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ZENODO
Dataset . 2026
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2026
License: CC BY
Data sources: Datacite
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Implementation of Blockchain Technology in Know Your Customer (KYC): RPPOT Model

Authors: Kenji Ariya Kennard; Michael Agustinus Santoso; William Finly Kusuma;

Implementation of Blockchain Technology in Know Your Customer (KYC): RPPOT Model

Abstract

Know Your Customer (KYC) is one of the key to preserving security in the financial sector. With KYC, we can verify a user identity through the required process just by analyzing various types of documents. However, traditional KYC still lacks time efficiency, too repetitive, and too risky as human error could be made. Therefore, blockchain exists to enhance KYC, growing significantly throughout the years and received good feedback for its efficiency and security. This study conducted Systematic Literature Review (SLR) based on the Regulatory, Process, People, Organization, Technology (RPPOT) model by identifying and analyzing essential factors in implementation of blockchain in KYC. There are 40 essential factors identified and categorized across the five RPPOT dimensions to demonstrate the varied nature of the transformation. Technology (T) resulted as the most frequently reported dimension highlighting the other essential specifications such as Security, Stability, and Interoperability. Success also does highlights Process (P) and Organization (O) factors, addressing the People (P) challenges, and followed a strict Regulatory (R) constraints. Furthermore, this research provides insightful analysis of the academic purposes, a multi-dimensional framework for financial institutions and policy makers in navigating the blockchain adoption in KYC.

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Keywords

Blockchain, Essential Factor, Know Your Customer (KYC), Systematic Literature Review

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    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average