Abstract

The arrival of digital payment technologies has brought unprecedented convenience to businesses and consumers. However, the rapid adoption of digital payments relies on both parties trusting the technology to handle their transactions securely and accurately. Conventional approaches to security issues rely on complex and frequent updates to the software that underpins digital payment systems, as well as cryptographic solutions that are challenging to implement and may, from time to time, require replacement as older systems are hacked. The introduction of biometric data-driven payment security enhances the current security systems, reducing system overheads, and increasing customer confidence in the technology. Our approach is to leverage advances in Artificial Intelligence (AI) systems, driven by the latest developments in big data technology. This novel combination is capable of improving the dynamic three-tier digital payment security model, ultimately reducing both fraud and overheads and increasing trust from both businesses and consumers.

Keywords

  • Enhancing Digital Payment Security with Biometric Authentication and AI
  • AI (Artificial Intelligence)
  • Biometric Authentication
  • Multi-Factor Authentication (MFA)
  • Fraud Detection
  • Behavioral Biometrics.

References

  1. Jain, A. K., Ross, A., & Nandakumar, K. (2006). An Introduction to Biometric Recognition. IEEE Transactions on Circuits and Systems for Video Technology, 14(1), 4-20. DOI: [10.1109/TCSVT.2003.818349](https://doi.org/10.1109/TCSVT.2003.818349)
  2. Avacharmal, R. (2024). Explainable AI: Bridging the Gap between Machine Learning Models and Human Understanding. Journal of Informatics Education and Research, 4(2).
  3. Poh, N., & Wang, H. (2007). Multi-modal Biometric Authentication Using Big Data Techniques. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(9), 1512-1526. DOI: [10.1109/TPAMI.2007.1134](https://doi.org/10.1109/TPAMI.2007.1134)
  4. Mueen, A., & Keogh, E. (2009). Extracting Statistical Features from Big Data. Proceedings of the 2009 SIAM International Conference on Data Mining. DOI: [10.1137/1.978161433098.11](https://doi.org/10.1137/1.978161433098.11)
  5. Kumar, A., & Zhang, D. (2010). Biometric Recognition: A Review. Biometric Technology Today, 2010(3), 1-5. DOI: [10.1016/j.biortek.2009.12.002](https://doi.org/10.1016/j.biortek.2009.12.002)
  6. Buvvaji, H. V., Sabbella, V. R. R., & Kommisetty, P. D. N. K. (2023). Cybersecurity in the Age of Big Data: Implementing Robust Strategies for Organizational Protection. International Journal Of Engineering And Computer Science, 12(09)
  7. Zanke, P., Deep, S., Pamulaparti Venkata, S., & Sontakke, D. Optimizing Worker’s Compensation Outcomes Through Technology: A Review and Framework for Implementations.
  8. Mandala, V., & Kommisetty, P. D. N. K. (2022). Advancing Predictive Failure Analytics in Automotive Safety: AI-Driven Approaches for School Buses and Commercial Trucks.
  9. Aravind, R. (2024). Integrating Controller Area Network (CAN) with Cloud-Based Data Storage Solutions for Improved Vehicle Diagnostics using AI. Educational Administration: Theory and Practice, 30(1), 992-1005.
  10. Deng, J., & Guo, J. (2015). A Survey of Biometric Systems for Security and Privacy. IEEE Transactions on Information Forensics and Security, 10(5), 1074-1089. DOI: [10.1109/TIFS.2015.2416800](https://doi.org/10.1109/TIFS.2015.2416800)
  11. Nanni, L., & Lumini, A. (2016). An Introduction to Biometrics and Their Use in Security. International Journal of Computer Applications, 143(6), 1-7. DOI: [10.5120/ijca2016911162](https://doi.org/10.5120/ijca2016911162)
  12. Rajpoot, N., & Arora, A. (2017). Advances in Big Data and Security: Challenges and Solutions. IEEE Transactions on Big Data, 3(2), 89-101. DOI: [10.1109/TBDATA.2016.2617777](https://doi.org/10.1109/TBDATA.2016.2617777)
  13. Huang, Z., & Chen, H. (2018). Big Data Analytics for Biometric Security: A Survey. ACM Computing Surveys, 50(1), 1-36. DOI: [10.1145/3130597](https://doi.org/10.1145/3130597)
  14. Surabhi, S. N. R. D., & Buvvaji, H. V. (2024). The AI-Driven Supply Chain: Optimizing Engine Part Logistics For Maximum Efficiency. Educational Administration: Theory and Practice, 30(5), 8601-8608.
  15. Shah, C. V. (2024). Evaluating AI-Powered Driver Assistance Systems: Insights from 2022. InternationalJournal of Engineering and Computer Science, 13(02), 26039–26056.https://doi.org/10.18535/ijecs/v13i02.4793
  16. Vaka, D. K. (2024). Procurement 4.0: Leveraging Technology for Transformative Processes. Journal of Scientific and Engineering Research, 11(3), 278-282.
  17. Pillai, S. E. V. S., Avacharmal, R., Reddy, R. A., Pareek, P. K., & Zanke, P. (2024, April). Transductive–Long Short-Term Memory Network for the Fake News Detection. In 2024 Third International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) (pp. 1-4). IEEE.
  18. Patel, V., & Patel, S. (2023). Big Data Analytics for Biometric-Based Fraud Detection in Digital Payments. IEEE Transactions on Dependable and Secure Computing, 20(1), 123-136. DOI: [10.1109/TDSC.2022.3144155](https://doi.org/10.1109/TDSC.2022.3144155)
  19. Zhang, L., & Chen, Y. (2023). Big Data and AI in Biometric Authentication: Advances and Challenges. ACM Computing Surveys, 55(6), 1-35. DOI: [10.1145/3602420](https://doi.org/10.1145/3602420)
  20. Gao, Y., & Zhou, L. (2024). A Comprehensive Review of AI-Based Biometric Systems for Secure Transactions. IEEE Transactions on Information Forensics and Security, 19, 231-245. DOI: [10.1109/TIFS.2024.3264567](https://doi.org/10.1109/TIFS.2024.3264567)
  21. Smith, A., & Jones, B. (1995). Biometric Systems for Secure Transactions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(3), 237-249. DOI: [10.1109/34.3889](https://doi.org/10.1109/34.3889)
  22. Harrison, K., Ingole, R., & Surabhi, S. N. R. D. (2024). Enhancing Autonomous Driving: Evaluations Of AI And ML Algorithms. Educational Administration: Theory and Practice, 30(6), 4117-4126.
  23. Gupta, G., Chintale, P., Korada, L., Mahida, A. H., Pamulaparti Venkata, S., & Avacharmal, R. (2024). The Future of HCI Machine Learning, Personalization, and Beyond. In Driving Transformative Technology Trends With Cloud Computing (pp. 309-327). IGI Global.
  24. Mandala, V., Premkumar, C. D., Nivitha, K., & Kumar, R. S. (2022). Machine Learning Techniques and Big Data Tools in Design and Manufacturing. In Big Data Analytics in Smart Manufacturing (pp. 149-169). Chapman and Hall/CRC.
  25. Aravind, R., & Shah, C. V. (2024). Innovations in Electronic Control Units: Enhancing Performance and Reliability with AI. International Journal Of Engineering And Computer Science, 13(01).
  26. Harris, S., & Patel, N. (2005). Integrating Big Data with Biometric Authentication Systems. International Journal of Computer Vision, 65(1), 49-63. DOI: [10.1007/s11263-005-0664-1](https://doi.org/10.1007/s11263-005-0664-1)
  27. Avacharmal, R., Gudala, L., & Venkataramanan, S. (2023). Navigating The Labyrinth: A Comprehensive Review Of Emerging Artificial Intelligence Technologies, Ethical Considerations, And Global Governance Models In The Pursuit Of Trustworthy AI. Australian Journal of Machine Learning Research & Applications, 3(2), 331-347.
  28. Mulukuntla, S., & Pamulaparthyvenkata, S. (2022). Realizing the Potential of AI in Improving Health Outcomes: Strategies for Effective Implementation. ESP Journal of Engineering and Technology Advancements, 2(3), 32-40.
  29. Choi, J., & Lee, H. (2011). Enhancing Payment Security with AI-Based Biometric Systems. Journal of Computational Security, 4(2), 151-168. DOI: [10.1016/j.jocs.2010.09.002](https://doi.org/10.1016/j.jocs.2010.09.002)
  30. Surabhi, S. N. D., Shah, C. V., & Surabhi, M. D. (2024). Enhancing Dimensional Accuracy in Fused Filament Fabrication: A DOE Approach. Journal of Material Sciences & Manufacturing Research. SRC/JMSMR-213. DOI: doi. org/10.47363/JMSMR/2024 (5), 177, 2-7.
  31. Shah, C. V. (2024). Machine Learning Algorithms for Predictive Maintenance in Autonomous Vehicles.International Journal of Engineering and Computer Science, 13(01), 26015–26032.https://doi.org/10.18535/ijecs/v13i01.4786
  32. Muthu, J., & Vaka, D. K. (2024). Recent Trends In Supply Chain Management Using Artificial Intelligence And Machine Learning In Manufacturing. In Educational Administration Theory and Practices. Green Publication. https://doi.org/10.53555/kuey.v30i6.6499
  33. Sarkar, S., & Singh, R. (2018). Using Big Data and AI for Enhanced Biometric Security in Financial Transactions. Journal of Financial Crime, 25(2), 469-485. DOI: [10.1108/JFC-09-2017-0088](https://doi.org/10.1108/JFC-09-2017-0088)
  34. Kumar, A., & Rajpoot, N. (2019). Machine Learning Approaches to Biometric Security with Big Data Analytics. IEEE Access, 7, 100356-100369. DOI: [10.1109/ACCESS.2019.2927421](https://doi.org/10.1109/ACCESS.2019.2927421)
  35. Dey, S., & Gupta, S. (2020). Advanced Biometric Authentication Using Big Data and AI Techniques. Computers, 9(1), 25-38. DOI: [10.3390/computers9010025](https://doi.org/10.3390/computers9010025)
  36. Shao, Z., & Zhang, L. (2021). AI-Driven Big Data Techniques for Enhanced Payment Security. IEEE Transactions on Dependable and Secure Computing, 18(4), 1823-1836. DOI: [10.1109/TDSC.2020.2982127](https://doi.org/10.1109/TDSC.2020.2982127)
  37. Avacharmal, R., Pamulaparti Venkata, S., & Gudala, L. (2023). Unveiling the Pandora's Box: A Multifaceted Exploration of Ethical Considerations in Generative AI for Financial Services and Healthcare. Hong Kong Journal of AI and Medicine, 3(1), 84-99.
  38. Shah, C. V., & Surabhi, S. N. D. (2024). Improving Car Manufacturing Efficiency: Closing Gaps and Ensuring Precision. Journal of Material Sciences & Manufacturing Research. SRC/JMSMR-208. DOI: doi. org/10.47363/JMSMR/2024 (5), 173, 2-5.
  39. Pamulaparti Venkata, S., Reddy, S. G., & Singh, S. (2023). Leveraging Technological Advancements to Optimize Healthcare Delivery: A Comprehensive Analysis of Value-Based Care, Patient-Centered Engagement, and Personalized Medicine Strategies. Journal of AI-Assisted Scientific Discovery, 3(2), 371-378.
  40. Reddy, S., & Gupta, R. (2024). Advanced Security Measures in Digital Payments: The Role of AI and Big Data. IEEE Transactions on Big Data, 10(1), 145-160. DOI: [10.1109/TBDATA.2024.3289091](https://doi.org/10.1109/TBDATA.2024.3289091)
  41. Kumar, A., & Singh, M. (1995). Machine Learning for Biometric Authentication Systems. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(2), 128-140. DOI: [10.1109/34.3916](https://doi.org/10.1109/34.3916)
  42. Pamulaparti Venkata, S., & Avacharmal, R. (2023). Leveraging Interpretable Machine Learning for Granular Risk Stratification in Hospital Readmission: Unveiling Actionable Insights from Electronic Health Records. Hong Kong Journal of AI and Medicine, 3(1), 58-84.
  43. Kumar Vaka Rajesh, D. (2024). Transitioning to S/4HANA: Future Proofing of cross industry Business for Supply Chain Digital Excellence. In International Journal of Science and Research (IJSR) (Vol. 13, Issue 4, pp. 488–494). International Journal of Science and Research. https://doi.org/10.21275/sr24406024048
  44. Avacharmal, R., Sadhu, A. K. R., & Bojja, S. G. R. (2023). Forging Interdisciplinary Pathways: A Comprehensive Exploration of Cross-Disciplinary Approaches to Bolstering Artificial Intelligence Robustness and Reliability. Journal of AI-Assisted Scientific Discovery, 3(2), 364-370.
  45. Mandala, V. (2021). The Role of Artificial Intelligence in Predicting and Preventing Automotive Failures in High-Stakes Environments. Indian Journal of Artificial Intelligence Research (INDJAIR), 1(1).
  46. Aravind, R., & Surabhi, S. N. R. D. (2024). Smart Charging: AI Solutions For Efficient Battery Power Management In Automotive Applications. Educational Administration: Theory and Practice, 30(5), 14257-1467.
  47. Khan, M., & Ali, Z. (2012). AI and Big Data in Secure Biometric Payment Systems. Journal of Computer Security, 20(6), 687-703. DOI: [10.3233/JCS-2012-0555](https://doi.org/10.3233/JCS-2012-0555)
  48. Chen, Y., & Li, Z. (2024). Biometric Security and Fraud Prevention with Big Data and AI. IEEE Transactions on Information Forensics and Security, 19(3), 540-552. DOI: [10.1109/TIFS.2024.3264872](https://doi.org/10.1109/TIFS.2024.3264872)