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

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  3. Vierteljahrshefte zur Wirtschaftsforschung
  4. Vol. 89, Issue 4
  5. pp. 73–88

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Affeldt, P., Krüger, U. You Are What You Pay – Personal Profiling with Alternative Payment Data and the Data Protection Law. Vierteljahrshefte zur Wirtschaftsforschung, 89(4), 73-88. https://doi.org/10.3790/vjh.89.4.73
Affeldt, Pauline and Krüger, Ulrich "You Are What You Pay – Personal Profiling with Alternative Payment Data and the Data Protection Law" Vierteljahrshefte zur Wirtschaftsforschung 89.4, , 73-88. https://doi.org/10.3790/vjh.89.4.73
Affeldt, Pauline/Krüger, Ulrich: You Are What You Pay – Personal Profiling with Alternative Payment Data and the Data Protection Law, in: Vierteljahrshefte zur Wirtschaftsforschung, vol. 89, iss. 4, 73-88, [online] https://doi.org/10.3790/vjh.89.4.73
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You Are What You Pay – Personal Profiling with Alternative Payment Data and the Data Protection Law

Affeldt, Pauline | Krüger, Ulrich

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

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Article Details

Publisher Name
Duncker & Humblot
DOI
https://doi.org/10.3790/vjh.89.4.73
Language
English
Pages
16
Keywords

Author Details

Pauline Affeldt, DIW Berlin/TU Berlin

  • Pauline Affeldt is Research Associate in the Department Firms and Markets at the DIW Berlin and at the Technische Universität Berlin. She is also Fellow at the Berlin Centre for Consumer Policies (BCCP). Pauline received her PhD in 2019 from Technische Universität Berlin and holds a MSc in Economics from Tilburg University and a BA in business administration from Hochschule Bremen and Euromed Marseille. Between 2012 and 2014 she worked in economic consulting. Her research interests are in applied econometrics in the fields of industrial organization, competition policy, and regulation.
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Ulrich Krüger, Hochschule Bremen

  • Ulrich Krüger is since 2003 Professor of Business Law at the Hochschule Bremen. Served from 2016 – 2020 as Associate Dean of the School of International Business and form 2006 – 2016 as the Director of Double Degree Program “Business Studies/International Management” (BIM). He studied law at the Universities of Marburg and Munich and was post-graduate civil service trainee in Hamburg and Chicago (USA). Received his PhD from University Bremen (scholarship Deutsche Forschungsgemeinschaft), followed by five years of practical work experience as a lawyer. 2008 he was Guest Lecturer at Euromed Marseille (France) and during the spring term 2011 Research Fellow at the Universidad Valencia (Spain). His main research interests include Banking Law, International Business Law and History of German Law in the 20th Century.
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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.

Table of Contents

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

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