Can machine learning, as a RegTech compliance tool, lighten the regulatory burden for charitable organisations in the United Kingdom?

Charanjit Singh, Lei Zhao, Wangwei Lin and Zhen Ye

Journal of Financial Crime, 2021, vol. 29, issue 1, 45-61

Abstract: Purpose - Machine learning is having a major impact on banking, law and other organisations. The speed with which this technology is developing to undertake tasks that are not only complex and technical but also time-consuming and that are subject to constantly changing parameters is astounding. The purpose of this paper is to explore the extent to which machine learning can be used as a solution to lighten the compliance and regulatory burden on charitable organisations in the UK; so that they can comply with their regulatory duties and develop a coherent and streamlined action plan in relation to technological investment. Design/methodology/approach - The subject is approached through the analysis of data, literature and domestic and international regulation. The first part of the study summarises the extent of current regulatory obligations faced by charities, these are then, in the second part, set against the potential technological solutions provided by machine learning as of July 2021. Findings - It is suggested that charities can use machine learning as a smart technological solution to ease the regulatory burden they face in a growing and impactful sector. Originality/value - The work is original because it is the first to specifically explore how machine learning as a technological advance can assist charities in meeting the regulatory compliance challenge.

Keywords: Machine learning; RegTech; English law; Unsupervised learning; CharityTech (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:eme:jfcpps:jfc-06-2021-0131

DOI: 10.1108/JFC-06-2021-0131

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