FinTech Regulation by Valerio Lemma

FinTech Regulation by Valerio Lemma

Author:Valerio Lemma
Language: eng
Format: epub
ISBN: 9783030423476
Publisher: Springer International Publishing


5.3 Big Data Sets are Never Complete: The Dynamic Nature of Big Data as a Regulatory Matter

From a regulatory perspective, it is useful to assume that big data merits an autonomous approach because of its capacity to influence the market by developing self-standing relationships (which can influence cognitive capacity, information completeness, the establishment of operations and the chain that such operations will follow to complete a transaction).29 Hence, it is possible to consider that big data is the evolution of the traditional relational database and business decision-making analytics, augmented with a new capacity to exploit the sources of unstructured data and execute automatic operations in cases when the new data satisfies preprogrammed conditions.

As such, it is possible to state that any set of big data is never complete.

Therefore, augmenting existing operations is one of the characteristics that presents structural issues that should be resolved without having regard to the content of the set of big data (Flood, M. et al. 2014). So, this suggests that from a legal perspective there is no need to quantify the experiences to develop a juridical strategy aimed at managing its impact on individual rights, financial stability and the common welfare. However, it is necessary to bear in mind that, as any other software or fintech tool, big data has a logical-syntactical structure; hence, it is necessary to understand the nature of its structure (as prescriptive) and the relevant classification for regulatory purposes (Mullainathan, S. 2014). Notably, and perhaps unsurprisingly, this is focusing upon the relevant infrastructure of the set itself (Paech, P. 2016). In fact, it is worth considering big data as a set of information and technologies (regardless of the types) in order to present both a definition (of big data) and a regulation (for big data).

It is clear that the study of big data (for regulatory purposes) does not deny other methodologies for considering fintech tools and big data analytics. If the analysis pursues understanding the convenience for public intervention over such phenomena, then the object of this research cannot be not limited to definitions, but extents to contents (which refers to the operations that will affect the people and the market).30 Hence, the study of the form of big data would not be a formalistic study of it. This is why it is worth considering big data with respect to technologies such as machine learning and artificial intelligence (by supporting the concept of ‘set’ itself, also being involved with data-related tools that are a key element of the aforesaid definition), and several examples provide evidence which suggests that there is a growing set of technologies frequently involved in big data analytics.31 Thus, it is possible to rely on such complexity to develop both a holistic definition and comprehensive regulation of the fintech environment (as a whole).

However, at this stage of the analysis, the preconception that big data is challenging the current paradigms and practices cannot be limited to data mining, nor data analytics. It involves the fintech activities based upon big data, and the strategy developed by their programmers.



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