Blockchain-Based KYC Verification System Using IPFS and Smart Contracts

Abstract

Know Your Customer (KYC) verification is a critical regulatory requirement in financial and digital services, aimed at validating the identity of users and preventing fraudulent activities such as money laundering and identity theft. Traditional KYC systems rely on centralized databases that are susceptible to data breaches, unauthorized access, and significant operational inefficiencies arising from repetitive document submission processes. This paper presents a Blockchain-Based KYC Verification System that leverages Ethereum smart contracts and the Inter Planetary File System (IPFS) to deliver a secure, transparent, and tamper-proof identity verification mechanism. In the proposed system, user documents are uploaded through a React-based web interface and stored on IPFS via the Pinata gateway, which generates a unique Content Identifier (CID) for each document. This CID, along with associated user metadata, is subsequently recorded on the Ethereum blockchain using a Solidity smart contract, ensuring immutability and long-term data integrity. An administrative panel allows authorized personnel to review, approve, or reject KYC submissions, while users can independently track their verification status using their unique blockchain address. The backend infrastructure is implemented using FastAPI and Web3.py, enabling efficient interaction between the application layer and the blockchain. Experimental results confirm that the system significantly reduces redundancy, enhances security, and improves the overall efficiency of the KYC process compared to conventional centralized approaches.

Country : India

1 Amruta Patil2 Shivani Mehtre3 Chaitanya Nagpure4 Sumit Dey5 Venkat Patil

  1. Department of Computer Science and Engineering, Artificial Intelligence and Machine Learning, Smt. Indira Gandhi College of Engineering, Navi Mumbai, Maharashtra, India
  2. Department of Computer Science and Engineering, Artificial Intelligence and Machine Learning, Smt. Indira Gandhi College of Engineering, Navi Mumbai, Maharashtra, India
  3. Department of Computer Science and Engineering, Artificial Intelligence and Machine Learning, Smt. Indira Gandhi College of Engineering, Navi Mumbai, Maharashtra, India
  4. Department of Computer Science and Engineering, Artificial Intelligence and Machine Learning, Smt. Indira Gandhi College of Engineering, Navi Mumbai, Maharashtra, India
  5. Guide, Professor, Department of Computer Science and Engineering, Artificial Intelligence and Machine Learning, Smt. Indira Gandhi College of Engineering, Navi Mumbai, Maharashtra, India

IRJIET, Volume 10, Issue 4, April 2026 pp. 255-261

doi.org/10.47001/IRJIET/2026.104037

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