Leveraging Cryptographic Hash Functions for Credit Card Fraud Detection
Govind Prasad Buddha
Govind Prasad Buddha, Vice President, Software Engineer III Bank of America.
Manuscript received on 27 February 2024 | Revised Manuscript received on 03 April 2024 | Manuscript Accepted on 15 May 2024 | Manuscript published on 30 May 2024 | PP: 16-21 | Volume-13 Issue-1, May 2024 | Retrieval Number: 100.1/ijrte.F801912060324 | DOI: 10.35940/ijrte.F8019.13010524
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Credit Card Fraud Detection, Cryptographic Hash Functions, Blockchain, Transactional Data.
Scope of the Article: Software Defined Networking and Network Function Virtualization