Big Data and algorithmic governance: the case of financial practices

ABSTRACT

Big Data and algorithmic governance are transforming traditional institutions and media of transnational governance in manners that hold important implications for power, accountability and effectiveness. Drawing on actor-network theory, this paper contrasts utopian or dystopian views on the increasing presence of Big Data in contemporary financial practices. We scrutinise the emerging impacts of Big Data in the public governance of private banks in the Basel III arrangements, private governance of individual actors in credit scoring and anarchic competitive governance of markets in high-frequency trading. Our findings reveal varied and emergent forms of governance through, with and by algorithms.

KEYWORDS:

• Algorithms
• bank capital adequacy
• Big Data
• credit scoring
• high-frequency trading

Acknowledgments

We thank Regine Paul and participants in the April 2015 Sociology of Risk and Uncertainty Mid-Term Conference in Stuttgart for helpful comments on an earlier draft of this paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes on contributors

Malcolm Campbell-Verduyn completed a doctoral degree in International Relations at McMaster University in 2015 and is currently an SSHRC Postdoctoral Fellow at the Balsillie School of International Affairs. His research combines a general focus on language, moralities and ideas in the global political economy with a specific interest in the roles of private actors, technologies and technical artefacts in contemporary global governance. His research has appeared in Business and Politics, Competition and Change, Global Society, as well as the Journal of European Public Policy.

Marcel Goguen is a Ph.D. candidate (ABD) in Political Science at McMaster University, Hamilton, Canada. His research focuses on the interstice between international relations theory, political theory and global finance. Under the supervision of Tony Porter, he is participating in research funded by the Social Sciences and Humanities Research Council of Canada, on ‘Numbers in the changing fabric of global governance’.

Tony Porter is Professor of Political Science, McMaster University, Hamilton, Canada. His most recent books are Transnational Financial Associations and the Governance of Global Finance: Assembling Power and Wealth (2013), coauthored with Heather McKeen-Edwards, and his edited Financial Regulation after the Global Financial Crisis (2014), both with Routledge. He is currently conducting research, funded by the Social Sciences and Humanities Research Council of Canada, on ‘Numbers in the changing fabric of global governance’.

Notes

1. Literature on web search engines as governance mechanisms, such as Dobusch (2012), is cited more extensively in Hansen and Porter (2015).

2. Which we differentiate from what has elsewhere been identified as ‘algorithmic regulation’ (O’Reilly 2013).

3. See for instance Executive Office of the President (2014: 4) and Winter (2011: 2). Further characteristics have been added, such as ‘veracity’, ‘viability’ and ‘value’ (e.g. Amoore and Piotukh 2015: 342). Yet it has been argued that these ‘wanna Vs’ are not ‘intrinsic, definitional Big Data properties’ (Grimes 2013).

4. See Kitchin and Lauriault (2015) for a thorough overview of the contrasts and interactions between big and ‘small’ data.

5. That amongst others include crowd-funding platforms like the CrowdBnk, peer-to-peer lenders like RateSetter, and open banking applications like The Open Bank Project. See Bonina (2013) for an overview.

6. Lammerant et al. (2014) provide a thorough overview of public policies pertaining to Big Data in various jurisdictions and Introna (2016: 28–9) summarises suggestions for how Big Data algorithms might be formally governed.

7. Alternatively labelled by Introna (2016: 30) as governance of algorithms and governance through algorithms, respectively.

8. Haldane (2011) for instance notes that this type of IT upgrading was a motivation for Basel II.

9. As stated by the Basel Committee on Banking Supervision (2013a):

one of the most significant lessons learned from the global financial crisis that began in 2007 was that banks’ information technology (IT) and data architectures were inadequate to support the broad management of financial risks. Many banks lacked the ability to aggregate risk exposures and identify concentrations quickly and accurately at the bank group level, across business lines, and between legal entities … this had severe consequences to the banks themselves and to the stability of the financial system as a whole.

10. For further examples see Spectrum (n.d.).

11. On tweets see for instance Cerchiello and Guidici (2014).

12. On initiatives at the FSB see LEI ROC (2013). On the conflict between US initiatives and G20/FSB initiatives for LEIs see Grody (2012).

13. For an overview see McLannahan (2016).

14. Nearly half of employers in the US rely on credit checks for hiring decisions, see Rivlin (2013).

15. For a detailed overview of HFT per market see Cardella et al (2014)

16. Estimated by an informal survey to be used by 10 per cent of buy-side traders (Automated Trader 2015).

Additional information

Funding

Research support from Social Sciences and Humanities Research Council of Canada [grant number 410-2011-2376] is gratefully acknowledged.