Skip to main content

Anthropocentric AI for EU Consumer Lending

‘Hacking’ Opacity and Discrimination ‘Sins’ Powered by Creditworthiness Machines

  • Conference paper
  • First Online:
Progress in Artificial Intelligence (EPIA 2024)

Abstract

Incorporating AI-based decision-making into consumer credit assessment under the framework of Consumer Law enhances regulatory compliance. This paper outlines a Multi-Agent Systems (MAS) to implementing Art. 18(6)(8)(9) of the EU 2023/2225 Directive, dated 18 October. In pursuit of this goal, we propose a legal framework emphasizing the necessity of hybrid oversight in AI-based consumer scoring. This study aims to improve transparency and fairness through the implementation of an Explainable Agent-based layer. Overall, this research introduces the concept of Machine-Centred Anthropocentrism. It acknowledges that, after the training, validation and testing stages, credit analysts no longer have complete psychological control over the data-driven entities programmers have given birth.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Thomas, L.C., Edelman, D., Crock, J.: Credit scoring and its applications: monographs on mathematical modeling and computation. Society for Industrial and Applied Mathematics, Philadelphia (2002)

    Book  Google Scholar 

  2. Louzada, F., Ara, A., Fernandes, G.: Classification methods applied to credit scoring: systematic review and overall comparison. Surv. Oper. Res. Manag.Sci. 21, 117–134 (2016). https://doi.org/10.1016/j.sorms.2016.10.001

    Article  MathSciNet  Google Scholar 

  3. Dastile, X., Celik, T., Potsane, M.: Statistical and machine learning models in credit scoring: a systematic literature survey. Appl. Soft Comput. J. 91 (2020). https://doi.org/10.1016/j.asoc.2020.106263

  4. Thomas, L.C.: A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers. Int. J. Forecast. 16, 149–172 (2000). https://doi.org/10.1016/S0169-2070(00)00034-0

    Article  Google Scholar 

  5. Addy, W., Ajayi-Nifise, A., Odeyemi, O., Falaiye, T.: AI in credit scoring: a comprehensive review of models and predictive analytics. Glob. J. Eng. Technol. Adv. 18, 118–129 (2024). https://doi.org/10.30574/gjeta.2024.18.2.0029

  6. Bhatore, S., Mohan, L., Reddy, Y.R.: Machine learning techniques for credit risk evaluation: a systematic literature review. J. Bank. Finan. Technol. 4, 111–138 (2020). https://doi.org/10.1007/s42786-020-00020-3

    Article  Google Scholar 

  7. Ferretti, F., Vandone, D.: Personal Debt in Europe: the EU Financial Market and Consumer insolvency. Cambridge University Press, Cambridge (2019)

    Book  Google Scholar 

  8. Garcia, A., Garcia, M., Rigobon, R.: Algorithmic discrimination in the credit domain: what do we know about it? AI & Soc. (2023). https://doi.org/10.1007/s00146-023-01676-3

    Article  Google Scholar 

  9. Wachter, S., Mittelstadt, B.: A right to reasonable inferences: re-thinking data protection law in the age of big data and AI. Columbia Bus. Law Rev. 494, 494–620 (2019)

    Google Scholar 

  10. Solove, D.J.: Artificial intelligence and privacy. Florida Law Rev. 77 (forthcoming) (2025). https://doi.org/10.2139/ssrn.4713111

  11. Burrell, J.: How the machine ‘thinks’: understanding opacity in machine learning algorithms. Big Data Soc. 3, 1–12 (2016). https://doi.org/10.1177/2053951715622512

    Article  Google Scholar 

  12. European Data Protection Supervisor (EDPS): Opinion 11/2021 on the Proposal for a Directive on consumer credits (2021). https://edps.europa.eu/data-protection/our-work/publications/opinions/edps-opinion-proposal-directive-consumer-credits_en

  13. Lee, J.: Access to finance for artificial intelligence regulation in the financial services industry. Euro. Busi. Organ. Law Rev. 21, 731–757 (2020). https://doi.org/10.1007/s40804-020-00200-0

    Article  Google Scholar 

  14. European Commission (Directorate-General for Justice and Consumers): Evaluation of Directive 2008/48/EC on credit agreements for consumers [Final Report] (2020)

    Google Scholar 

  15. Leal, A.A.: Algorithms, creditworthiness, and lending decisions. In: Moura Vicente, D., Soares Pereira, R., Alves Leal, A. (eds.) Legal Aspects of Autonomous Systems. ICASL 2022. Data Science, Machine Intelligence, and Law, vol. 4. Springer, Cham (2024). https://doi.org/10.1007/978-3-031-47946-5_17

  16. Thomas, L.C., Ho, J., Scherer, W.: Time will tell: behavioural scoring and the dynamics of consumer credit assessment. IMA J. Manag. Math. 12, 89–103 (2001). https://doi.org/10.1093/imaman/12.1.89

    Article  Google Scholar 

  17. European Banking Authority (EBA): Guidelines on loan origination and monitoring (2020)

    Google Scholar 

  18. Anderson, R.A.: Credit Intelligence & Modelling: Many Paths through the Forecast of Credit Rating and Scoring. Oxford University Press, Oxford (2022)

    Google Scholar 

  19. Trönnberg, C.-C., Hemlin, S.: Banker’s lending decision making: a psychological approach. Manag. Financ. 38, 1032–1047 (2012). https://doi.org/10.1108/03074351211266775

    Article  Google Scholar 

  20. Anderson, R.A.: The Credit Scoring Toolkit: Theory and Practice for Retail Credit Risk Management and Decision Automation. Oxford University Press, New York (2007)

    Book  Google Scholar 

  21. Sowa, K., Przegalinska, A., Ciechanowski, L.: Cobots in knowledge work human - AI collaboration in managerial professions. J. Bus. Res. 125, 135–142 (2021). https://doi.org/10.1016/j.jbusres.2020.11.038

    Article  Google Scholar 

  22. Abbass, H.A.: Social integration of artificial intelligence: Functions, automation allocation logic and human-autonomy trust. Cogn. Comput. 11, 159–171 (2019). https://doi.org/10.1007/s12559-018-9619-0

    Article  Google Scholar 

  23. Garibay, O.O., et al.: Six human-centered artificial intelligence grand challenges. Int. J. Hum.-Comput. Interact. 39, 391–437 (2023). https://doi.org/10.1080/10447318.2022.2153320

    Article  Google Scholar 

  24. Zerilli, J., Knott, A., Maclaurin, J., Gavaghan, C.: Algorithmic decision-making and the problem of control. Minds Mach. J. Artif. Intell. Philos. Cogn. Sci. 29, 555–578 (2019). https://doi.org/10.1007/s11023-019-09513-7

    Article  Google Scholar 

  25. Fitts, P.M., et al.: Human Engineering for an Effective Air-Navigation and Traffic-Control System. Ohio State University Research Foundation Columbus (1954)

    Google Scholar 

  26. Koulu, R.: Human control over automation: EU policy and AI ethics. Euro. J. Legal Stud. 12, 9–46 (2020). https://doi.org/10.2924/EJLS.2019.019

    Article  Google Scholar 

  27. Wachter, S., Mittelstadt, B., Russel, C.: Counterfactual explanations without opening the black box. Harv. J. Law Technol. 31, 841–877 (2018)

    Google Scholar 

  28. Arrieta, A., et al.: Herrera, Francisco: Explainable Artificial Intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible. Fusion 58, 82–115 (2020). https://doi.org/10.1016/j.inffus.2019.12.012

    Article  Google Scholar 

  29. Aggarwal, N.: The norms of algorithmic credit scoring. Cambridge Law J. 80, 42–73 (2021). https://doi.org/10.1017/S0008197321000015

    Article  Google Scholar 

  30. Methnani, L., Tubella, A.A., Dignum, V., Theodorou, A.: Let me take over: variable autonomy for meaningful human control. Front. Artif. Intell. 4, 1 (2021). https://doi.org/10.3389/frai.2021.737072

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Diogo Morgado Rebelo .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Rebelo, D.M., de Andrade, F.P., Novais, P. (2025). Anthropocentric AI for EU Consumer Lending. In: Santos, M.F., Machado, J., Novais, P., Cortez, P., Moreira, P.M. (eds) Progress in Artificial Intelligence. EPIA 2024. Lecture Notes in Computer Science(), vol 14967. Springer, Cham. https://doi.org/10.1007/978-3-031-73497-7_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-73497-7_25

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-73496-0

  • Online ISBN: 978-3-031-73497-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics