Abstract
This chapter shows that networks widely distribute both big data and fintech applications, allowing fintech firms to execute their activities all around the world. Furthermore, the unbundling of the intermediation process into chains of transactions is leading banking and financial activities outside the scope of supervision. Thus, this chapter investigates the reason fintech applications do not appear to be regulatory-neutral. It also investigates the ‘acts of fintech’ and the responsibility of individuals in developing network of operations and concludes by assessing the current need for transparency, with regard to both the circulation of financial information in the market and the mitigation of the bargaining power in bilateral transactions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
In particular, the analysis refers to ECB. 2018. “Guide to assessments of fintech credit institution licence applications”.
- 2.
In addition, see the consideration of Marcacci, A. 2017. “Digitally-provided Financial Services under EU Law: Overcoming the Current Patchwork of Europeanized Private International Law and Sectorially-harmonized National Private Laws” Studi sull’integrazione europea; the author begins his analysis from the forward-looking idea of a pan-European private law code merging national legal traditions—both civil and common—and, eventually, replacing them (horizontal approach).
- 3.
Obviously, the role of private law is preeminent; see Paech, P. 2016. “The Governance of Blockchain Financial Networks” Modern Law Review p. 1073 ff.
- 4.
See Buchak, G. and Matvos, G. and Piskorski, T. and Seru, A. 2017. “Fintech, Regulatory Arbitrage, and the Rise of Shadow Banks”, NBER Working Paper no 23288; the authors studied how two forces, regulatory differences and technological advantages, contributed to this growth.
- 5.
See European Commission’s Action Plan on how to harness the opportunities presented by technology-enabled innovation in financial services (FinTech), 8 march 2018.
See also Vezzoso, S. 2018. “Fintech, Access to Data, and the Role of Competition Policy” “Competition and Innovation”, São Paulo, on the revised EU Directive on payment services in the internal market (PSD2) that entered into application on 13 January 2018 and introduced a sector-specific data portability rule dubbed access to account, or XS2A.
- 6.
It is remarked by “The Promise of FinTech – Something New Under the Sun?”, speech given by Mark Carney, Governor of the Bank of England, Chair of the Financial Stability Board Deutsche Bundesbank G20 conference on “Digitising finance, financial inclusion and financial literacy”, Wiesbaden, 25 January 2017.
- 7.
In particular, see Capriglione F. and Sacco Ginevri, A. “Metamorfosi della governance bancaria” Milano, 2019 on the issues in banking and the management of its risks.
- 8.
See Maume, P. 2018. “Reducing Legal Uncertainty and Regulatory Arbitrage for Robo-Advice” European Company and Financial Law Review, which argues that robo-advisers provide investment advice within the meaning provided by MiFiDII. Hence, they are subject to authorization by the national regulator and ongoing conduct requirements. It might be tempting to introduce regulatory sandboxes to address the persisting legal uncertainties in practice, but such a regulatory change does not seem likely in the near future. Instead, regulatory arbitrage should be reduced by a uniform application of the MiFiDII framework throughout the EU to the fintech business also.
- 9.
Moreover, see Varian, H. R. 2014. “Big Data: New tricks for econometrics” Journal of Economic Perspectives. P. 3 ff.; the author began his analysis by describing how computers are now involved in many economic transactions and can capture data associated with these transactions, which can then be manipulated and analysed. The author also described a few of these tools for manipulating and analysing big data.
- 10.
It refers specifically to FSB 2017, “Artificial intelligence and machine learning in financial services,” November, on the need of technological applications to deal with big data and greater computing power.
- 11.
Furthermore, see Bholat, D. 2015. “Big data and central banks” Big Data & Society, which recalled one standard definition of Big Data: “It is data displaying one or more of the following characteristics: (a) These data are of high volume, often because data are reported on a granular basis, that is, item-by-item, for example, loan-by-loan or security-by-security; (b) these data are of high velocity, because these data are frequently updated and, at the limit, collected and analysed in real-time; (c) these data are qualitatively various, meaning they are either non-numeric, such as text or video, or they are extracted from novel sources, such as social media, Internet search records or biometric sensors.”
- 12.
Moreover, see Cœuré, B. 2017. “Policy analysis with big data. Speech by the member of the Executive Board of the ECB, at the conference on Economic and Financial Regulation in the Era of Big Data”, Banque de France, Paris, 24 November who recalls Tissot, B. 2017. “Big data and central banking”, IFC Bulletin No 44; indeed, big data usually refers to unstructured data resulting from non-statistical activity and/or structured data that create operational challenges owing to their size or complexity; see for example, Nymand-Andersen, P. 2015. “Big data: the hunt for timely insights and decision certainty: Central banking reflections on the use of big data for policy purposes”, IFC Working Paper No 14.
- 13.
It is worth considering the authors that went deep into this issue; see Dehaene, S. 2009 “Origins of Mathematical Intuitions: The Case of Arithmetic”. Annals of the New York Academy of Sciences, p. 1156; Burr, D. and Ross, J. 2008 “A Visual Sense of Number” Current Biologyp. 425 ff.; Izard, V. and Dehaene, S. 2008. “Calibrating the mental number line” Cognition p. 1221 ff.; Burr, D. and Ross, J. 2008 “Response: Visual number” Current Biology p. 18 f.; Durgin, F. H 2008 “Texture density adaptation and visual number revisited”. Current Biology.
- 14.
It recalls FSB 2017. “Artificial intelligence and machine learning in financial services. Market developments and financial stability implications”, p. 4 ff. In addition, see Ward, J. S. and Barker, A. 2013 “Undefined By Data: A Survey of Big Data Definitions” Cornell University research paper no. arXiv:1309.5821.
- 15.
It refers to American Psychological Association 2013 “Glossary of psychological terms”. Apa.org. Retrieved 2014-08-13.
- 16.
In particular, it recalls Schumpeter, J. 1942, “Capitalism, socialism and democracy, (ed. 1994, Abingdon-on-Thames, p. 83.
- 17.
See Arrow, K. J. 1971, “Essays in theory of risk bearing”, Chicago, p. 224.
See, on this topic, Savelyev, A. 2016 “Contract Law 2.0: ‘Smart’ Contracts As the Beginning of the End of Classic Contract Law” Higher School of Economics Research Paper No. WP BRP 71/LAW/2016, about the issues in aligning the powers of the government with distributed technologies.
- 18.
See Williamson, O.E. 1985 “The economic institution of capitalisms”, London, p. 404.
- 19.
It refers to Posner, R. 2008 “How Judges think”, Harvard, p. 230. See also Hermalin, B. E. and Katz, A. W. and Craswell, R. 2006 “The Law and Economics of Contracts” Columbia Law and Economics Working Paper No. 296, in respect of four topic areas that correspond to the major doctrinal divisions of the law of contracts. These areas include freedom of contract (i.e., the scope of private power to create binding obligations), formation of contracts (both the procedural mechanics of exchange and the rules that govern pre-contractual behaviour), contract interpretation (what consequences follow when agreements are ambiguous or incomplete) and enforcement of contractual obligations.
This refers also to Boschetti, B. 2016. “Soft law e normatività: un’analisi comparata” Rivista della Regolazione dei mercati, p. 32 ff.; the author’s analysis highlights the variety of phenomena that fall into the soft law category and the many roles played by soft law at different levels (including the interinstitutional one). This author pointed out that it is precisely in this wider context that the regulative and regulatory functions of soft law come to light. In all of the legal systems examined, soft law shows itself to be an extraordinary instrument, playing a key role in ensuring and guaranteeing the effectiveness, balance and dynamicity of the legal system itself. Furthermore, the author refers to these multiple roles that are strengthened and underpinned by legislators, who implement mechanisms that not only permit soft law to accede to the field of normativity, but also encourage compliance with it by increasing the costs of non-compliance by the imposition of duties, such as the duty to report non-compliance, to give reasons for non-compliance, to disclose the names of those who are not in compliance with soft law, thereby ensuring the effectiveness of soft law and, ultimately, the regulatory process itself.
- 20.
It recalls Bank of England 2014 “Strategic plan: Background information”.
- 21.
“Big data is the term increasingly used to describe the process of applying serious computing power—the latest in machine learning and artificial intelligence—to seriously massive and of- ten highly complex sets of information”, see “The Big Bang: How the Big Data Explosion Is Changing the World – Microsoft UK Enterprise Insights Blog – Site Home – MSDN Blogs”.
- 22.
See Wibisono, O. and Ari, H. D. and Widjanarti, A. and Andhika Zulen, A. and Tissot, B. “The use of big data analytics and artificial intelligence in central banking” IFC Bulletin no. 50, which analysed the various aspects related to the use of big data (associated techniques by central banks), and covered three main aspects: (1) an assessment of the main big data sources and associated analytical techniques that are relevant for central banks; (2) the insights provided by big data for economic policy, with an overview of concrete central bank projects aiming at improving statistical information, macroeconomic analysis and forecasting, financial market monitoring and financial risk assessment; and (3) the use of big data in crafting central bank policies, including organisational aspects and related challenges.
- 23.
See Williamson, O. E. 1985. “The economic institution of capitalisms”, London, p. 399, which recalls Kronman, A. T. 1985, “Contract law and the state of nature” Journal of law, economics and organization
- 24.
It recalls Bholat, D 2013. “The future of central bank data”. Journal of Banking Regulation p. 185 ff.; see also Buzzi, F. 2005, “La teologia per il diritto dell’uomo e dei popoli” Iustitia p. 269 ff., and Bianco Alberto, A. 1981, “La grazia perfeziona la natura. Il fondamento scritturistico del diritto naturale” Studi cattolici, p. 266 ff.
- 25.
This remarks the conclusions of Edwards, l. 2016. “Privacy, Security and Data Protection in Smart Cities: A Critical EU Law Perspective” European Data Protection Law Review, which argues that smart cities combine the three greatest current threats to personal privacy, with which regulation has so far failed to deal effectively; the Internet of Things (IoT) or “ubiquitous computing”; “Big Data”; and the Cloud. He seeks solutions both from legal institutions such as data protection law and from “code”, proposing in particular from the ethos of Privacy by Design, a new “social impact assessment” and new human: computer interactions to promote user autonomy in ambient environments.
- 26.
It includes a reference to Diebold, F. X. 2012. “On the Origin(s) and Development of the Term ‘Big Data’” PIER Working Paper No. 12-037, which highlighted that the first significant academic references (independent of each other and of Silicon Graphics) appear to be Weiss and Indurkhya (1998) in computer science and Diebold (2000) in statistics/econometrics. Douglas Laney of Gartner also produced insightful work (again unpublished and non-academic) slightly later. According to this author, the term is now firmly entrenched, but the phenomenon continues unabated, and the discipline is still emerging.
- 27.
See Kache, F. 2015. “Dealing with digital information richness in supply chain management. A review and a big data analytics approach” Kassel, who questioned what are the implication of these analytics on information usage at corporate level.
See also Beyer, M. A. and Laney, D. 2012. “The importance of big data: A definition”. Stamford, CT: Gartner.
- 28.
Therefore, this part of the research will not go deeply into the analysis of the regulation provided for the protection of the above rights, due to the current pro-sharing behaviour of people.
On this point, see Tene, O. and Polonetsky, J. 2013. “Big Data for All: Privacy and User Control in the Age of Analytics” Northwestern Journal of Technology and Intellectual Property, which highlighted that data are now available for analysis in raw form, escaping the confines of structured databases and enhancing researchers’ abilities to identify correlations and conceive of new, unanticipated uses for existing information. In addition, the increasing number of people, devices and sensors that are now connected by digital networks has revolutionized the ability to generate, communicate, share and access data.
See also Rubinstein, I. 2012. “Big Data: The End of Privacy or a New Beginning?” NYU School of Law, Public Law Research Paper No. 12-56; Wachter, S. and Mittelstadt, B. 2018. “A Right to Reasonable Inferences: Re-Thinking Data Protection Law in the Age of Big Data and AI” Columbia Business Law Review, which argued that a new data protection right, the ‘right to reasonable inferences’, is needed to help close the accountability gap currently posed by ‘high risk inferences’, meaning inferences drawn from big data analytics that damage privacy or reputation, or have low verifiability in the sense of being predictive or opinion-based while being used in important decisions.
It is worth mention that—according to these authors—this right would require ex-ante justification to be given by the data controller to establish whether an inference is reasonable. Hence, this disclosure would address (1) why certain data form a normatively acceptable basis from which to draw inferences; (2) why these inferences are relevant and normatively acceptable for the chosen processing purpose or type of automated decision; and (3) whether the data and methods used to draw the inferences are accurate and statistically reliable. However, the ex ante justification is bolstered by an additional ex post mechanism enabling unreasonable inferences to be challenged.
- 29.
In addition, it is worth considering the analysis of Dijcks, J. P. 2012. “Oracle: Big data for the enterprise”. Oracle White Paper
- 30.
In addition, it recalls the considerations of Taylor, L. and Schroeder, R. and Meyer, E. 2014. “Emerging practices and perspectives on Big Data analysis in economics: Bigger and better or more of the same?” Big Data & Society
- 31.
Reference is made to Google, 2013. “Google Trends for Big Data”. It is worth considering also Tene, O. 2007, “What Google Knows: Privacy and Internet Search Engines” Utah Law Review; Preis, T. and Moat, H. S. and Stanley, H. E. 2013, “Quantifying Trading Behavior in Financial Markets Using Google Trends” Scientific Reports p. 1684 ff.; Newman, N. 2014, “Search, Antitrust and the Economics of the Control of User Data” Yale Journal on Regulation and their focus on the control of personal data by corporations, which can entrench monopoly power in an economy shaped increasingly by the power of big data.
- 32.
In particular, it can be noted that Cohen, J. E. 2012 “What Privacy Is For” Harvard Law Review, considered that the efforts to repackage pervasive surveillance as innovation—under the moniker “Big Data”—are better understood as efforts to enshrine the methods and values of the modulated society at the heart of the system of knowledge production. In short, privacy incursions harm individuals, but not only individuals. Privacy incursions in the name of progress, innovation and ordered liberty jeopardize the continuing vitality of the political and intellectual culture.
- 33.
See Banterle, F. 2018, “Data Ownership in the Data Economy: A European Dilemma” EU Internet Law in the digital era, whose analysis refers to the question arising where a multitude of actors interact in the elaboration of data: Who owns the data? Indeed, according to this author, while organized data sets can be subject to intellectual property rights, and the use of personal data is regulated by data protection laws, this question particularly applies to raw (machine-generated) data, which are increasing their value as a source of precious insights and fall outside the scope of classical ownership/property schemes.
- 34.
See Grossman, R. and Siegel, K. 2014. “Organizational Models for Big Data and Analytics” Journal of Organization Design, p. 20 ff.
See also Kitchin, R. 2014, “Thinking Critically About and Researching Algorithms” The Programmable City Working Paper 5, which starts from the consideration that the era of ubiquitous computing and big data is now firmly established, with more and more aspects of the everyday lives being mediated, augmented, produced and regulated by digital devices and networked systems powered by software.
- 35.
It refers to Cœuré, B. 2017. “Policy analysis with big data. Speech by the Member of the Executive Board of the ECB, at the conference on Economic and Financial Regulation in the Era of Big Data”, Banque de France, Paris, 24 November 2017.
- 36.
It recalls also Maume, P. 2018. “Reducing Legal Uncertainty and Regulatory Arbitrage for Robo-Advice” European Company and Financial Law Review, which highlighted that regulators and courts should also be aware that software replacing human advisers diverges from the basic idea of human interaction that forms the basis of contract law. Indeed, it seemed that investment firms were able to use new technology in the services they provide. However, as this means introducing new risks for investors, the investment firm should be subject to a strict liability regime for failures of the respective technology (e.g. the unavailability of the service).
- 37.
Moreover, see Gebauer, S. and Mazelis, F. 2019. “Macroprudential Regulation and Leakage to the Shadow Banking Sector” DIW Berlin Discussion Paper No. 1814, which developed a model that differentiates between regulated, monopolistically competitive commercial banks and a shadow banking system that relies on funding in a perfectly competitive market for investments.
- 38.
It is worth referring to the research of Barth, J. R. and Caprio, G. and Levine, R. E. 2001. “The Regulation and Supervision of Banks Around the World: A New Database” World Bank Policy Research Working Paper No. 2588 for a comprehensive analysis of the following aspects of banking: entry requirements, ownership restrictions, capital requirements, activity restrictions, external auditing requirements, characteristics of deposit insurance schemes, loan classification and provisioning requirements, accounting and disclosure requirements, troubled bank resolution actions, and (uniquely) the quality of supervisory personnel and their actions.
- 39.
Furthermore, see Ait-Sahalia, Y. and Karaman, M. and Mancini, L. 2018. “The Term Structure of Variance Swaps and Risk Premia” Swiss Finance Institute Research Paper No. 18-37 about a model-free analysis that reveals a significant price jump component in variance swap rates.
- 40.
It recalls Tene, O. and Polonetsky, J. 2013. “Big Data for All: Privacy and User Control in the Age of Analytics” Northwestern Journal of Technology and Intellectual Property, which pointed out that data creates enormous value for the world economy, driving innovation, productivity, efficiency and growth. At the same time, the ‘data deluge’ presents privacy concerns which could stir a regulatory backlash dampening the data economy and stifling innovation. In order to craft a balance between beneficial uses of data and in individual privacy, policymakers must address some of the most fundamental concepts of privacy law, including the definition of ‘personally identifiable information’, the role of individual control, and the principles of data minimization and purpose limitation.
These authors emphasized the importance of providing individuals with access to their data in usable format, in order to conclude that this will let individuals share the wealth created by their information and incentivize developers to offer user-side features and applications harnessing the value of big data. Hence, where individual access to data is impracticable, data are likely to be deidentified to an extent sufficient to diminish privacy concerns. This research shows, in addition, that organizations should be required to disclose their decisional criteria, since in a big data world it is often not the data but rather the inferences drawn from them that give cause for concern.
- 41.
It refers to uncertainty “in a sense radically distinct from the familiar notion of risk”, hence the option to report the first term only to non-measurable criticalities, and the second to the predictable and measurable negative events (the so-called expected losses). See Knight, F. 1921 “Risk, Uncertainty, and Profit”, (Ed. 2009) Cambridge, where it is highlighted that “the difficulties … arisen from a confusion of ideas which goes deep down into the foundations of our thinking.”
- 42.
In particular, it can be recalled Morellec, E. and Wang, N. 2004. “Capital Structure, Investment, and Private Benefits of Control” Simon Business School Working Paper No. FR04-17, which examined the impact of the opportunistic behaviour of the controlling shareholder on investment and financing decisions.
- 43.
These considerations recall Buchak, G. and Matvos, G. and Piskorski, T. and Seru, A. 2017. “Fintech, Regulatory Arbitrage, and the Rise of Shadow Banks”, NBER Working Paper no 23288, with respect to the conclusion that a difference—in difference tests exploiting geographical heterogeneity—induced by four specific increases in regulatory burden—capital requirements, mortgage servicing rights, mortgage-related lawsuits, and the movement of supervision to Office of Comptroller and Currency following closure of the Office of Thrift Supervision—reveals that traditional banks contracted in markets where they faced more regulatory constraints; shadow banks partially filled these gaps.
- 44.
It is worth adding that Brito, J. and Shadab, H. B. and Castillo O’Sullivan, A. 2014. “Bitcoin Financial Regulation: Securities, Derivatives, Prediction Markets, and Gambling” Columbia Science and Technology Law Review suggested that—to the extent regulation and enforcement becomes more costly than its benefits—policymakers should consider and pursue strategies consistent with that new reality, such as efforts to encourage resilience and adaptation.
- 45.
In addition, see Chesbrough, H. and Bogers, M. 2014. “Explicating Open Innovation: Clarifying an Emerging Paradigm for Understanding Innovation” “New Frontiers in Open Innovation”, Oxford who clarify and develop the conceptualization of open innovation, which can be defined as a distributed innovation process based on purposively managed knowledge flows across organizational boundaries, using pecuniary and non-pecuniary mechanisms in line with the organization’s business model.
See also Sandulli, F. D. and Chesbrough, H. 2009. “The Two Sides of Open Business Models” SSRN research paper no. 1325682 about the role of key resources, and how they can be aggregated into open business models.
- 46.
It includes the conclusion of Arner, D. W. and Barberis, J. N. and Buckley, R. P. 2016. “FinTech, RegTech and the Reconceptualization of Financial Regulation” University of Hong Kong Faculty of Law Research Paper No. 2016/035, which argued that, whilst the principal regulatory objectives (e.g. financial stability, prudential safety and soundness, consumer protection and market integrity, and market competition and development) remain, their means of application are increasingly becoming inadequate. Regtech developments are leading towards a paradigm shift necessitating the reconceptualization of financial regulation.
- 47.
It is remarkable that Maume, P. 2018, “Regulating Robo-Advisory” Texas International Law Journal, referred to them as internet-based advisory services that use algorithms to create investment recommendations with no human input. The author argues that robo-advisory is essentially different from traditional financial advice. Nevertheless, it demonstrates that current regulation, in particular the European Union framework for financial intermediaries, is able to address most of the resulting issues. The core conclusion is that, in applying the existing rules to robo-advisors, the rules should not be interpreted to create a level-playing field for all market participants.
See also Maume, P. 2018. “Reducing Legal Uncertainty and Regulatory Arbitrage for Robo-Advice” European Company and Financial Law Review, which pointed out that the nature of the interaction between client and machine raises many legal questions under the applicable EU regulation.
- 48.
The analysis may include also a reference to Sanz Bayón, P. and Vega, L. G. 2018. “Automated Investment Advice: Legal Challenges and Regulatory Questions” Banking & Financial Services Policy Report, which examined the basics of the automated investment advice (Robo-Advisory), attempted a definition and a taxonomy, lays the necessary groundwork for analysing this phenomenon, and offered a broad framework to allow a better understanding of the current legal situation of Robo-Advisory.
- 49.
See, on this point, Gomber, P. and Kauffman, R. J. and Parker, C. and Weber, B. 2017. “ On the Fintech Revolution: Interpreting the Forces of Innovation, Disruption and Transformation in Financial Services” Journal of Management Information Systems, p. 220 ff.; the authors discuss: (1) operations management in financial services, and the changes that are occurring there; (2) technology innovations that have begun to leverage the execution and stakeholder value associated with payments settlement, cryptocurrencies, blockchain technologies and cross-border payment services; (3) multiple fintech innovations that have impacted lending and deposit services, peer-to-peer (P2P) lending and the use of social media; (4) issues with respect to investments, financial markets, trading, risk management, robo-advisory and related services that are influenced by blockchain and fintech innovations.
- 50.
It recalls Maume, P. 2017. “In Unchartered Territory – Banking Supervision Meets Fintech” Corporate Finance p. 373 ff., which lays out the current German legal framework, considering that, in the EU credit institutions and financial service providers, including outsourcing, are regulated under EU laws, most notably Directive 2014/65/EU (‘MiFiD2’), Directive 2013/36/EU (‘CRD IV’) and Regulation (EU) 575/2013 (‘CRR’). Within this framework, the author discusses the trend towards cooperation between banks and fintechers, the applicable legal framework, potential banking license requirements and the obstacles of outsourcing of banking functions to fintechers under the current framework.
- 51.
See Zirpoli, F. and Becker, M. C. 2008. “Organizing Complex Product Development: Outsourcing, Performance Integration and the Role of Product Architecture” SSRN research paper no. 1087236, which questions the key micro-organizational decisions for seizing the benefits of a networked innovation strategy. The authors show that managers can greatly benefit from focusing their attention on the organizational aspects of leveraging external sources of innovation and adjusting their innovation strategy, including ‘make or buy’ choices, accordingly. Hence, they propose to consider strategic and micro-organizational decisions as tightly coupled and mutually influencing in the context of complex product development.
- 52.
It remarks the findings of Ban, G. Y. and Rudin, C. 2018. “The Big Data Newsvendor: Practical Insights from Machine Learning” Operations Research 67 who investigated the data-driven newsvendor problem when one has n observations of p features related to the demand as well as historical demand data. The authors proposed solving the ‘Big Data’ newsvendor problem via single-step machine learning algorithms. Specifically, they refer to algorithms based on the Empirical Risk Minimization (ERM) principle, with and without regularization, and an algorithm based on Kernel-weights Optimization (KO).
- 53.
See Marcacci, A. 2017. “Digitally-provided Financial Services under EU Law: Overcoming the Current Patchwork of Europeanized Private International Law and Sectorially-harmonized National Private Laws” Studi sull’integrazione europea.
- 54.
See Torresetti, R. and Nordio, C., 2014. “Scaling Operational Loss Data and Its Systemic Risk Implications” SSRN Research paper no. 2360483.
It is worth considering Jacobsen, S. F. and Tschoegl, A. E. 1997. “The Norwegian Banks in the Nordic Consortia: A Case of International Strategic Alliances in Banking” SSRN research paper no 52068. On this topic, see also Mainelli, M. and Smith, M. 2015. “Sharing Ledgers for Sharing Economies: An Exploration of Mutual Distributed Ledgers (Aka Blockchain Technology)” Journal of Financial Perspectives.
- 55.
It is worth considering the conclusions of Maine, H. J. S. 1861. “Ancient law” (ed. 2005) p. 170.
- 56.
Let us recall Greco, P. 1930. “Le operazioni di banca”, Padova on the tendence to form special kind of agreement to regulate the operations of banking and finance, who admitted that looking for the essential element in the economic function and the corresponding legal structure of bank contracts, bringing them back and framing them in the general system of legal acts, are among the most difficult tasks, but also the most attractive for the lawyer.
- 57.
See Lobel, O. 2016. “The Law of the Platform” Minnesota Law Review, which assumed that unsurprisingly the platform economy defies conventional regulatory theory. Indeed, the author poses a foundational inquiry: Do the regulations carry over to the platform economy?
- 58.
See, in this respect, Jagtiani, J. A. and Lemieux, C. 2017. “Fintech Lending: Financial Inclusion, Risk Pricing, and Alternative Information” FRB of Philadelphia Working Paper No. 17-17, whose conclusions highlighted that Fintech has been playing an increasing role in shaping financial and banking landscapes.
- 59.
In addition, see Van Loo, R. 2018. “Making Innovation More Competitive: The Case of Fintech” UCLA Law Review, which highlighted that innovation has raised the stakes for fixing this structural flaw. However, the author suggests that if allowed to compete fully, financial technology challengers could bring large consumer welfare advances and reduce the size of “Too Big To Fail” banks, thereby lessening the chances of a financial crisis. Furthermore, if allowed to grow unchecked, either fintech start-ups or the big banks acquiring them may reach the size of technology giants, thereby increasing systemic risk. Hence, the author concludes that if the goal is to benefit consumers, strengthen markets, or prevent crises, a reallocation of competition authority would better position regulators to navigate the future of innovation.
- 60.
Let us recall the Italian experience on the unlawful banking in absence of the relevant authorization; see Severino, P. 2000. “Le disposizioni integrative e correttive del Testo Unico delle leggi in materia bancaria e creditizia. Il quadro sanzionatorio: innovazioni nelle fattispecie e nella procedura applicativa” Diritto della banca e del mercato finanziario; Capriglione, F. 1997. “La problematica della “banca di fatto” dopo il d.lg. 385/1993” La nuova giurisprudenza civile commentata; Cantone, R. 1996. “L’abusivismo finanziario: esperienze da un’indagine giudiziaria. Nota a Cass. sez. V pen. 6 ottobre 1995” Cassazione penale, p. 3122 ff.; Criscuolo, L. “L’esercizio abusivo di attività finanziaria: profili giuridici e strumenti di contrasto” Cassazione penale, 1996, p. 1334.
- 61.
Moreover, see Capriglione, F. and Masera, R. 2016. “Bank Corporate Governance: A New Paradigm” Open Review of Management, Banking and Finance. See also Canova, T. A. 2009. “Financial Market Failure as a Crisis in the Rule of Law: From Market Fundamentalism to a New Keynesian Regulatory Model” Harvard Law & Policy Review; Gadinis, S. 2013. “From Independence to Politics in Financial Regulation” UC Berkeley Public Law Research Paper No. 2137215.
- 62.
It refers to Kumar, A. and Jacobson, S. H. 1998. “Optimal and Near-Optimal Decisions for Procurement and Allocation of a Critical Resource with a Stochastic Consumption Rate” University of Michigan Business School Working Paper No. 98019, which—using the illustration of a periodic-review, stochastic-demand, fixed-route, centralized, multi-echelon distribution system—proposed a non-ranking allocation policy that is faster than those existing in the literature and also develop an optimal replenishment algorithm that is independent of the allocation assumption.
- 63.
See Zetzsche, D. A. and Preiner, C. 2017. “Cross-Border Crowdfunding – Towards a Single Crowdfunding Market for Europe” University of Luxembourg Law Working Paper No. 2017/002; the authors—in contrast to the European Commission’s Capital Market Action Plan—take the view that national limitations on crowd investing and crowd lending de facto are the result of limits de jure.
In particular, the authors detailed how European regulators could facilitate a Single European Crowdfunding Market while limiting both the risks for investors and the regulatory burden for crowdfunding platforms and recipients. In light of the regulatory experience with other financial products and the segregating effect of product-based approaches, many of which exist in the EU/EEA Member States, they believed that existing product regulation is insufficient to enable a European cross-border crowdfunding market. Instead, regulation based on the ‘MiFID light’ framework could function as basis for a cross-border crowdfunding manager passport, given the minimum protection it affords both investors and the financial system, and the low costs it imposes on the platform.
- 64.
Furthermore, see Scott, K. E. 2009. “Lessons from the Crisis” Stanford Law and Economics Olin Working Paper No. 385, which provided a simplified approach based on three stages: first, a look at the key factors that led to the increasing riskiness of US home mortgages; second, how those risks were transmitted as securities from US housing lenders to institutional investors around the globe; and third, how those risks led to huge losses and created a credit crunch that moved the impact from the financial economy to the real economy and produced a severe recession. Then there is a factual foundation for deriving the lessons that ought to be taken away from this very expensive experience.
See also Masera, R. 2013. “US Basel III Final Rule on Banks’ Capital Requirements: A Different-Size-Fits-All Approach” PSL Quarterly Review; Popov, A. A. and Udell, G. F. 2010. “Cross-Border Banking and the International Transmission of Financial Distress During the Crisis of 2007-2008” ECB Working Paper No. 1203; Chan-Lau, J. A. and Chen, Z. 1998. “Financial Crisis and Credit Crunch as a Result of Inefficient Financial Intermediation—With Reference to the Asian Financial Crisis” IMF Working Paper No. 98/127.
- 65.
In the light of the financial constraints that the operators have had to face, some phenomena of small entity (which, in a different socio-economic context, would not have aroused any interest in the market) end up taking on importance. It is referring to activities to which it is certainly not possible to attribute significance on the level of their concrete contribution to the processes of economic development; see Mollick, E. R. 2013. “The Dynamics of Crowdfunding: An Exploratory Study” Journal of Business Venturing.
- 66.
It raises a reference to Glinavos, I. 2010. “Regulation and the Role of Law in Economic Crisis” European Business Law Review, which offers a discussion on the relationship of deregulation to financial crisis, arguing that there is a direct link between the receding reach of the state and market instability, drawing analogies with previous instances of market failure, like the Great Depression. On the basis of this connection, a theoretical portrayal of perceptions of the role of law in modern capitalism is attempted, where the main message is that dominant modern perceptions of the state market relationship allow a role for regulation but still do not recognize the state as the legitimate author of such regulation, showing a preference for market-led solutions.
- 67.
In this respect, it brings up the conclusions of Jagtiani, J. A. and Lemieux, C. 2017. “Fintech Lending: Financial Inclusion, Risk Pricing, and Alternative Information” FRB of Philadelphia Working Paper No. 17-17, which find that Lending Club’s consumer-lending activities have penetrated areas that could benefit from additional credit supply, such as areas that lose bank branches and those in highly concentrated banking markets. Moreover, the authors find a high correlation with interest rate spreads, Lending Club rating grades, and loan performance.
- 68.
It refers also to Bernstein, S. and Korteweg, A. G. and Laws, K. 2015. “Attracting Early Stage Investors: Evidence from a Randomized Field Experiment” Stanford University Graduate School of Business Research Paper No. 14-17, which used a randomized field experiment to identify which start-up characteristics are most important to investors in early stage firms.
- 69.
It is worth considering the options of the Italian legislator, who has regulated the crowd-businesses, in the wake of what has already been achieved in the United States with the introduction of the JOBS Act (Jumpstart Our Business Startups Act) of April 2012. This refers in particular to Legislative Decree no. 179 of 18 October 2012, called ‘Crescita Bis’ and converted with amendments by Law no. 221 of 17 December 2012, which introduced the regulation of equity crowdfunding (as well as its application to financing processes for the so-called innovative start-ups) as part of specific measures aimed at boosting competition, the development of national infrastructures and the competitiveness of the internal market.
- 70.
See again Zetzsche, D. A. and Preiner, C. 2017. “Cross-Border Crowdfunding – Towards a Single Crowdfunding Market for Europe” University of Luxembourg Law Working Paper No. 2017/002, which— following the (1) too-small-to-care, (2) too-large-to-ignore, and (3) too-big-to-fail development path of FinTech business models—suggested adding a relevance threshold of €250,000 in transaction volume to the MiFID light framework and imposing regulation to address systemic risk concerns for very large crowdfunding platforms that may arise in the future.
- 71.
It is worth considering also Kaminski, J. and Hopp, C. and Tykvova, T. 2019. “New Technology Assessment in Entrepreneurial Financing – Can Crowdfunding Predict Venture Capital Investments?” Technological Forecasting and Social Change whose cointegration tests suggest a long-run relationship between crowdfunding and venture capital investments, while impulse response functions indicate a positive effect running from crowdfunding to venture capital within two to six months.
- 72.
See Haldane, A. and Shanbhogue, R. and Attanasio, O. and Besley, T. J. and Lindert, P. H. and Piketty, T. and Ventura, K. 2015. “Capital in the 21st Century” Bank of England Quarterly Bulletin 2015 Q1, which presented research on various issues relating to inequality, including: access to education; wealth and taxation policy; and the role of governance and institutions.
See also Upadhyay, V. 2015. “Can Capitalism Survive High Degree of Automation? A Comparison with Thomas Piketty’s Argument” SSRN Research Paper no. 2558989; Magness, P. and Murphy, R. P. 2014. “Challenging the Empirical Contribution of Thomas Piketty’s Capital in the 21st Century” Journal of Private Enterprise
- 73.
In this way, the availability of various forms of network has allowed the dissemination of multiple business projects, underlying the development of innovative ideas (especially in the fields of art, music, film, as well as donations to solidarity and social utility).
- 74.
In a nutshell, there is a modus operandi that involves a review of the fees of the credit intermediation activity; this is because, against the disbursement made by the saver, the appointed mechanisms of financing do not provide for an immediate counter-value (monetary and/or material) to be allocated to the latter. In addition, there are the operational peculiarities found in online social lending platforms, aimed at satisfying the credit needs of private individuals (sometimes the ones falling into the categories of ‘non-bankable’ individuals), who interact with each other through direct meeting mechanisms (in the so-called peer-to-peer mode) resulting in the creation of alternative financing channels to those provided by banking intermediaries.
- 75.
In addition, see Cumming, D. J. and Johan, S. A. and Zhang, Y. 2019. “The Role of Due Diligence in Crowdfunding Platforms” Journal of Banking and Finance, which pointed out that that due diligence is related to legislation requirement, platform size and type or complexity of crowdfunding campaigns.
- 76.
See Omarova, S. T. 2019 “New Tech v. New Deal: Fintech As A Systemic Phenomenon” Yale Journal on Regulation on how and why specific fintech applications—cryptocurrencies, distributed ledger technologies, digital crowdfunding and robo-advising—are poised to amplify the effect of these destabilizing mechanisms, and thus potentially exacerbate the tensions and imbalances in today’s financial markets and the broader economy. It is this potential that renders fintech a public policy challenge of the highest order. The author introduced the concept of the New Deal settlement in finance: a fundamental political arrangement, in force for nearly a century, pursuant to which profit-seeking private actors retain control over allocating capital and generating financial risks, while the sovereign public bears responsibility for maintaining systemic financial stability. Moreover, He presented an alternative account of fintech as a systemic, macro-level phenomenon: grounding his analysis of evolving fintech trends in a broader institutional context, exposes the normative and political significance of the current fintech moment. It argues that the arrival of fintech enables a potentially decisive shift in the underlying public–private balance of powers, competencies and roles in the financial system.
- 77.
See Masera, R. 2011. “Taking the Moral Hazard Out of Banking: The Next Fundamental Step in Financial Reform” PSL Quarterly Review, which highlighted that the path between financial meltdown and moral hazard in banking is, at best, narrow and impervious. Moreover, the author pointed out that, during the financial crisis, public support became the standard response to save the banks in difficulty, heightening and broadening the moral hazard issue: subordinated/senior debt holders and large depositors were bailed out and equity holders were partially sheltered. In the Eurozone, the implicit promise to bail out governments in difficulty has encouraged SIFIs and other financial operators to speculate on the yield differential between sovereigns and the ECB money market interest rates. The policy framework proposed here is two-pronged: the EFSF should evolve to permit more flexible and wide-ranging interventions, and be able to manage sovereign debt restructuring; with respect to SIFIs, very early corporate, market and supervisory responses are suggested. Intervention of supervisory authorities with mandatory (special) powers would occur before the threshold of non-viability and, on a gone-concern basis, in terms of a European resolution procedure.
See also Rifkin, J. 2014 “The Zero Marginal Cost Society: The Internet of Things, the Collaborative Commons, and the Eclipse of Capitalism”. New York
- 78.
Moreover, see Buchak, G. and Matvos, G. and Piskorski, T. and Seru, A. 2017. “Fintech, Regulatory Arbitrage, and the Rise of Shadow Banks”, NBER Working Paper no 23288, which pointed out that fintech lenders appear to offer a higher-quality product and charge a premium of 14–16 basis points. Relative to other lenders, they seem to use different information to set interest rates. A quantitative model of mortgage lending suggests that regulation accounts for roughly 60% of shadow bank growth, while technology accounts for roughly 30%.
- 79.
In addition, it is worth considering that the above is in line with the first attempts made to clarify the contents of this phenomena; see Lucantoni, P. 2019. “Distributed ledger techonology e infrastrutture di negoziazione e post-trading”, in VV.AA., “Fintech: diritto, tecnologia e finanza”, Roma, p. 97 ff.
- 80.
Furtheromore, see Maume, P. 2018. “Reducing Legal Uncertainty and Regulatory Arbitrage for Robo-Advice” European Company and Financial Law Review who pointed out that the nature of the interaction between client and machine raises many legal questions under the applicable EU regulation. See also Maume, P. 2018. “Regulating Robo-Advisory” Texas International Law Journal.
- 81.
Obviously, this refers also to any format, including any improvements, modifications, derivative works, patches, updates and upgrades thereto.
- 82.
In particular, it refers to Hacker, P. and Lianos, I. and Dimitropoulos, G. and Eich, E. 2018. “Regulating Blockchain: Techno-Social and Legal Challenges” “Regulating Blockchain. Techno-Social and Legal Challenges”, Oxford; see also—on this point—the considerations of Powell, L. 2017. “Big Data and Regulation in the Insurance Industry” SSRN Research Paper no. 2951306.
- 83.
See Malhotra, Y. 2016. “Beyond Model Risk Management to Model Risk Arbitrage for FinTech Era: How to Navigate ‘Uncertainty’…When ‘Models’ Are ‘Wrong’…And Knowledge’…‘Imperfect’! Knight Reconsidered Again: Risk, Uncertainty, & Profit Beyond ZIRP & NIRP! Research Presentation at: 2016 Princeton Quant Trading Conference, which presented certain basis for understanding emerging Cyber Finance practices at the intersection of leading-edge developments in both finance- and cybersecurity-related risk and uncertainty management. In addition, it also developed computational quantitative finance modelling foundations for industrywide insurance practices.
- 84.
It recalls Eling, M. and Schmeiser, H. and Schmit, J. T. 2006. “The Solvency II Process: Overview and Critical Analysis” Risk Management and Insurance Review; Vaughan, T. M. 2009. “The Implications of Solvency II for U.S. Insurance Regulation” Networks Financial Institute Policy Brief No. 2009-PB-03; Filipovic, D. and Kremslehner, R. and Muermann, A. 2013. “Optimal Investment and Premium Policies under Risk Shifting and Solvency Regulation” Swiss Finance Institute Research Paper No. 11-11.
Furthermore, Colaert, V. A. 2015. “European Banking, Insurance and Investment Services Law: Cutting Through Sectoral Lines?” Common Market Law Review questioned: (1) what is the adequate level and model of financial supervision in the EU, and (2) what is the adequate level and model of financial legislation in the EU.
See also Scalise M. and Fichera F. 2017. “Solvency II: impatti del nuovo regime sui profili pubblicistici della vigilanza assicurativa” Diritto del mercato assicurativo e finanziario, p. 119 ff.; Sartori, F. 2017. “Disciplina dell’impresa e statuto contrattuale: il criterio della sana e prudente gestione” Banca borsa e titoli di credito, p. 131 ff.
It is worth recalling Argentati, A. 2018. “Polizze assicurative abbinate al credito e tutela del cliente: analisi critica dei più recenti sviluppi normativi” Rivista Trimestrale di Diritto dell’Economia, 2018, p. 271; Corrias, P. 2018. “Profili generali della nuova disciplina recata dalla Direttiva 2016/97/EU” Rivista Trimestrale di Diritto dell’Economia, 2018, p. 158 ff.; Landini, S. 2018. “Distribuzione assicurativa da IDD al decreto attuativo passando per EIOPA e IVASS” Diritto del mercato assicurativo e finanziario, p. 183 ff.
- 85.
See Lamberton, C. and Brigo, D. and Hoy, D. 2017. “Impact of Robotics, RPA and AI on the Insurance Industry: Challenges and Opportunities” Journal of Financial Perspectives; the authors consider the current challenges and opportunities in applications of Robotics to financial services and to insurance, and then discuss advanced Artificial Intelligence (AI) applications, arguing that such applications depend on the general advancements of AI, where human-level interaction is not yet available.
It is also worth recalling on this point Prince, A. and Schwarcz, D. B. 2019. “Proxy Discrimination in the Age of Artificial Intelligence and Big Data” Iowa Law Review on proxy discrimination, considered as a particularly pernicious subset of disparate impact. Like all forms of disparate impact, it involves a facially-neutral practice that disproportionately harms members of a protected class. But a practice producing a disparate impact only amounts to proxy discrimination when the usefulness to the discriminator of the facially neutral practice derives, at least in part, from the very fact that it produces a disparate impact.
- 86.
Regulatory studies have to understand the multifaceted implications of algorithm accountability on the expectations that individuals may have thereof with respect to automated decision-making, by considering that the rationales can only be served if controllers cannot hide behind algorithms for automated individual decision-making; see Moerel, E.M.L. and Storm, M. 2019. “Automated Decisions Based on Profiling: Information, Explanation or Justification – That Is The Question!” Autonomous Systems and the Law, Oxford.
- 87.
See Peters, G. and Shevchenko, P. V. and Cohen, R. 2018. “Understanding Cyber-Risk and Cyber-Insurance” Macquarie University Faculty of Business & Economics Research Paper; the authors discuss the emerging market of cyber risk insurance and the challenges faced by this market resulting from the diversity of insurance coverage on uncertainty relating to potential exposures and vulnerabilities associated with this risk class.
- 88.
See Yeung, K. 2016. “Hypernudge: Big Data as a Mode of Regulation by Design” Information, Communication & Society, which argued that concerns about the legitimacy of these techniques are not satisfactorily resolved through reliance on individual notice and consent, touching upon the troubling implications for democracy and human flourishing if big data analytic techniques driven by commercial self-interest continue their onward march unchecked by effective and legitimate constraints. See also Marcacci, A. 2017. “Digitally-provided Financial Services under EU Law: Overcoming the Current Patchwork of Europeanized Private International Law and Sectorially-harmonized National Private Laws” Studi sull’integrazione europea.
- 89.
It is worth recalling that the nature of the interaction between client and machine raises many legal questions under the applicable EU regulation; see Maume, P. 2018. “Reducing Legal Uncertainty and Regulatory Arbitrage for Robo-Advice” European Company and Financial Law Review.
- 90.
See El Khoury, A. 2018. “Personal Data, Algorithms and Profiling in the EU: Overcoming the Binary Notion of Personal Data through Quantum Mechanics” Erasmus Law Review, which proposed to analyse the binary notion of personal data and highlight its limits, in order to propose a different conception of personal data.
See also Ben-Shahar, O. and Logue, K. D. 2012. “Outsourcing Regulation: How Insurance Reduces Moral Hazard” Michigan Law Review; Verhoef, P. C. and Donkers, N. 2001. “Predicting Customer Potential Value: An Application in the Insurance Industry” ERIM Report Series Reference No. ERS-2001-01-MKT; Skeel, D. A. 1999. “The Market Revolution in Bank and Insurance Firm Governance: Its Logic and Limits” Washington University Law Quarterly.
- 91.
See Swedloff, R. 2014. “Risk Classification’s Big Data (R)evolution” Connecticut Insurance Law Journal on the promise that the algorithms driving big data will offer greater predictive accuracy than traditional statistical analysis alone.
It recalls also the analysis of Gaver, J. J. and Paterson, J. S. 1998. “The Association between External Monitoring and Earnings Management in the Property-Casualty Insurance Industry” SSRN Research Paper no. 144419, which examined the association between external monitoring and earnings management by property-casualty insurers.
- 92.
It is worth considering Kerber, W. and Frank, J. 2017. “Data Governance Regimes in the Digital Economy: The Example of Connected Cars” SSRN Research Paper no. 3064794, which reviewed the issues of privacy, data ownership and data access from a specific point of view. In this perspective, the authors applied their analytical framework to the complex problem of data governance in connected cars (with its different stakeholders car manufacturers, car owners, car component suppliers, repair service providers, insurance companies and other service providers), and identifies several potential market failure problems in regard to this specific data governance problem (especially competition problems, information/behavioral and privacy problems).
- 93.
It is worth recalling the analysis of Raskin, M. 2017. “The Law and Legality of Smart Contracts” Georgetown Law Technology Review, p. 305 ss.; Kôlvart, M. and Poola, M. and Rull, A. 2016 “Smart Contracts” “The Future of Law and eTechnologies”, London, p. 133 ss.; Mik, E. 2017 “Smart Contracts: Terminology, Technical Limitations and Real World Complexity”, Law,Innovation & Technology, p. 269 ff.
- 94.
It is worth considering the conclusions of Helveston, M. 2016. “Consumer Protection in the Age of Big Data” Washington University Law Review, which, in describing the potential problems raised by insurers’ uses of data and constructing a regulatory framework for addressing these issues, raised as many questions about the factors that should determine whether a certain type of coverage falls within the consumer regulatory scheme or not. Hence, the need for a regulatory intervention aimed at choosing the trade-offs between actuarial fairness and other goals (as stability, inclusion, wealth) that cannot be resolved through analytic reasoning, but are inherent to normative matters.
References
“The Big Bang: How the Big Data Explosion Is Changing the World” – Microsoft UK Enterprise Insights Blog – Site Home – MSDN Blogs.
Ait-Sahalia, Y. and Karaman, M. and Mancini, L. 2018. “The Term Structure of Variance Swaps and Risk Premia”, Swiss Finance Institute Research Paper No. 18-37.
American Psychological Association, 2013. “Glossary of psychological terms”, Apa.org.
Anderson, C. 2008. “The end of theory: The data deluge makes the scientific method obsolete”, Wired Magazine.
Argentati, A. 2018. “Polizze assicurative abbinate al credito e tutela del cliente: analisi critica dei più recenti sviluppi normativi”, Rivista Trimestrale di Diritto dell’Economia.
Arner, D. W. and Barberis, J. N. and Buckley, R. P. 2016. “FinTech, RegTech and the Reconceptualization of Financial Regulation” University of Hong Kong Faculty of Law Research Paper No. 2016/035.
Arrow, K. J. 1971. “Essays in theory of risk bearing”, Chicago.
Ban, G. Y. and Rudin, C. 2018. “The Big Data Newsvendor: Practical Insights from Machine Learning”, Operations Research 67.
Bank of England, 2014. “Strategic plan: Background information”.
Banterle, F. 2018. “Data Ownership in the Data Economy: A European Dilemma” EU Internet Law in the digital era.
Barone, E. and Masera, R. 2000. “Capital Requirements, Capital Adequacy and Risk Management”, SSRN research paper no. 2574336.
Barth, J. R. and Caprio, G. and Levine, R. E. 2001. “The Regulation and Supervision of Banks Around the World: A New Database” World Bank Policy Research Working Paper No. 2588.
Benos, E., and Sagade, S. 2012. “High frequency trading behaviour and its impact on market quality: Evidence from theUK equity market”, Bank of England Working Paper 469.
Ben-Shahar, O. and Logue, K. D. 2012. “Outsourcing Regulation: How Insurance Reduces Moral Hazard”, Michigan Law Review.
Bernstein, S. and Korteweg, A. G. and Laws, K. 2015. “Attracting Early Stage Investors: Evidence from a Randomized Field Experiment”, Stanford University Graduate School of Business Research Paper No. 14-17.
Beyer, M. A. and Laney, D. 2012. “The importance of big data: A definition”. Stamford, CT: Gartner.
Bholat, D. 2013. “The future of central bank data”. Journal of Banking Regulation.
Bholat, D. 2015. “Big data and central banks” Big Data & Society.
Bianco Alberto, A. 1981. “La grazia perfeziona la natura. Il fondamento scritturistico del diritto naturale” Studi cattolici.
Boschetti, B. 2016. “Soft law e normatività: un’analisi comparata”, Rivista della Regolazione dei mercati.
Brito, J. and Shadab, H. B. and Castillo O’Sullivan, A. 2014. “Bitcoin Financial Regulation: Securities, Derivatives, Prediction Markets, and Gambling”, Columbia Science and Technology Law Review.
Buchak, G. and Matvos, G. and Piskorski, T. and Seru, A. 2017. “Fintech, Regulatory Arbitrage, and the Rise of Shadow Banks”, NBER Working Paper No 23288.
Burr, D. and Ross, J. 2008a. “A Visual Sense of Number” Current Biology.
Burr, D. and Ross, J. 2008b. “Response: Visual number” Current Biology.
Buzzi, F. 2005. “La teologia per il diritto dell’uomo e dei popoli”, Iustitia.
Canova, T. A. 2009. “Financial Market Failure as a Crisis in the Rule of Law: From Market Fundamentalism to a New Keynesian Regulatory Model” Harvard Law & Policy Review.
Cantone, R. 1996. “L’abusivismo finanziario: esperienze da un’indagine giudiziaria. Nota a Cass. sez. V pen. 6 ottobre 1995”, Cassazione penale.
Capriglione, F. 1997. “La problematica della “banca di fatto” dopo il d.lg. 385/1993”, La nuova giurisprudenza civile commentata.
Capriglione, F. and Masera, R. 2016. “Bank Corporate Governance: A New Paradigm”, Open Review of Management, Banking and Finance.
Capriglione, F. and Sacco Ginevri, A. 2019. “Metamorfosi della governance bancaria”, Milano.
Carney, M. 2017 “The Promise of FinTech – Something New Under the Sun? Speech given by the Governor of the Bank of England Chair of the Financial Stability BoardDeutsche Bundesbank G20 conference on Digitising finance, financial inclusion and financial literacy”, Wiesbaden, 25 January.
Chan-Lau, J. A. and Chen, Z. 1998 “Financial Crisis and Credit Crunch as a Result of Inefficient Financial Intermediation--With Reference to the Asian Financial Crisis” IMF Working Paper No. 98/127.
Chesbrough, H. and Bogers, M. 2014. “Explicating Open Innovation: Clarifying an Emerging Paradigm for Understanding Innovation”, New Frontiers in Open Innovation, Oxford.
Cœuré, B. 2017. “Policy analysis with big data. Speech by the member of the Executive Board of the ECB, at the conference on Economic and Financial Regulation in the Era of Big Data”, Banque de France, Paris, 24 November.
Cohen, J. E. 2012. “What Privacy Is For” Harvard Law Review.
Colaert, V. A. 2015. “European Banking, Insurance and Investment Services Law: Cutting Through Sectoral Lines?”, Common Market Law Review.
Corrias, P. 2018. “Profili generali della nuova disciplina recata dalla Direttiva 2016/97/EU”, Rivista Trimestrale di Diritto dell’Economia.
Criscuolo, L. 1996. “L’esercizio abusivo di attività finanziaria: profili giuridici e strumenti di contrasto” Cassazione penale.
Cumming, D. J. and Johan, S. A. and Zhang, Y. 2019. “The Role of Due Diligence in Crowdfunding Platforms” Journal of Banking and Finance.
Davenport, T. 2014. “Big Data at Work: Dispelling the Myths, Uncovering the Opportunities”, Harvard Business Review.
De Fiore, F. and Uhlig, H. 2005. “Bank Finance Versus Bond Finance: What Explains the Differences between Us and Europe?”, ECB Working Paper No. 547.
Dehaene, S. 2009. “Origins of Mathematical Intuitions The Case of Arithmetic”. Annals of the New York Academy of Sciences.
Diebold, F. X. 2012. “On the Origin(s) and Development of the Term ‘Big Data’”, PIER Working Paper No. 12-037.
Dijcks, J. P. 2012. “Oracle: Big data for the enterprise”, Oracle White Paper.
Durgin, F. H. 2008. “Texture density adaptation and visual number revisited”, Current Biology.
EBA 2020 “EBA Report on big data and advanced analytics”, January.
ECB. 2018. “Guide to assessments of fintech credit institution licence applications”.
ECB 2020, “Central bank group to assess potential cases for central bank digital currencies”, Bruxelles, 21 January.
Edwards, l. 2016. “Privacy, Security and Data Protection in Smart Cities: A Critical EU Law Perspective”, European Data Protection Law Review.
Einav L. and Levin J. D. 2013. “The data revolution and economic analysis”. NBER Working Paper Series No. 19035.
EIOPA 2019. “Report on best practices on licencing requirements, peer-to-peer insurance and the principle of proportionality in an insurtech context”, 27 March.
El Khoury, A. 2018. “Personal Data, Algorithms and Profiling in the EU: Overcoming the Binary Notion of Personal Data through Quantum Mechanics”, Erasmus Law Review.
Eling, M. and Schmeiser, H. and Schmit, J. T. 2006. “The Solvency II Process: Overview and Critical Analysis” Risk Management and Insurance Review.
Fahlenbrach, R. and Prilmeier, R. and Stulz, R. M. 2016. “Why Does Fast Loan Growth Predict Poor Performance for Banks?”, Swiss Finance Institute Research Paper No. 16-24.
Filipovic, D. and Kremslehner, R. and Muermann, A. 2013. “Optimal Investment and Premium Policies under Risk Shifting and Solvency Regulation”, Swiss Finance Institute Research Paper No. 11-11.
Flood, M. et al. 2014. “The application of visual analytics to financial stability monitoring”, OFR Working Paper 14.
FSB. 2017. “Artificial intelligence and machine learning in financial services. Market developments and financial stability implications”.
FSB 2020. “Global Monitoring Report on Non-Bank Financial Intermediation 2019”, 19 January.
Gadinis, S. 2013. “From Independence to Politics in Financial Regulation” UC Berkeley Public Law Research Paper No. 2137215.
Gaver, J. J. and Paterson, J. S. 1998. “The Association between External Monitoring and Earnings Management in the Property-Casualty Insurance Industry”, SSRN Research Paper No. 144419.
Gebauer, S. and Mazelis, F. 2019. “Macroprudential Regulation and Leakage to the Shadow Banking Sector”, DIW Berlin Discussion Paper No. 1814.
Glinavos, I. 2010 “Regulation and the Role of Law in Economic Crisis” European Business Law Review.
Gomber, P. and Kauffman, R. J. and Parker, C. and Weber, B. 2017. “On the Fintech Revolution: Interpreting the Forces of Innovation, Disruption and Transformation in Financial Services” Journal of Management Information Systems.
Google, 2013. “Google Trends for Big Data”.
Gorton, G. B. and Metrick, A. 2010. “Securitized Banking and the Run on Repo” Yale ICF Working Paper No. 09-14.
Greco, P. 1930. “Le operazioni di banca”, Padova.
Grossman, R. and Siegel, K. 2014. “Organizational Models for Big Data and Analytics” Journal of Organization Design.
Hacker, P. and Lianos, I. and Dimitropoulos, G. and Eich, E. 2018. “Regulating Blockchain: Techno-Social and Legal Challenges”, Oxford
Haldane, A. 2013. “Why institutions matter (more than ever)”. Speech delivered at the Centre for Research on SocioCultural Change Annual Conference, School of Oriental and African Studies, London.
Haldane, A. and Shanbhogue, R. and Attanasio, O. and Besley, T. J. and Lindert, P. H. and Piketty, T. and Ventura, K. 2015. “Capital in the 21st Century”, Bank of England Quarterly Bulletin 2015 Q1.
Hardin, G. 1968. “The Tragedy of The Commons”, Science.
Hazen, T. L. 2012. “Crowdfunding or Fraudfunding? Social Networks and the Securities Laws – Why the Specially Tailored Exemption Must be Conditioned on Meaningful Disclosure”, North Carolina Law Review.
Helveston, M. 2016. “Consumer Protection in the Age of Big Data”, Washington University Law Review.
Hermalin, B. E. and Katz, A. W. and Craswell, R. 2006. “The Law and Economics of Contracts”, Columbia Law and Economics Working Paper No. 296.
Hubbard, E. M. and Piazza, M.; Pinel, P. and Dehaene, S. 2005. “Interactions between number and space in parietal cortex”, Nature Reviews Neuroscience.
Iannarone, N. G. 2017. “Computer As Confidant: Digital Investment Advice and the Fiduciary Standard” Chicago-Kent Law Review.
Izard, V. and Dehaene, S. 2008. “Calibrating the mental number line” Cognition.
Jacobsen, S. F. and Tschoegl, A. E. 1997. “The Norwegian Banks in the Nordic Consortia: A Case of International Strategic Alliances in Banking”, SSRN research paper no 52068.
Jagtiani, J. A. and Lemieux, C. 2017. “Fintech Lending: Financial Inclusion, Risk Pricing, and Alternative Information”, FRB of Philadelphia Working Paper No. 17-17.
Kache, F. 2015. “Dealing with digital information richness in supply chain management. A review and a big data analytics approach”, Kassel.
Kaminski, J. and Hopp, C. and Tykvova, T. 2019. “New Technology Assessment in Entrepreneurial Financing – Can Crowdfunding Predict Venture Capital Investments?” Technological Forecasting and Social Change.
Kerber, W. and Frank, J. 2017. “Data Governance Regimes in the Digital Economy: The Example of Connected Cars”, SSRN Research Paper No. 3064794.
Kitchin, R. 2014. “Thinking Critically About and Researching Algorithms”, The Programmable City Working Paper 5.
Knight, F. 1921. “Risk, Uncertainty, and Profit”, (Ed. 2009) Cambridge.
Kôlvart, M. and Poola, M. and Rull, A. 2016. “Smart Contracts”, The Future of Law and eTechnologies, London.
Kronman, A. T. 1985. “Contract law and the state of nature” Journal of law, economics and organization.
Kumar, A. and Jacobson, S. H. 1998. “Optimal and Near-Optimal Decisions for Procurement and Allocation of a Critical Resource with a Stochastic Consumption Rate”, University of Michigan Business School Working Paper No. 98019.
Lamberton, C. and Brigo, D. and Hoy, D. 2017. “Impact of Robotics, RPA and AI on the Insurance Industry: Challenges and Opportunities”, Journal of Financial Perspectives.
Landini, S. 2018. “Distribuzione assicurativa da IDD al decreto attuativo passando per EIOPA e IVASS”, Diritto del mercato assicurativo e finanziario.
Lemma, V. 2016. “The Shadow Banking System. Creating Transparency in the Financial Markets”. London.
Lobel, O. 2016. “The Law of the Platform”, Minnesota Law Review.
Lucantoni, P. 2019. “Distributed ledger techonology e infrastrutture di negoziazione e post-trading”, on VV.AA., Fintech: diritto, tecnologia e finanza, Roma.
Magness, P. and Murphy, R. P. 2014. “Challenging the Empirical Contribution of Thomas Piketty’s Capital in the 21st Century”, Journal of Private Enterprise.
Maine, H. J. S. 1861. “Ancient law” (ed. 2005).
Mainelli, M. and Smith, M. 2015. “Sharing Ledgers for Sharing Economies: An Exploration of Mutual Distributed Ledgers (Aka Blockchain Technology)”, Journal of Financial Perspectives.
Malhotra, Y. 2016. “Beyond Model Risk Management to Model Risk Arbitrage for FinTech Era: How to Navigate ‘Uncertainty’…When ‘Models’ Are ‘Wrong’…And Knowledge’…‘Imperfect’! Knight Reconsidered Again: Risk, Uncertainty, & Profit Beyond ZIRP & NIRP!”, Research Presentation at: 2016 Princeton Quant Trading Conference.
Marcacci, A. 2017. “Digitally-provided Financial Services under EU Law: Overcoming the Current Patchwork of Europeanized Private International Law and Sectorially-harmonized National Private Laws” Studi sull’integrazione europea.
Masera, R. 2011. “Taking the Moral Hazard Out of Banking: The Next Fundamental Step in Financial Reform”, PSL Quarterly Review.
Masera, R. 2013. “US Basel III Final Rule on Banks’ Capital Requirements: A Different-Size-Fits-All Approach”, PSL Quarterly Review.
Maume, P. 2017. “In Unchartered Territory – Banking Supervision Meets Fintech”, Corporate Finance.
Maume, P. 2018a. “Reducing Legal Uncertainty and Regulatory Arbitrage for Robo-Advice” European Company and Financial Law Review.
Maume, P. 2018b. “Regulating Robo-Advisory” Texas International Law Journal.
Mik, E. 2017. “Smart Contracts: Terminology, Technical Limitations and Real World Complexity”, Law, Innovation & Technology.
Moerel, E.M.L. and Storm, M. 2019 “Automated Decisions Based on Profiling: Information, Explanation or Justification – That Is The Question!” Autonomous Systems and the Law, Oxford.
Mollick, E. R. 2013 “The Dynamics of Crowdfunding: An Exploratory Study”, Journal of Business Venturing.
Morellec, E. and Wang, N. 2004. “Capital Structure, Investment, and Private Benefits of Control” Simon Business School Working Paper No. FR04-17.
Moritz, A. and Block, J. H. 2013. “Crowdfunding und Crowdinvesting: State-of-the-Art der wissenschaftlichen Literatur”, Zeitschrift für KMU und Entrepreneurship.
Mullainathan, S. 2014. “Big data and the inductive method to theory testing: A framework with applications”, Hahn lecture delivered at the Royal Economics Society Annual Conference, University of Manchester, Manchester.
Newman, N. 2014. “Search, Antitrust and the Economics of the Control of User Data”, Yale Journal on Regulation.
Nymand-Andersen, P. 2015. “Big data: the hunt for timely insights and decision certainty: Central banking reflections on the use of big data for policy purposes”, IFC Working Paper No 14.
Omarova, S. T. 2019. “New Tech v. New Deal: Fintech As A Systemic Phenomenon” Yale Journal on Regulation.
Paech, P. 2016. “The Governance of Blockchain Financial Networks” Modern Law Review.
Peters, G. and Shevchenko, P. V. and Cohen, R. 2018. “Understanding Cyber-Risk and Cyber-Insurance”, Macquarie University Faculty of Business & Economics Research Paper.
Popov, A. A. and Udell, G. F. 2010 “Cross-Border Banking and the International Transmission of Financial Distress During the Crisis of 2007-2008” ECB Working Paper No. 1203.
Posner, R. 2008. “How Judges think”, Harvard.
Powell, L. 2017. “Big Data and Regulation in the Insurance Industry”, SSRN Research Paper No. 2951306.
Preis, T. and Moat, H. S. and Stanley, H. E. 2013. “Quantifying Trading Behavior in Financial Markets Using Google Trends”, Scientific Reports.
Prince, A. and Schwarcz, D. B. 2019. “Proxy Discrimination in the Age of Artificial Intelligence and Big Data”, Iowa Law Review.
Raskin, M. 2017. “The Law and Legality of Smart Contracts”, Georgetown Law Technology Review.
Rifkin, J. 2014 “The Zero Marginal Cost Society: The Internet of Things, the Collaborative Commons, and the Eclipse of Capitalism”. New York.
Rubinstein, I. 2012. “Big Data: The End of Privacy or a New Beginning?”, NYU School of Law, Public Law Research Paper No. 12-56.
Sandulli, F. D. and Chesbrough, H. 2009. “The Two Sides of Open Business Models”, SSRN research paper No. 1325682.
Sanz Bayón, P. and Vega, L. G. 2018. “Automated Investment Advice: Legal Challenges and Regulatory Questions” Banking & Financial Services Policy Report.
Sartori, F. 2017. “Disciplina dell’impresa e statuto contrattuale: il criterio della sana e prudente gestione”, Banca borsa e titoli di credito.
Savelyev, A. 2016. “Contract Law 2.0: «Smart» Contracts As the Beginning of the End of Classic Contract Law” Higher School of Economics Research Paper No. WP BRP 71/LAW/2016.
Scalise M. and Fichera F. 2017. “Solvency II: impatti del nuovo regime sui profili pubblicistici della vigilanza assicurativa”, Diritto del mercato assicurativo e finanziario.
Schumpeter, J. 1942. “Capitalism, socialism and democracy”, (ed. 1994, Abingdon-on-Thames.
Scott, K. E. 2009. “Lessons from the Crisis”, Stanford Law and Economics Olin Working Paper No. 385.
Severino, P. 2000. “Le disposizioni integrative e correttive del Testo Unico delle leggi in materia bancaria e creditizia. Il quadro sanzionatorio: innovazioni nelle fattispecie e nella procedura applicativa”, Diritto della banca e del mercato finanziario.
Simon, P. 2015. “Too Big to Ignore: The Business Case for Big Data”, Hoboken.
Skeel, D. A. 1999. “The Market Revolution in Bank and Insurance Firm Governance: Its Logic and Limits”, Washington University Law Quarterly.
Susskind, R. and Susskind, D. 2015 “Future of the Professions: How Technology will Transform the Work of Human Experts”, Oxford.
Swedloff, R. 2014. “Risk Classification’s Big Data (R)evolution”, Connecticut Insurance Law Journal.
Taylor, L. and Schroeder, R. and Meyer, E. 2014. “Emerging practices and perspectives on Big Data analysis in economics: Bigger and better or more of the same?”, Big Data & Society.
Tene, O. 2007. “What Google Knows: Privacy and Internet Search Engines”, Utah Law Review.
Tene, O. and Polonetsky, J. 2013 “Big Data for All: Privacy and User Control in the Age of Analytics” Northwestern Journal of Technology and Intellectual Property.
Tissot, B. 2017. “Big data and central banking”, IFC Bulletin No 44.
Torresetti, R. and Nordio, C., 2014. “Scaling Operational Loss Data and Its Systemic Risk Implications” SSRN Research paper no. 2360483.
Upadhyay, V. 2015. “Can Capitalism Survive High Degree of Automation? A Comparison with Thomas Piketty’s Argument”, SSRN Research Paper No. 2558989.
Van Loo, R. 2018. “Making Innovation More Competitive: The Case of Fintech” UCLA Law Review.
Varian, H. R. 2014. “Big Data: New tricks for econometrics” Journal of Economic Perspectives.
Vaughan, T. M. 2009 “The Implications of Solvency II for U.S. Insurance Regulation”, Networks Financial Institute Policy Brief No. 2009-PB-03.
Verhoef, P. C. and Donkers, N. 2001. “Predicting Customer Potential Value: An Application in the Insurance Industry”, ERIM Report Series Reference No. ERS-2001-01-MKT.
Vezzoso, S. 2018. “Fintech, Access to Data, and the Role of Competition Policy” ”Competition and Innovation”, São Paulo.
Wachter, S. and Mittelstadt, B. 2018. “A Right to Reasonable Inferences: Re-Thinking Data Protection Law in the Age of Big Data and AI”, Columbia Business Law Review.
Ward, J. S. and Barker, A. 2013. “Undefined By Data: A Survey of Big Data Definitions” Cornell University Research Paper No. arXiv:1309.5821.
Wibisono, O. and Ari, H. D. and Widjanarti, A. and Andhika Zulen, A. and Tissot, B. 2019 “The use of big data analytics and artificial intelligence in central banking” IFC Bulletin no. 50.
Williamson, O. E. 1985. “The economic institution of capitalisms”, London.
Yeung, K. 2016. “Hypernudge: Big Data as a Mode of Regulation by Design”, Information, Communication & Society.
Zetzsche, D. A. and Preiner, C. 2017. “Cross-Border Crowdfunding – Towards a Single Crowdfunding Market for Europe”, University of Luxembourg Law Working Paper No. 2017/002.
Zirpoli, F. and Becker, M. C. 2008. “Organizing Complex Product Development: Outsourcing, Performance Integration and the Role of Product Architecture”, SSRN research paper no. 1087236.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2020 The Author(s)
About this chapter
Cite this chapter
Lemma, V. (2020). Fintech, Chain Transactions and Open Banking. In: FinTech Regulation. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-42347-6_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-42347-6_5
Published:
Publisher Name: Palgrave Macmillan, Cham
Print ISBN: 978-3-030-42346-9
Online ISBN: 978-3-030-42347-6
eBook Packages: Economics and FinanceEconomics and Finance (R0)