Blockchain-enabled KYC integration for CLV optimization with robust M-Estimation and IRLS method

This research introduces an innovative approach in implementing Know Your Customer (KYC) on blockchain technology as a means of using data, hybrid robust m-estimation, and the iteratively reweighted less squares (IRLS) method to optimize CLV data. This approach aims to improve the accuracy and reliability of CLV predictions by ensuring the security and reliability of customer data. This tool can help companies manage and increase CLV more effectively, meeting data security and compliance standards. The R-squared validation test results are close to 1, so the model can explain data variations well. RMSE and MSE have small values, ​​so the model has good performance in predicting the target value. With these achievements, this approach contributes to the development of better marketing strategies and business decisions in an increasingly complex and rapidly changing digital environment.

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Publication Details

Journal
ICT Express
Published
2025-04-01
DOI
https://doi.org/10.1016/j.icte.2025.03.006
Citations
4
Primary Topic
Vehicle License Plate Recognition
Type
article
Field-Weighted Citation Impact
4.54
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article

Blockchain-enabled KYC integration for CLV optimization with robust M-Estimation and IRLS method

Tengku Siti Meriam Tengku Wook, Fanindia Purnamasari, Mahyuddin K. M. Nasution, Marischa Elveny
4 citations
ICT Express
Vehicle License Plate Recognition
4.54
article

Blockchain-enabled KYC integration for CLV optimization with robust M-Estimation and IRLS method

Tengku Siti Meriam Tengku Wook, Fanindia Purnamasari, Mahyuddin K. M. Nasution, Marischa Elveny
article en
4 citations

Abstract

This research introduces an innovative approach in implementing Know Your Customer (KYC) on blockchain technology as a means of using data, hybrid robust m-estimation, and the iteratively reweighted less squares (IRLS) method to optimize CLV data. This approach aims to improve the accuracy and reliability of CLV predictions by ensuring the security and reliability of customer data. This tool can help companies manage and increase CLV more effectively, meeting data security and compliance standards. The R-squared validation test results are close to 1, so the model can explain data variations well. RMSE and MSE have small values, ​​so the model has good performance in predicting the target value. With these achievements, this approach contributes to the development of better marketing strategies and business decisions in an increasingly complex and rapidly changing digital environment.

ICT ExpressVol. 11(3)
Universitas Sumatera Utara (ID), National University of Malaysia (MY)
Openalex Percentile: Top 6%
Vehicle License Plate Recognition
4.54
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