Purpose Decentralized finance (DeFi), enabled by blockchain, could bring about a new financial system, where peers will interact directly, with little or no place for traditional intermediation. However, some crucial tasks cannot be left solely to an algorithm and, consequently, most DeFi applications still require human decisions. The aim of this research is to assess the role of intermediation in the light of DeFi, analysing how humans and algorithms will interact. Design/methodology/approach The authors based their work on a twofold qualitative methodology, first analysing publicly available secondary data, particularly from white papers and DeFi Pulse (a website providing data on DeFi solutions) and then running two focus group discussions. Findings DeFi does not eliminate financial intermediation, but enables it to be performed in new ways, where decentralization means that no single entity can hold too much power or monopoly. DeFi has, however, inherited risks from the underlying technologies that unintentionally facilitate illegal behaviour and can hamper the authorities’ supervision. The complex duality algorithm- vs human-based actions will not be solved indisputably in favour of the former, as DeFi solutions can range from requiring algorithms to play a dominant role, to enabling greater human interaction by actively involving more people. Originality/value This research contributes to the emerging debate between algorithm- and human-based intermediation, especially in relation to the standing literature on financial intermediation, where considerations made in the light of the newest theories on blockchain and DeFi are still scarce.

In recent years, Insurtech innovations, driven by technologies such as artificial intelligence and blockchain, emerged in the insurance industry, with the promise of improving efficiency. However, while the positive impact of technology on insurance companies’ efficiency is expected, literature assessing it empirically is scarce, when it comes to recent technological change. Focusing on the US public P&C insurance sector in the period 2012–2018 and relying on both nonparametric (two stage DEA) and parametric (SFA) approaches, it emerges that on average insurance companies were not able to leverage on technological innovations to improve their efficiency. On average a relative level of efficiency among companies, according to a two stage DEA model, was quite stable in time, while the SFA approach shows that the distance between efficient and less efficient firms slightly increased. Moreover, we found one very efficient firm, almost a leader of the market in terms of efficiency, and a homogeneous group of followers, indicating that there is vast scope for improvement for less efficient companies. Nevertheless, even the most efficient company impaired its efficiency over time, suggesting that neither the leader nor on average the followers properly leveraged technology to improve their efficiency. In a competitive scenario, with new players’ entrance and fierce competition, inertia may seriously affect their positioning. Academicians, managers and policymakers should carefully consider the effects that a non-improvement of efficiency following technological change may have on market structure, competition and regulations, potentially opening to further discussion on how technological innovations adoption should be facilitated.

Higher regulatory compliance requirements, fast and continuous changes in regulations and high digital dynamics in the financial markets are powering RegTech (regulatory technology), defined as technology‐enabled innovation applied to the world of regulation, compliance, risk management, reporting and supervision. This work builds on a systematic literature review and a bibliometric analysis of the literature on RegTech, its influential papers and authors, its main areas of research, its past and its future. The resulting multi-dimensional framework bridges across four main dimensions, starting with regulation and technology, where one or more regulations, not necessarily financial ones, are addressed with the support of technologies (e.g. artificial intelligence, DLT, blockchain, smart contracts, API). Data play a central role, as sharing them enables data ecosystems, where additional value can be attained by each market participant, while data automation and machine-readable regulations empower regulators to pull data directly from the banks’ systems and combine these data with data obtained directly from customers or other external sources. Several applications emerge, both for regulated entities, covering matters of compliance, monitoring, risk management, reporting and operations, as well as for authorities, which can leverage on RegTech (SupTech) solutions to make policies, to undertake their authorising, supervising and enforcement operations, for monitoring and controlling purposes, and even to issue fines automatically. As a consequence, stakeholders can reap a series of benefits, such as higher efficiency and effectiveness, accuracy, transparency and lower compliance costs but also risks, such as cyber risk, algorithmic biases, and dehumanization.

No abstract