AccScience Publishing / IJOSI / Volume 9 / Issue 2 / DOI: 10.6977/IJoSI.202504_9(2).0009
ARTICLE

A blockchain-based solution to combating identity crime and credit card application fraud using data mining algorithms

Amol Jagdish Shakadwipi1* Dinesh Chandra Jain2 S. Nagini3
Show Less
1 Research Scholar, Department of Computer Science and Engineering, Oriental University, Indore, India
2 Research Supervisor, Department of Computer Science and Engineering, Oriental University, Indore, India
3 Research Co-Supervisor, Department of Computer Science and Engineering, Oriental University, Indore, India
Submitted: 28 November 2024 | Revised: 23 January 2025 | Accepted: 3 February 2025 | Published: 8 April 2025
© 2025 by the Author(s). Licensee AccScience Publishing, USA. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC BY-NC 4.0) ( https://creativecommons.org/licenses/by-nc/4.0/ )
Abstract

Fraud, specifically identity theft and credit card fraud, poses significant threats not only to financial institutions but also to their users. In response to this growing problem, we present an innovative approach that integrates self-sovereign identity management based on blockchain and complex data analysis. Our comprehensive solution is designed to revolutionize identity verification in credit card application processes by significantly enhancing security and reducing vulnerability to identity fraud. The system that will be developed from our solution will help users obtain self-sovereign identity credentials through blockchain technology or distributed ledger technology, granting them full control over their personal data. This approach has been proven to drastically reduce the likelihood of identity theft, and it does not require centralization of data. Besides, the use of blockchain technology ensures more credible records of identification, as they are transparent and immutable. At P&L, we combine smart data mining with blockchain-based identity solutions as our primary strategy. These algorithms detect patterns and anomalies related to identity theft in massive datasets. The technology can quickly flag suspicious activity and verify identity claims in real-time by continuously comparing recent user activity with historical data.

Keywords
Fraudulent
Credit Card Applications
Suspicious Activities
Vulnerability
Blockchain
Identity Verification
Identity Management
Identity Theft
References
  1. Alex, P., & Reed, D. (2021). *Self-Sovereign Identity: Decentralized Digital Identity and Verifiable Credentials*. United States: Manning Publications.
  2. Bolton, R.J., & Hand, D.J. (2001). A Survey of Credit Card Fraud Detection Techniques. *Expert Systems with Applications*, 20(4), 125-130.
  3. Doe, J., Smith, A., & Brown, B. (2022). Practical Implementation of Self-Sovereign Identity in Financial Services. *IEEE Transactions on Services Computing*, 15(1), 124-134.
  4. Herenj, A., & Mishra, S. (2013). Secure Mechanism for Credit Card Transaction Fraud Detection System. *International Journal of Advanced Research in Computer and Communication Engineering*, 2(2).
  5. Jha, S., Gupta, S., & Kumar, S. (2017). Fraud Detection in Banking Using Data Mining. *IEEE Transactions on Dependable and Secure Computing*, 14(3), 297-309.
  6. Kshetri, N., & Voas, J. (2020). Decentralised Identity Management on Blockchain. *IEEE Software*, 37(4), 76-82.
  7. Latchoumi, T.P., & Vijay Kannan, V.M. (2013). Synthetic Identity of Crime Detection. *International Journal of Advanced Research in Computer Science and Software Engineering*, 3(7), 551-560.
  8. Mistry, S., et al. (2019). A Blockchain-Based Identity Management System. *IEEE Transactions on Dependable and Secure Computing*, 16(6), 1025-1038.
  9. Phua, C., Smith-Miles, K., Lee, V., & Gayler, R. (2010). Adaptive Spike Detection for Resilient Data Stream Mining.
  10. Phua, C., Smith-Miles, K., Lee, V., & Gayler, R. (2012). Resilient Identity Crime Detection. *IEEE Transactions on Knowledge and Data Engineering*, 2(3), 533-546.
  11. Shakadwipi, A.J., Jain, D.C., & Nagini, S. (2023). Detection of Identity Theft in Credit Card Application Forms Through Data Mining Techniques Utilizing Multilayer Algorithms. *Journal of Namibian Studies*, 35, 49-64.
  12. Shakadwipi, A.J., Jain, D.C., & Nagini, S. (2023a). Credit Card Application Form Identity Crime Detection Using Data Mining Algorithm with Multilayer Algorithm. *SJIS*, 35(1), 212-218.
  13. Shukla, N., & Pandey, S. (2012). Document Fraud Detection with the Help of Data Mining and Secure Substitution Method with Frequency Analysis. *International Journal of Advanced Computer Research*, 2(2), 149.
  14. Smith, J., et al. (2022). Blockchain-Based Identity Verification for Financial Services. *IEEE Transactions on Services Computing*, 15(3), 1081-1093.
  15. Stallings, W., Li, C., & Rai, R. (2021). Self-Sovereign Identity Frameworks: A Comprehensive Review. *IEEE Internet Computing*, 25(1), 42-50.
  16. Swathi, M., & Kalpana, K. (2013). Spirit of Identity Fraud and Counterfeit Detection. *International Journal of Computer Trends and Technology (IJCTT)*, 4(6).
  17. Vidhya, K., & Dinesh Kumar, P. (2013). Multi-Secure Approach for Credit Card Application Validation. *International Journal of Computer Trends and Technology*, 4(2), 120-123.
  18. Wang, Y., Zhang, R., & Xie, T. (2019). Blockchain and Data Mining Integration for Improved Security. *IEEE Transactions on Industrial Informatics*, 15(8), 4691-4698.
  19. Zohrevand, A., et al. (2020). Blockchain and Data Mining Integration: A Survey. *IEEE Access*, 8, 23125-23149.
Share
Back to top
International Journal of Systematic Innovation, Electronic ISSN: 2077-8767 Print ISSN: 2077-7973, Published by AccScience Publishing