Fintech and the Innovation Trilemma

Whether in response to roboadvising, artificial intelligence, or crypto-currencies like Bitcoin, regulators around the world have made it a top policy priority to supervise the exponential growth of financial technology (or fintech ) in the post-Crisis era. However, applying traditional regulatory strategies to new technological ecosystems has proven conceptually difficult. Part of the challenge lies in the tradeoffs involved in regulating innovations that could conceivably both help and hurt consumers and market participants alike. Problems also arise from the common assumption that today\u27s fintech is a mere continuation of the story of innovation that has shaped finance for centuries. This Article provides a novel theoretical framework for understanding and regulating fintech by showing how the supervision of financial innovation is invariably bound by what can be described as a policy Trilemma. Specifically, we argue that when seeking to provide clear rules, maintain market integrity, and encourage financial innovation, regulators have long been able to achieve, at best, two out of the three goals. Moreover, today\u27s innovations exacerbate the tradeoffs historically embodied in the Trilemma by either reconfiguring or disintermediating traditional financing operations and the discrete services supporting them, thereby introducing unprecedented uncertainty as to their risks and benefits. This Article thus proceeds to catalogue the strategies taken by regulatory authorities to navigate the Trilemma, and posits them as operating across a spectrum of interrelated responses. It then proposes supplemental administrative tools to support not only market, but also regulatory data gathering and experimentation

Dealing with Disruption: Emerging Approaches to Fintech Regulation

“Fintech” refers to a variety of digital assets, technologies, and infrastructure that deal with the operation of today’s financial markets. The regulation of this presents both legal and regulatory challenges. This article examines the regulatory responses to fintech disruption; specifically, the “experimentation” approach, the “incorporation” approach, and the “accommodation” approach. These approaches provide a baseline for further discussion and policy analysis in response to “Fintech.

Dealing with Disruption: Emerging Approaches to Fintech Regulation

“Fintech” refers to a variety of digital assets, technologies, and infrastructure that deal with the operation of today’s financial markets. The regulation of this presents both legal and regulatory challenges. This article examines the regulatory responses to fintech disruption; specifically, the “experimentation” approach, the “incorporation” approach, and the “accommodation” approach. These approaches provide a baseline for further discussion and policy analysis in response to “Fintech.

Dealing with Disruption: Emerging Approaches to Fintech Regulation

“Fintech” refers to a variety of digital assets, technologies, and infrastructure that deal with the operation of today’s financial markets. The regulation of this presents both legal and regulatory challenges. This article examines the regulatory responses to fintech disruption; specifically, the “experimentation” approach, the “incorporation” approach, and the “accommodation” approach. These approaches provide a baseline for further discussion and policy analysis in response to “Fintech.

Cost- and workload-driven data management in the cloud

This thesis deals with the challenge of finding the right balance between consistency, availability, latency and costs, captured by the CAP/PACELC trade-offs, in the context of distributed data management in the Cloud. At the core of this work, cost and workload-driven data management protocols, called CCQ protocols, are developed. First, this includes the development of C3, which is an adaptive consistency protocol that is able to adjust consistency at runtime by considering consistency and inconsistency costs. Second, the development of Cumulus, an adaptive data partitioning protocol, that can adapt partitions by considering the application workload so that expensive distributed transactions are minimized or avoided. And third, the development of QuAD, a quorum-based replication protocol, that constructs the quorums in such a way so that, given a set of constraints, the best possible performance is achieved. The behavior of each CCQ protocol is steered by a cost model, which aims at reducing the costs and overhead for providing the desired data management guarantees. The CCQ protocols are able to continuously assess their behavior, and if necessary to adapt the behavior at runtime based on application workload and the cost model. This property is crucial for applications deployed in the Cloud, as they are characterized by a highly dynamic workload, and high scalability and availability demands. The dynamic adaptation of the behavior at runtime does not come for free, and may generate considerable overhead that might outweigh the gain of adaptation. The CCQ cost models incorporate a control mechanism, which aims at avoiding expensive and unnecessary adaptations, which do not provide any benefits to applications. The adaptation is a distributed activity that requires coordination between the sites in a distributed database system. The CCQ protocols implement safe online adaptation approaches, which exploit the properties of 2PC and 2PL to ensure that all sites behave in accordance with the cost model, even in the presence of arbitrary failures. It is crucial to guarantee a globally consistent view of the behavior, as in contrary the effects of the cost models are nullified. The presented protocols are implemented as part of a prototypical database system. Their modular architecture allows for a seamless extension of the optimization capabilities at any level of their implementation. Finally, the protocols are quantitatively evaluated in a series of experiments executed in a real Cloud environment. The results show their feasibility and ability to reduce application costs, and to dynamically adjust the behavior at runtime without violating their correctness