The project was awarded in 2017 with the funds of PRIN (Progetti di Ricerca di Interesse Nazionale). The research group is focused on studying different, yet interwoven technologies that are drastically re-shaping the current banking and financial background and enabling important new opportunities for financial players: digital platforms, big data, automating algorithms and distributed ledger technology (DLT).
Fintech Regulation aims to acquire a whole comprehension of these technologies and to understand both their benefits and risks for all stakeholders (users, financial or technological players and the market) in order to set the grounds for a FinTech regulation that can balance the interests of financial stability and consumer protection with those of technology innovation and financial competition. In such a way, it is thought FinTech can correctly develop, benefit the recovery of the economy and boost the financing of small medium enterprises (SMEs). Regarding this, the research also aims at analyzing the regulatory policies that have been developing internationally to address the “FinTech revolution” and assessing the effectiveness of their potential implementation.
Digital platforms
Digital platforms connect two or more sides enabling the creation of business models that create value by leveraging powerful network effects and facilitating exchanges between two or more interdependent groups. Consequently, digital platforms are adopted to ease producers and end-users transacting with each other. More specifically, digital platforms are used by FinTech players to launch (equity and lending-based) crowdfunding with all the regulatory issues it entails.
Moreover, when integrated with cloud computing services, digital platforms also enable companies to share computer processing resources and information with each other, enhancing their collaboration in financial innovation and promoting both efficiencies and economies of scale.
Indeed, cloud computing reduces the need to build and maintain the infrastructures typically associated with innovation as well as many of their complexities, drastically easing different remote users to develop, run and manage new applications. All of this drives new opportunities in the area of payments. For instance, payment-as-you-go business models, in which the service price is charged based on consumer usage.
BIG DATA
Big data is the enabling technology that allows the creation of extremely large datasets of varied data that can be computationally analyzed on a real-time basis to reveal patterns, trends and associations, especially those regarding human behavior and interactions. Financial institutions have always used data, but now, the type and sources of data as well as the use and type of data analytics tools is growing exponentially. The relevance of technology-driven applications in almost every segment of the value chain of the financial services sectors has accelerated the evidence to varying extents in the banking, insurance and securities sectors. More broadly, big data may be used by institutions with a variety of business models, both traditional institutions and newcomers, to gain from innovation and improve their competitiveness.
In particular, all kinds of financial activities/products could be impacted as the use of big data technologies may serve various purposes: profile consumers, customer loyalty management (including monitoring consumer sentiment towards products/institutions), creditworthiness assessments, marketing campaigns, market segmentation decisions, product development, other risk assessment, suitability/appropriateness tests, demands and needs tests, pricing products/services, underwriting risk, fraud prevention and AML/customer identification through supervisory technology (SupTech), increase internal efficiency within firms through regulatory technology (RegTech), help business decision-making, support finance and risk control activities, assess selling processes/distribution, increase revenues through the commercialization of data, etc.
Thanks to big data, costs of research are reduced while accuracy of relevant information is preserved. Consequently, both needs and preferences of current (and potential) customers can be wholly understood and the placement of financial products tailored customer by customer.
ALGORITHMS
Algorithms are used for the automation of human-based processes still characterizing financial activities. The potential implementations of such algorithms are extremely wide as well as the risks they drive. In the last years, they led to the rise of high-frequency trading (HFT) and automated financial consultancy (also known as robo-advisory). Moreover, there liance on algorithms has steadily increased to improve credit-scoring assessments. In addition, the use algorithms have been recently introduced in managing regulatory and supervisory processes, respectivelyl eading to the rise of the above mentioned RegTech and SupTech.
Regarding algorithms, our research expands also to those adopted in the field of artificial intelligence (AI), which is the information technology that mimics tasks normally requiring human intelligence (such as visual perception or speech recognition). More specifically, the research focuses on the subset of AI named “machine learning” that is the adoption of self-learning algorithms enabling computers to learn from previous datasets analysis and to illuminate new invariants and patterns unfolding within financial markets.
DLT
Distributed Ledger Technology (DLT) is a decentralized transaction and data management technology that leverages computer protocols replicating the same ledger and updating them without the need for a central responsible entity. More specifically, DLTs allow all the participants of a same network (the so-called nodes) to record information on the ledger and to access it on a real-time basis. Among DLTs, the technology of blockchain currently plays a pivotal role, also in light of the growing interests of markets on its most famous application, the Bitcoin. In a blockchain, “blocks” are sets of data which are secured by cryptography and then linked to the previous block. Each single part of the chain cannot be changed without breaking the sequence, thus reducing the risk that information is modified by a third party, ensuring data integrity; in addition, the management of the chain (i.e., the insertion of new blocks at the end of the chain) can be attributed by a network of separate entities, thus making the network extremely resilient.
Aside from blockchains, DLTs can be enabled by different technologies and the features of each DLT arrangement can significantly vary. The common element, however, is the capacity of the network to be managed neither without a single entity being attributed a dominant position (i.e., writing/management privileges) and guarding against double-spending (or similar) risks, nor without resorting to expensive processes to reconcile information between ledgers. Combining cryptography, game theory and distributed consensus principles, blockchain technologies promise to offer a new solution to the never-ending human problem of trust.