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You Are What You Pay – Personal Profiling with Alternative Payment Data and the Data Protection Law

You Are What You Pay – Personal Profiling with Alternative Payment Data and the Data Protection Law
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Year:    2020

Author:    Affeldt, Pauline, Krüger, Ulrich

Vierteljahrshefte zur Wirtschaftsforschung, Vol. 89 (2020), Iss. 4 : pp. 73–88

Abstract

Summary: The global trend toward cashless payment started well before the corona pandemic. Along with it, investors in the data-driven tech industry are inspired by the promise of targeted behavioral scoring based on big data. It seems economically tempting to combine these two trends by using all data generated by the payment services to create personal profiles. However, this business model conflicts with the individual’s right of informational self-determination and raises questions regarding inaccuracies, discrimination, and the non-transparency of the algorithms underlying these profiles. Our article provides a short overview over the recent economic developments in the financial service industry and a legal assessment in light of the GDPR. Not everything that is feasible with big data scoring using alternative payment data is legally allowed in Europe. Nevertheless, traditional banks could have the opportunity to improve their internal credit scoring systems and use individual customer profiles to further market their financial services.

Zusammenfassung: Nicht erst seit der Corona Pandemie gibt es weltweit den Trend zum bargeldlosen Zahlungsverkehr. Zudem beflügelt die Vorstellung eines zielgenauen Behavioral (Big Data) Scoring die Fantasien von Investoren in der Datentechnologiebranche. Es scheint ökonomisch verführerisch, beide Trends zusammenführen, wenn man alle Daten aus dem Zahlungsverkehr für ein persönliches Profil auswerten würde. Dieses Geschäftsmodell liegt jedoch mit dem Recht des Einzelnen auf informationelle Selbstbestimmung im Konflikt und wirft Fragen auf im Hinblick auf Ungenauigkeit, Diskriminierung und Intransparenz. Unser Artikel gibt einen Überblick über die ökonomische Entwicklung des Sektors und eine rechtliche Bewertung insbesondere aus Sicht der europäischen Datenschutz-Grundverordnung. Nicht alles was im Big Data Scoring mit alternativen Zahlungsdaten möglich sein könnte, ist in Europa auch rechtlich zulässig. Vor allem für die „klassischen“ Banken könnte sich gleichwohl eine Möglichkeit eröffnen ihre internen Credit Scoring Systeme zu verbessern und mit angepasst-individuellen Kundenprofilen weitere ihrer Finanzdienstleistungen zu vertreiben.

Journal Article Details

Publisher Name:    Global Science Press

Language:    English

DOI:    https://doi.org/10.3790/vjh.89.4.73

Vierteljahrshefte zur Wirtschaftsforschung, Vol. 89 (2020), Iss. 4 : pp. 73–88

Published online:    2020-10

AMS Subject Headings:    Duncker & Humblot, Duncker & Humblot

Copyright:    COPYRIGHT: © Global Science Press

Pages:    16

Keywords:    G20 G23 G28 alternative payment data personal profiling credit scoring GDPR data protection law

Author Details

Affeldt, Pauline

Krüger, Ulrich

  1. Amazon (2020): “Small Business Success in Challenging Times”. 2020 Amazon SMB Impact Report.  Google Scholar
  2. Bank for International Settlements (2019): Annual Economic Report.  Google Scholar
  3. Berg, T., V. Burg, A. Gombovi, and M. Puri (2018): “On the Rise of Fintechs – Credit Scoring using Digital Footprints”, NBER Working Papers, No. 24551.  Google Scholar
  4. Björkegren, D., and D. Grissen (2019): “Behaviour Revealed in Mobile Phone Usage Predicts Credit Repayment”, The World Economic Review, 0(0): pp. 1 – 17.  Google Scholar
  5. Caplovitz, David (1963): “The Poor Pay More: Consumer Practices of Low-Income Families”, New York: Free Press of Glencoe.  Google Scholar
  6. Citron, K. D., and F. Pasquale (2014): “The Scored Society: Due Process for Automated Predictions”, Washington Law Review 89, 2014, pp. 1 – 33.  Google Scholar
  7. Claessens, S., J. Frost, G. Turner, and F. Zhu (2018): “Fintech Credit Markets around the World: Size, Drivers and Policy Issues”, BIS Quarterly Review, September 2018, pp. 29 – 49.  Google Scholar
  8. Clifford, D., I. Graef, and P. Valcke (2019): “Pre-formulated Declarations of Data Subject Consent – Citizen-Consumer Empowerment and the Alignment of Data, Consumer and Competition Law Protections”, German Law Journal, pp. 679 – 721.  Google Scholar
  9. Consumer Financial Protection Bureau (2019): “Building a Bridge to Credit Visibility” – A Report on the CFPB’s September 2018 Building a Bridge to Credit Visibility Symposium, 2019.  Google Scholar
  10. De la Mano, M., and J. Padilla (2019): “Big Tech Banking”, Journal of Competition Law and Economics, 14(4): pp. 494 – 526.  Google Scholar
  11. European Commission (2016): “Consumer vulnerability across key markets in the European Union”, written by: London Economics, VVA Consulting and Ipsos Mori consortium  Google Scholar
  12. Financial Stability Board (2017): “Financial Stability Implications from FinTech: Regulatory and Supervisory Issues that merit Authorities’ Attention”, June.  Google Scholar
  13. Frost, J., L. Gambacorta, Y. Huang, H. S. Shin, and P. Zbinden (2019): “BigTech and the Changing Structure of Financial Intermediation”, BIS Working Papers, No. 779.  Google Scholar
  14. Gausling, T. (2019): “Datenschutzrechtliche Bewertung KI-gestützter Kommunikations-Tools und Profiling-Maßnahmen”, Zeitschrift für Datenschutz, pp. 335 – 341.  Google Scholar
  15. Golland, A. (2020): “Herausforderungen von Algorithmen im Schnittbereich von Ethik, Ökonomie und Datenschutz”, Computer und Recht, pp. 186 – 194.  Google Scholar
  16. Hoeren, T., and S. Pinell (2018): “Das neue kalifornische Datenschutzrecht am Maßstab der DS-GVO”, MultiMedia und Recht, pp. 711 – 716.  Google Scholar
  17. Hurley, M., and J. Adebayo (2016): “Credit Scoring in the Era of Big Data”. The Yale Journal of Law and Technology, 18(1): pp. 148 – 216.  Google Scholar
  18. Jagtiani, J., and C. Lemieux (2019): “The Roles of Alternative Data and Machine Learning in FinTech Lending: Evidence from the LendingClub Consumer Platform”, Federal Reserve Bank of Philadelphia, Financial Management, 48(4): pp. 1009 – 1029.  Google Scholar
  19. King, M. R. (2019): “The Competitive Threat from TechFins and BigTech in Financial Services.” Michael R. King and Richard Nesbitt (eds.), The Technological Revolution in Financial Services. Toronto: University of Toronto Press, Forthcoming.  Google Scholar
  20. Krämer, W. (2020): “Die Rechtmäßigkeit der Nutzung von Scorewerten”, Neue Juristische Wochenschrift, pp. 497 – 502.  Google Scholar
  21. Lewinski, K. v., and D. Pohl (2018): “Auskunfteien nach der europäischen Datenschutzreform”, Zeitschrift für Datenschutz, pp. 17 – 23.  Google Scholar
  22. Lindner, R. (2019): “Zahlungsabwickler Wirecard – Was wir heute machen, bringt in zehn Jahren kein Geld mehr”, 2019 October 11th, Frankfurter Allgemeine Zeitung.  Google Scholar
  23. Sachverständigenrat für Verbraucherfragen (2018): “Verbrauchergerechtes Scoring”, Gutachten des Sachverständigenrats für Verbraucherfragen.  Google Scholar
  24. Silber, N. (2017): “Discovering that the Poor Pay More: Race Riots, Poverty, and the Rise of Consumer Law”, Fordham Urban Law Journal, pp. 1319 – 1328.  Google Scholar
  25. Simitis, S., G. Hornung, and I. Spiecker (eds.) (2019): “Datenschutzrecht – Kommentar zur DSGVO mit BDSG”.  Google Scholar
  26. Stulz, M. (2019): “FinTech, BigTech, and the Future of Banks”, Journal of Applied Corporate Finance, 31(4): pp. 86 – 97.  Google Scholar
  27. Tanda, A., and C.-M. Schena (2019): “FinTech, BigTech, and Banks – Digitalisation and its Impact on Banking Business Models.”, Palgrave Macmillan Studies in Banking and Financial Institutions, Philip Molyneux (eds.).  Google Scholar
  28. Uecker, P. (2019): “Die Einwilligung im Datenschutzrecht und ihre Alternativen”, Zeitschrift für Datenschutz, pp. 248 – 251.  Google Scholar
  29. Zetzsche, D., R. Buckley, D. Arner, and J. Barberis (2017): “From FinTech to TechFin: The Regulatory Challenges of Data-Driven Finance”, EBI Working Paper Series 2017, No. 6.  Google Scholar

Section Title Page Action Price
Pauline Affeldt and Ulrich Krüger: 73
 You Are What You Pay – Personal Profiling with Alternative Payment Data and the Data Protection Law 73
1 Introduction 74
2 From Fintechs to BigTech in Financial Services 74
2.1 Developments in Financial Services Markets 74
2.2 Reasons for BigTech Entry into Financial Service Markets 76
2.3 Potential Uses of Alternative Payment Data 78
2.3.1 Alternative Payment Data 78
2.3.2 Potential Uses 79
3 The Legal Framework of Personal Profiling with Alternative Payment Data 80
3.1 GDPR: The Legal Basis in Art. 6 81
3.1.1 Consent, Art. 6 I a) 81
3.1.2 Necessity for Performance of Contract, Art. 6 I b) 73
3.1.3 Legitimate Interest, Art. 6 I f) 73
3.2 Special Rule for Scoring in German Law: § 31 BDSG 73
3.3 Lawfulness of Processing Alternative Payment Data for Personal Profiling 73
3.3.1 Internal Credit Scoring 73
3.3.2 Disclosure for External Credit Scoring 73
3.3.3 Customer Profiling for Marketing of Own Financial Services 73
3.3.4 Customer Profiling for the Marketing of Other Services, Products or External Advertising 73
3.4 Personal Profiling and the Protection Against Automated Individual Decision-making 73
3.4.1 Decisions Based on Internal Credit Scoring 73
3.4.2 Customer Profiles for Financial Services 73
3.5 Data Protection Impact Assessment 73
3.6 Result 73
4 Conclusion 73
5 References 73