Vol. 4 No. 2 (2025): April
RESEARCH ARTICLES

Enhancing Credit Allocation With Blockchain-Based KYC

DOWNLOAD PDF
  • G Divya,
  • N Venkata Sadanandha,
  • M Prudhvi,
  • B Anil Kumar,
  • J Sandhya Rani
G Divya
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
N Venkata Sadanandha
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
M Prudhvi
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
B Anil Kumar
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
J Sandhya Rani
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
DOI: https://doi.org/10.5281/zenodo.15181579

Published 2025-04-09

Keywords

  • Know Your Customer (KYC),
  • blockchain,
  • Ethereum,
  • smart contracts,
  • decentralized systems,
  • credit allocation
    • ...More
    Less

How to Cite

G Divya, N Venkata Sadanandha, M Prudhvi, B Anil Kumar, & J Sandhya Rani. (2025). Enhancing Credit Allocation With Blockchain-Based KYC. International Journal of Computational Learning & Intelligence, 4(2), 440–447. https://doi.org/10.5281/zenodo.15181579

Copyright (c) 2025 G Divya, N Venkata Sadanandha, M Prudhvi, B Anil Kumar, J Sandhya Rani

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Abstract

The Know Your Customer (KYC) process is a foundation of the financial sector, ensuring compliance with anti-money laundering (AML) regulations and mitigating risks such as fraud and identity theft. However, traditional KYC systems are often centralized, time-consuming, and vulnerable to data breaches. This paper introduces a blockchain-based KYC system designed to address these challenges. By leveraging Ethereum smart contracts, the proposed model enables real-time sharing of consumer credit limits, risk profiles, and security information among financial institutions. The framework also incorporates a Proof-of-Stake (PoS) consensus mechanism to mitigate security threats like Sybil attacks. The results demonstrate that the blockchain-based KYC system significantly enhances operational efficiency, data security, and client experience while reducing costs.

DOWNLOAD PDF

References

  1. Lemieux, V. L. (2016). Trusting records: Is blockchain technology the answer? Records Management Journal, 26(2), 110–139.
  2. Viriyasitavat, W., & Hoonsopon, D. (2019). Blockchain characteristics and consensus in modern business processes. Journal of Industrial Information Integration, 13, 32–39.
  3. Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. Retrieved from https://bitcoin.org/bitcoin.pdf
  4. Perera, S., Nanayakkara, S., Rodrigo, M. N. N., Senaratne, S., & Weinand, R. (2020). Blockchain technology: Is it hype or real in the construction industry? Journal of Industrial Information Integration, 17, 1–20.
  5. Karadag, B., Akbulut, A., & Zaim, A. H. (2022). A review on blockchain applications in fintech ecosystem. In Proceedings of the International Conference on Advanced Creative Networks and Intelligent Systems (ICACNIS) (pp. 1–5). IEEE.
  6. Ethereum. (2023). Ethereum whitepaper. Retrieved from https://ethereum.org/en/whitepaper/
  7. Mansoor, N., Antora, K. F., Deb, P., Arman, T. A., Manaf, A. A., & Zareei, M. (2023). A review of blockchain approaches for KYC. IEEE Access, 11, 121013–121042.
  8. George, D., Wani, A., & Bhatia, A. (2019). A blockchain based solution to know your customer (KYC) dilemma. In Proceedings of the IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS) (pp. 1–6). IEEE.
  9. Roman, D., & Stefano, G. (2016). Towards a reference architecture for trusted data marketplaces: The credit scoring perspective. In Proceedings of the 2nd International Conference on Open and Big Data (OBD) (pp. 95–101). IEEE.
  10. Karaylan, H. (2019). Blockchain and its applications for financial technology solutions. Yüksek Öğretim Dergisi, 9(2), 123–135.
  11. Patel, S. B., Bhattacharya, P., Tanwar, S., & Kumar, N. (2021). KiRTi: A blockchain-based credit recommender system for financial institutions. IEEE Transactions on Network Science and Engineering, 8(2), 1044–1054.
  12. Madapuri, R. K., & Mahesh, P. C. S. (2017). HBS-CRA: Scaling impact of change request towards fault proneness: Defining a heuristic and biases scale (HBS) of change request artifacts (CRA). Cluster Computing, 22(S5), 11591–11599.
  13. Dwaram, J. R., & Madapuri, R. K. (2022). Crop yield forecasting by long short‐term memory network with Adam optimizer and Huber loss function in Andhra Pradesh, India. Concurrency and Computation: Practice and Experience, 34(27).
  14. Reddy, B. S. H. (2025). Deep learning-based detection of hair and scalp diseases using CNN and image processing. Milestone Transactions on Medical Technometrics, 3(1), 145–155.
  15. Reddy, B. S. H., Venkatramana, R., & Jayasree, L. (2025). Enhancing apple fruit quality detection with augmented YOLOv3 deep learning algorithm. International Journal of Human Computations & Intelligence, 4(1), 386–396.
  16. Ahmed, S. T., & Sandhya, M. (2019). Real-time biomedical recursive images detection algorithm for Indian telemedicine environment. In Cognitive Informatics and Soft Computing: Proceeding of CISC 2017 (pp. 723–731). Springer Singapore.
  17. Ahmed, S. T., Mahesh, T. R., Srividhya, E., Vinoth Kumar, V., Khan, S. B., Albuali, A., & Almusharraf, A. (2024). Towards blockchain based federated learning in categorizing healthcare monitoring devices on artificial intelligence of medical things investigative framework. BMC Medical Imaging, 24(1), 105.
  18. Ahmed, S. T., Sivakami, R., Banik, D., Khan, S. B., Dhanaraj, R. K., Mahesh, T. R., & Almusharraf, A. (2024). Federated learning framework for consumer IoMT-edge resource recommendation under telemedicine services. IEEE Transactions on Consumer Electronics.
  19. Ahmed, S. T., Basha, S. M., Arumugam, S. R., & Kodabagi, M. M. (2021). Pattern Recognition: An Introduction. MileStone Research Publications.
  20. Fathima, A. S., Basha, S. M., Ahmed, S. T., Khan, S. B., Asiri, F., Basheer, S., & Shukla, M. (2025). Empowering consumer healthcare through sensor-rich devices using federated learning for secure resource recommendation. IEEE Transactions on Consumer Electronics.