9 research outputs found

    Stakeholder Survey Preliminary White Paper

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    With a generous grant from the Columbia-IBM Center for Blockchain and Data Transparency, the Columbia Law School/Business School Program in the Law and Economics of Capital Markets (the “Program”) is conducting a survey of domestic and international securities markets regulators, entrepreneurs, private industry leaders, legal practitioners, academics, and other stakeholders in the securities markets (the “Survey”). The Survey is designed to provide a deeper understanding of what these stakeholders consider to be the most important issues raised by distributed ledger technology (“DLT”) for the securities markets and their regulation. DLT is an integral part of the larger revolution in computing, communication, and data storage that has transformed securities markets over the last few decades and promises further radical change in the years to come

    Stakeholder Survey Preliminary White Paper

    No full text
    With a generous grant from the Columbia-IBM Center for Blockchain and Data Transparency, the Columbia Law School/Business School Program in the Law and Economics of Capital Markets (the “Program”) is conducting a survey of domestic and international securities markets regulators, entrepreneurs, private industry leaders, legal practitioners, academics, and other stakeholders in the securities markets (the “Survey”). The Survey is designed to provide a deeper understanding of what these stakeholders consider to be the most important issues raised by distributed ledger technology (“DLT”) for the securities markets and their regulation. DLT is an integral part of the larger revolution in computing, communication, and data storage that has transformed securities markets over the last few decades and promises further radical change in the years to come

    Blockchain Technology: Supply Chain Insights from ERP

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    The chapter provides a high level understanding of how ERP system alongside Blockchain technology will be a powerful tool to improve supply chain operations. The chapter details out how the two technologies will complement each other in every aspect of supply chain functions bringing in transparency, efficiency, and cost reduction. The chapter considers every aspects of supply chain for an ERP enabled organizations and details out use cases for master data, engineering design, sales process, procurement process, demand and supply planning process, manufacturing process, and logistics management processes. The chapter provides use case details and high level understanding of technology for product provenance and how it can bring in supply chain transparency using blockchain. The chapter illustrates theoretical and conceptual model for use of open and permissioned blockchain in different supply chain applications with real life practical use cases as is being developed and deployed in various industries and business functions. The chapter also emphasizes the use of blockchain in distribution industry and how it can solve pertinent problems as it exists today in the distribution supply chain. The chapter ends with an outlook of blockchain how it will shape the future to come and challenges which lies there within

    Blockchain in agriculture

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    Blockchain is an emerging digital technology allowing ubiquitous financial transactions among distributed untrusted parties, without the need of intermediaries such as banks. This chapter examines the impact of blockchain technology in agriculture and food supply chain, presents existing ongoing projects and initiatives, and discusses overall implications, challenges and potential, with a critical view over the maturity of these projects. Our findings indicate that blockchain is a promising technology towards a transparent supply chain of food, with many ongoing initiatives in various food products and food-related issues, but many barriers and challenges still exist, which hinder its wider popularity among farmers and systems. These challenges involve technical aspects, education, policies and regulatory frameworks.Comment: arXiv admin note: substantial text overlap with arXiv:1908.0739

    How Artificial Intelligence and machine learning research impacts payment card fraud detection: A survey and industry benchmark

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