CREDIT CARD FRAUD DETECTION USING BLOCKCHAIN AND GRASSMANN ALGORITHM
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The rapid expansion of digital payments and e-commerce has significantly increased the risk of credit card fraud, exposing the inadequacy of traditional authentication mechanisms such as passwords, PINs, and CVV codes. These credential-based methods verify only what a person possesses or knows, not who they physically are — a distinction that fraudsters readily exploit.
This project presents a secure online shopping and payment platform that addresses this gap by integrating facial biometric authentication with blockchain-based transaction recording. The Grassmann algorithm is applied for face recognition, enabling robust identity verification across real-world variations in lighting, facial expression, and head orientation. At the point of payment, the system captures a live facial image, processes it through the Grassmann subspace matching framework, and compares it against the registered cardholder's stored biometric profile. Only a verified match permits the transaction to proceed.
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M, S., S, S. B., N, S. P. & J, R. S. (2026). Credit Card Fraud Detection using Blockchain and Grassmann Algorithm. International Journal of Science, Strategic Management and Technology, 02(04). https://doi.org/10.55041/ijsmt.v2i4.276
M, Saffana, et al.. "Credit Card Fraud Detection using Blockchain and Grassmann Algorithm." International Journal of Science, Strategic Management and Technology, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i4.276.
M, Saffana,Shabana S,Shanmuga N, and Raveenaa J. "Credit Card Fraud Detection using Blockchain and Grassmann Algorithm." International Journal of Science, Strategic Management and Technology 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i4.276.
References
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[6]. Alarfaj, F. K., and Shahzadi, S. "Enhancing fraud detection in banking with deep learning: graph neural networks and autoencoders for real-time credit card fraud prevention." IEEE
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