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2019 | OriginalPaper | Chapter

14. How Technology (or Distributed Ledger Technology and Algorithms Like Deep Learning and Machine Learning) Can Help to Comply with Regulatory Requirements

Authors : Moritz Plenk, Iosif Levant, Noah Bellon

Published in: The Impact of Digital Transformation and FinTech on the Finance Professional

Publisher: Springer International Publishing

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Abstract

An ever-increasing volume of data at financial institutions combined with a regulatory tsunami leads to problems that are difficult to solve with the current methods in order to meet the regulatory requirements. This article investigates how Distributed Ledger Technology (DLT), Artificial Intelligence (AI) and Machine Learning (ML) can open new horizons for the regulatory value chain and can be seen as possible cost reducers. The power of AI and ML is based on the data being used. In this article, we will take a closer look at how DLT could improve regulatory reporting quality and processes and what implications DLT can have for banks and regulators with respect to regulatory reporting. Moreover, it investigates how AI can support us in regulatory reporting, using MiFID 2 as an example. It discusses what risks can be mitigated for Anti Money Laundering (AML), Know Your Customer (KYC) and how the generic intelligent stress tests help to meet the minimum requirements. Lastly, it describes how natural language processing (NLP) can reduce the regulatory burden.

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Footnotes
1
English and Hammond, Cost of compliance 2018 (2018).
 
2
BearingPoint Institute (2015).
 
3
Bauerle, What is the difference blockchain and database (2018b)
 
4
Brown, Carlyle, Grigg, and Hearn (2016).
 
5
World Economic Forum (2016).
 
6
Brown et al. (2016).
 
7
Nakamoto (2008).
 
8
Brown et al. (2016).
 
9
Brown et al. (2016).
 
10
Bauerle, What can a blockchain do (2018a).
 
11
Hong Kong Applied Science and Technology Research Institute (2016).
 
12
English and Hammond, Cost of Compliance 2017 (2017); and English and Hammond, Cost of Compliance 2018 (2018).
 
14
CMR (2018).
 
15
Grasshoff et al. (2017).
 
16
CNBC (2018).
 
Literature
go back to reference BearingPoint Institute. (2015). Reforming regulatory reporting: Are we headed toward real-time? Bearingpoint. BearingPoint Institute. (2015). Reforming regulatory reporting: Are we headed toward real-time? Bearingpoint.
go back to reference Brown, R. G., Carlyle, J., Grigg, I., & Hearn, M. (2016). Corda: An introduction. Brown, R. G., Carlyle, J., Grigg, I., & Hearn, M. (2016). Corda: An introduction.
go back to reference English, S., & Hammond, S. (2017). Cost of compliance 2017. Thomson Reuters. English, S., & Hammond, S. (2017). Cost of compliance 2017. Thomson Reuters.
go back to reference English, S., & Hammond, S. (2018). Cost of compliance 2018. Thomson Reuters. English, S., & Hammond, S. (2018). Cost of compliance 2018. Thomson Reuters.
go back to reference Hong Kong Applied Science and Technology Research Institute. (2016). Whitepaper on distributed ledger technology. Hong Kong Monetary Authority. Hong Kong Applied Science and Technology Research Institute. (2016). Whitepaper on distributed ledger technology. Hong Kong Monetary Authority.
Metadata
Title
How Technology (or Distributed Ledger Technology and Algorithms Like Deep Learning and Machine Learning) Can Help to Comply with Regulatory Requirements
Authors
Moritz Plenk
Iosif Levant
Noah Bellon
Copyright Year
2019
DOI
https://doi.org/10.1007/978-3-030-23719-6_14