Payments data: do consumers want banks to keep them in a safe or turn them into gold?

ABSTRACT

For policymakers seeking to protect financial stability and financial institutions, it is key to know consumers’ attitudes towards payments data usage. This article provides detailed insight into these attitudes based on unique surveys held among Dutch consumers. Attitudes towards payments data usage by banks and the related factors depend on the purpose of the data use. For example, most people do not mind if their payments data are being used to enhance security but do not want these data to be shared with restaurants to get special offers. The selling of payments data by banks to other companies would result in a significant decline of trust in banks.

KEYWORDS:

• Privacy
• payments data
• consumer attitudes
• banks
• trust

JEL CLASSIFICATION:

• D14
• D12

Acknowledgments

I am grateful for discussions with colleagues at DNB and their feedback on this research. I thank Ikrame Azaaj and Irene Zondervan (Motivaction) and Miquelle Marchand (CentERdata) for arranging the surveys, DNB’s Translations team for providing linguistic services and colleagues of DNB’s Supervision Policy division for sharing the trust survey data. I am also thankful for comments received from the researchers and policymakers that joined my presentations at the ECB, DNB, the TIBER 2017 Symposium on Psychology and Economics, and the ECB and Banca d’Italia joint conference “Digital transformation of the retail payments ecosystem”. In addition, I am grateful to three anonymous reviewers for their thoughtful comments and suggestions. The views expressed in this paper do not necessarily reflect the views of DNB or those of the Eurosystem.

Disclosure statement

No potential conflict of interest was reported by the author.

Notes

¹ See the 1948 Universal Declaration of Human Rights at www.un.org/en/universal-declaration-human-rights/. URL last accessed on 15 August 2019.

² These are some examples of newspaper articles devoted to privacy: https://www.nytimes.com/topic/subject/privacy, https://www.economist.com/briefing/2019/03/23/big-tech-faces-competition-and-privacy-concerns-in-brussels, https://www.economist.com/united-states/2019/02/28/congress-is-trying-to-create-a-federal-privacy-law. URLs last accessed on 15 August 2019.

³ For example, the General Data Protection Regulation (GDPR, Regulation (EU) 2016/679) has been in effect as of May 2018. One of its goals is to give citizens back control over their personal data. These data can only be gathered legally under strict conditions and only if the purpose of data collection is legitimate and if misuse is prevented.

⁴ There is only some general indication that financial data is regarded as personal (European Commission 2011) and sensitive (Pew Research Center 2014), and that benefits can trigger more data sharing (Accenture 2017).

⁵ For more information on the CentERpanel, see Teppa and Vis (2012) and www.centerdata.nl/en/projects-by-centerdata/the-centre-panel. URL last accessed on 15 August 2019.

⁶ See www.centerdata.nl/en/publications for all publications. URL last accessed on 15 August 2019.

⁷ For some questions, the response rate is slightly higher (up to 82.4%). The DTS is an annual survey.

⁸ The Banking Confidence Monitor is available at https://www.nvb.nl/media/1280/nvb-banking-confidence-monitor-2018.pdf. URL last accessed on 15 August 2019.

⁹ Weighted results are available upon request and do not differ much from the unweighted results. The average answer given differs 0.1 at the most. There is no difference in the ranking.

¹⁰ ‘iDEAL is an online payment method that enables consumers to pay online through their own bank.’ (https://www.ideal.nl/en/).

¹¹ Respondents for which the income is unknown are excluded from the analysis.

¹² Appendix A with a description of the variables, summary statistics and correlation matrices is available upon request.

¹³ This factor was not included in the 2010 and 2012 surveys that Jansen, Mosch, and Van der Cruijsen (2015) used.

¹⁴ The difference between the weighted and unweighted results is small. The weighted results are as follows: completely: 27%; very strongly: 22%, strongly: 21%, neutral: 13%, limited: 4%, very limited: 2%, absolutely not: 3%, I do not know: 6%.

¹⁵ Other factors were: (1) ‘The explanations related to financial products of your bank(s) are lengthy and difficult to read.’, (2) ‘The share price of your bank(s) drops (drop) sharply.’, (3) ‘Family and friends are advising you to withdraw funds from your bank(s).’, (4) ‘Your bank(s) gets (get) government support.’, (5) ‘Your bank(s) is (are) repeatedly a target of cybercrime.’.

¹⁶ ‘I do not know’ answers are set at missing.

¹⁷ Alternatively, people with low trust may understand the question differently than people with high trust. For example, people with high trust in banks may think that the question refers only to non-sensitive personal information and that the bank would first ask for their consent, while people with low trust in banks may think that the bank ruthlessly sells all data without asking for their consent.

¹⁸ The 2019 DTS data show a strong and significant positive correlation between trust in the capacity to repay deposits and more general measures of trust in the financial sector. The results are available upon request.

¹⁹ Appendix A includes a description of the variables, summary statistics and a correlation matrix and is available upon request.

²⁰ The reported estimated coefficients are the ordered logit regression coefficients, but the interpretation of the results is mainly based on marginal effects.