ba60f6118d1e33cbb54644 20240521040413631 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Recent Technology and Engineering (IJRTE) IJRTE 22773878 10.35940/ijrte.2277-3878 https://www.ijrte.org/ 05 30 2024 13 1 Vice President, Software Engineer III Bank of America. Govind Prasad Buddha https://orcid.org/0009-0000-5282-7964 Credit card fraud remains a significant challenge in the financial industry, posing substantial financial losses to both consumers and businesses. Traditional fraud detection methods often rely on rule-based approaches and statistical models, which may struggle to keep pace with evolving fraud tactics and sophisticated cyber threats. In this paper, we propose a novel approach to credit card fraud detection leveraging cryptographic hash functions. Cryptographic hash functions offer robust security guarantees, including collision resistance and preimage resistance, making them well-suited for ensuring the integrity and authenticity of transaction data. Our proposed system employs cryptographic hash functions, such as SHA-256, to generate unique hash values for credit card transactions. These hash values serve as digital fingerprints of the transaction data, enabling secure verification and auditing of transactions on the blockchain. We conducted experiments using a dataset of 100,000 credit card transactions, evaluating the performance of our system in terms of accuracy, precision, recall, and F1-score. The results demonstrate the effectiveness of our approach in accurately identifying fraudulent transactions while minimizing false positives. Furthermore, we discuss the implications of our findings and explore future research directions, including the integration of advanced cryptographic techniques and blockchain technology to enhance the security and privacy of credit card transactions. Overall, our study underscores the importance of cryptographic hash functions in building robust and secure fraud detection systems capable of combating emerging fraud threats in the digital era. 05 30 2024 16 21 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true International Journal of Recent Technology and Engineering (IJRTE) No, I didn’t receive. No conflicts of interest to the best of our knowledge. No, the article does not require ethical approval and consent to participate with evidence. Credit Card Transactions Fraud Detection Dataset (kaggle.com) I am only the sole author of the article. 10.35940/ijrte.F8019.13010524 https://www.ijrte.org/portfolio-item/F801912060324/