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This paper explores ve regulatory technology areas ripe for automation in regulation using blockchain technology: Q Intelligent regulatory advisor: an arti cial intelligent frontend to the regulatory handbook to simplify registration

Algorithmic Regulation: Automating Financial Compliance Monitoring and Regulation Using AI and Blockchain

Journal of financial transformation, (2017): 14-21

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摘要

Efficient financial regulation is crucial to the future success of the financial services industry and especially the rapidly evolving new financial technology (FinTech) area. The concept of “algorithmic regulation,” modelled on “algorithmic trading systems” [Treleaven et al. (2013)], is to stream compliance, social networks data, and oth...更多

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简介
  • There is a growing concern about regulation and compliance, which is increasingly perceived to have negative effects on the development of nancial services, discouraging innovation by requiring an ever-growing amount of data reporting.
重点内容
  • There is a growing concern about regulation and compliance, which is increasingly perceived to have negative effects on the development of nancial services, discouraging innovation by requiring an ever-growing amount of data reporting
  • This paper explores ve regulatory technology (RegTech) areas ripe for automation in regulation using blockchain technology: Q Intelligent regulatory advisor: an arti cial intelligent frontend to the regulatory handbook to simplify registration
  • We explore the potential structure of algorithmic regulation systems built upon blockchain smart contract technology
  • This paper presents the concept of algorithmic regulation modeled on the algorithmic trading paradigm, and employing technology under development for blockchain distributed ledgers and smart contracts
  • The ve major components are: “intelligent regulatory advisor,” “automated monitoring” of abuse, light-weight “automated reporting” principally for FinTech companies, “regulatory policy modeling,” and “automated regulation.” As discussed, algorithmic regulation applied to nance builds on the pioneering work of the R3 consortium of banks in the area of smart “ nancial” contracts, and any results will be applicable to smart “legal” contracts in general, and the “algorithmic regulation” paradigm applied to government, as proposed by Tim O’Reilly, the founder and CEO of O’Reilly Media Inc
  • What is clear is that blockchain smart contract technology will have a more major “disruptive” effect on legal services than FinTech is having on nancial services
结果
  • The concept – inspired by algorithmic trading systems – is a comprehensive automated system for compliance and regulation, where analytics is driven by regulations encoded as computer programs, leveraging blockchain smart contract technology.
  • Codified regulations and social media data to identify individuals’, rms’ and sector-wide potential abuse; c) “automated reporting” by regulated rms, notably FinTech companies; d) “regulatory policy” speci ed by international, government, and regulatory bodies; and e) “automated regulation” where regulations are codi ed, compliance reports are stored in a blockchain, and regulatory analytics is applied to identify abuse, regulatory breaches, and potential risks.
  • Automated monitoring: this covers scraping the web, social media sites, newspapers, blogs, and chat rooms, seeking to identify complaints about individuals and rms, and sector-wide abuse, such as the incorrect selling of Payment Protection Insurance (PPI) in the U.K. there is a number of commercial tools for harvesting web data, such as Adobe Social, Brandwatch, and Synthesio, identi cation of potential sources of online information remains a big challenge, since disadvantaged victims of small nancial rms are unlikely to use Twitter or Facebook to air their grievances.
  • Automated Regulation: lastly, automation comprises ve components: 1) the monitoring analytics component that uses sentiment analysis to identify individuals, rms, and sector-wide problems that may cause concern; 2) the compliance reports encoded using blockchain distributed ledger technology; 3) the compliance analytics component that seeks to identify regulatory breaches, AML, KYC, etc.; 4) the systemic risk component that seeks to identify major rms at risk (e.g., Solvency II); and 5) the regulatory rules component that contains codi ed regulations using Smart Contract technology.
  • This paper presents the concept of algorithmic regulation modeled on the algorithmic trading paradigm, and employing technology under development for blockchain distributed ledgers and smart contracts.
结论
  • The ve major components are: “intelligent regulatory advisor,” “automated monitoring” of abuse, light-weight “automated reporting” principally for FinTech companies, “regulatory policy modeling,” and “automated regulation.” As discussed, algorithmic regulation applied to nance builds on the pioneering work of the R3 consortium of banks in the area of smart “ nancial” contracts, and any results will be applicable to smart “legal” contracts in general, and the “algorithmic regulation” paradigm applied to government, as proposed by Tim O’Reilly, the founder and CEO of O’Reilly Media Inc. What is clear is that blockchain smart contract technology will have a more major “disruptive” effect on legal services than FinTech is having on nancial services.
引用论文
  • Batkins, S., and D. Goldbeck, 2016, “Six years after Dodd-Frank: higher costs, uncertain benefits,” The American Action Forum, July 20
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  • Batrinca, B., and P. Treleaven, 2015, “Social media analytics: a survey of techniques, tools and platforms,” AI & Society 30:1, 89-116
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  • Brummer, C., and D. Gorfine, 2014, “FinTech: building a 21st century regulator’s toolkit,” Center for Financial Markets, Milken Institute, http://bit.ly/1CRgQBP
    Findings
  • Lewis, A., 2015, “A gentle introduction to blockchain technology,” BraveNewCoin, http://bit.ly/2jdE8iz
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  • PayPal, 2013, “21st century regulation: putting innovation at the heart of payment regulation,” http://bit.ly/2hZzNid
    Findings
  • Szabo, N., 2002, “A formal language for analyzing contracts,” http://bit.ly/2i2IRpB
    Findings
  • Treleaven, P., M. Galas, V. Lalchand, 2013, “Algorithmic trading review,” Communications of the ACM 56:11, 76-85
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  • U.K. Government Office for Science, 2012, “The future of computer trading in the financial markets,” a report of the U.K. Government Chief Scientific Advisor, http://bit.ly/2j0LXZH
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  • U.K. Government Office for Science, 2015, “FinTech futures: the UK as a world leader in financial technologies,” a report of the U.K. Government Chief Scientific Advisor, March 18, http://bit.ly/1FCBDgS
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  • U.K. Government Office for Science, 2016, “Distributed ledger technology: beyond blockchain,” a report of the U.K. Government Chief Scientific Advisor, http://bit.ly/1WreVPL
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  • Walker, H. M., 1990, “Program verification (tutorial session): techniques and uses,” SIGCSE ’90 Proceedings symposium on Computer science education
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