprint
Zhou, Y., Kantarcıoğlu, M., & Clifton, C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.04534 · Cited by 6 · see citing works
Algorithmic and Economic Perspectives on FairnessPreprint
Parkes, D. C., Vohra, R., & participants, O. W. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.05282 · Cited by 6 · see citing works
The never ending European credit data mess
Ferretti, F. (2017) · Brunel University Research Archive (BURA) (Brunel University London) · Cited by 4 · see citing works
Flagged first, then by citations
Research works printed but not linked: 3
- C. Dwork, M. Hardt, T. Pitassi, O. Reingold, and R. Zemel, ''Fair- ness through awareness,'' in Proc. 3rd Innov. Theor. Comput. Sci. Conf. New York, NY, USA: Association for Computing Machinery, Jan. 2012, pp. 214-226.
- W. Dieterich, C. Mendoza, and T. Brennan, ''Compas risk scales: Demon- strating accuracy equity and predictive parity,'' Northpointe Inc., Traverse City, MI, USA, vol. 7, no. 4, Tech. Rep., 2016.
- B. Paaßen, A. Bunge, C. Hainke, L. Sindelar, and M. Vogelsang, ''Dynamic fairness-Breaking vicious cycles in automatic decision making,'' in Proc. 27th Eur. Symp. Artif. Neural Netw. (ESANN), 2019, pp. 1-16.
Cited by
Sorted by citations
AI Fairness in Data Management and Analytics: A Review on Challenges, Methodologies and Applications
Chen, P., Wu, L., & Wang, L. (2023) · Applied Sciences · doi:10.3390/app131810258 · Cited by 155 · see citing works
Tigges, M., Mestwerdt, S., Tschirner, S., & Mauer, R. (2024) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2024.123491 · Cited by 57 · see citing works
Performance, Fairness, and Explainability in AI-Based Credit Scoring: A Systematic Literature Review
Bahlool, R. A., Hewahi, N., & Elmedany, W. M. (2026) · Journal of risk and financial management · doi:10.3390/jrfm19020104 · Cited by 20 · see citing works
Do FinTech algorithms reduce gender inequality in banks loans? A quantitative study from the USA
Song, Z., Rehman, S. U., PingNg, C., Zhou, Y., Washington, P., & Verschueren, R. (2024) · Journal of Applied Economics · doi:10.1080/15140326.2024.2324247 · Cited by 9 · see citing works
A Guide to Machine Learning Epistemic Ignorance, Hidden Paradoxes, and Other Tensions
Naser, M. Z. (2025) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.70038 · Cited by 9 · see citing works
AI-Driven Credit Scoring and Credit Line Solution for the Unreserved and Self-Employed
Kumbhar, T., Agrawal, D., Saldanha, L., & Koshti, D. (2024) · International Conference on Inventive Computing and Informatics (ICICI) · doi:10.1109/icici62254.2024.00039 · Cited by 6 · see citing works
Bahadori, S., & Hassanzadeh, A. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.131872 · Cited by 6 · see citing works
Consistent Range Approximation for Fair Predictive Modeling
Zhu, J., Galhotra, S., Sabri, N., & Salimi, B. (2023) · Proceedings of the VLDB Endowment · doi:10.14778/3611479.3611498 · Cited by 4 · see citing works
Credit Scoring for Loan Applicants Using Machine Learning Models
Krishna, K. S., Kumar, B. D., Reddy, M. D., Saketh, C., & Venugopalan, M. (2024) · International Conference on Intelligent Technologies (CONIT) · doi:10.1109/conit61985.2024.10626212 · Cited by 4 · see citing works
Predicting Loan Risk in Banking Sectors using Machine Learning
Akalya, S., Veronica, J. J., Jenitha, J. M. M., Gladence, L. M., & Angel, M. T. G. R. (2025) · International Conference on Sentiment Analysis and Deep Learning (ICSADL) · doi:10.1109/icsadl65848.2025.10933481 · Cited by 3 · see citing works
Domain-Constrained Stacking Framework for Credit Default Prediction
Ding, M.-L., Ma, Y., & You, F. (2025) · Mathematics · doi:10.3390/math13213451 · Cited by 3 · see citing works
Dahake, P. S., & Tirpude, R. (2025) · International Conference on Data Science and Business Systems (ICDSBS) · doi:10.1109/icdsbs63635.2025.11031530 · Cited by 2 · see citing works
Kraus, E. B., Pargent, F., Hilbert, S., & Augustin, T. (2025) · Collabra Psychology · doi:10.1525/collabra.145043 · Cited by 2 · see citing works
AI in Business and Finance: A Micro-to-Macro Perspective on Its Impact
Shehzadi, A., Amjad, M. S., Anwar, M. N., Satti, F. A., & Fraz, M. M. (2024) · Archives of Business Research · doi:10.14738/abr.1210.17781 · Cited by 1 · see citing works
Nayak, S. (2026) · International Research Conference on Smart Computing and Systems Engineering (SCSE) · doi:10.1109/scse70081.2026.11499928 · Cited by 1 · see citing works
Algorithmic Decision-Making and Human Autonomy in Finance: A Systematic Review of AI Ethics Research
Bhatti, T. (2026) · Journal of Statistical Theory and Applications · doi:10.1007/s44199-026-00175-w · Cited by 7 · see citing works
Achieving Equity via Transfer Learning With Fairness Optimization
Wang, X., Chang, C., & Yang, C. C. (2024) · IEEE Access · doi:10.1109/access.2024.3519465 · Cited by 1 · see citing works
Lead, D. E., Koppolu, H. K. R., Sheelam, G. K., & IT Data Engineer, S. S. (2024) · Global research and development journal for engineering. · doi:10.70179/jhbcj508 · Cited by 1 · see citing works
Bahlool, R. A., Hewahi, N. M., & Elmedany, W. M. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5784362
Algorithmic fairness and bias mitigation in financial artificial intelligence: scoping review
Wecchi, M., & Berton, L. (2026) · AI and Ethics · doi:10.1007/s43681-026-01238-5
Mohanty, P., & Mishra, S. (2026) · Business Strategy and the Environment · doi:10.1002/bse.71333
AI-Driven Hybrid Graph-Ensemble Approach for Credit Risk Classification
Sorathiya, A., Mehta, J. H., Kudale, P., Bhanushali, H., Sorathiya, M., Mehta, T., Suresh, H., & Sorathiya, M. (2026) · International Conference on Electronics, Computers and Artificial Intelligence (ECAI) · doi:10.1109/ecai69016.2026.11613611
Bahlool, R. A., & Hewahi, N. M. (2026) · AI · doi:10.3390/ai7080312
Kundavaram, R. R., Onteddu, A. R., & Bandhela, R. R. (2026) · Applied Computational Intelligence and Soft Computing · doi:10.1155/acis/8520062
Adegun, A., Li, W., & Fawale, S. (2026) · International Conference on Electronics, Communications and Computer Science (ICECCS) · doi:10.1109/iceccs70432.2026.11651164
Kordoni, A., Sachoulidou, A., & de Oliveira, C. R. (2026) · Cambridge University Press eBooks · doi:10.1017/9781009623605.006
Xu, T., Zhang, J., & Zhu, W. (2026) · International Conference on Artificial Intelligence and Digital Finance (AIDF) · doi:10.1145/3844275.3844345
FairLend: Privacy-preserving gender-fairness auditing for loan processing
Kumar, M., Epiphaniou, G., Hooper, M., & Maple, C. (2026) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2026.104684
- Authors
- Moldovan, D.
- Affiliations
- Babeș-Bolyai University (RO)
- Corresponding author
- Moldovan, Darie
- ORCID
- Moldovan, Darie
- Venue
- IEEE Access
- Published
- 2023
- Volume
- 11
- Pages
- 59729-59743
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W4380685022
- Cited by
- 28
- References
- 48
- Citations by year
- 2026: 13 · 2025: 6 · 2024: 7 · 2023: 2
- FWCI
- 6.99
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University) (cc-by)
- Funders
- 2022 Development Fund of Babeş-Bolyai University
- Romanian National Authority for Scientific Research (PN-III-P4-ID-PCE-2020-0929)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Business, Management and Accounting › Accounting › Financial Distress and Bankruptcy Prediction
- Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
- Keywords
- algorithmic decision-making, algorithmic fairness, fairness metrics, creditworthiness assessment, loan applicants, fairness-accuracy trade-off, machine learning in finance
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
