Examining the Role of Generative AI in Preventing Deepfake Financial Fraud: Challenges and Opportunities in the FinTech Ecosystem

Author

Ms. Aparna Saxena, Dr. D. Praveenadevi


Abstract

 Generative Artificial Intelligence has made deepfake financial fraud more sophisticated, yet it also gives institutions new ways to detect and prevent it. This study examines the role of Generative AI capabilities in preventing deepfake financial fraud in the FinTech ecosystem, with AI-based fraud detection and prevention as the mediating variable and Data Privacy and Security as the moderating variable in the conceptual framework. The study follows a quantitative, cross-sectional design. A structured questionnaire with five-point Likert items produced 130 responses (N = 130), which were analysed in Microsoft Excel using Cronbach’s alpha, chi-square tests, one-way ANOVA, linear regression and moderated regression. All four constructs were reliable (Cronbach’s α = .850–.902), and respondents’ perceptions were generally favourable, with construct means between 3.67 and 3.99 on a five-point scale. Generative AI capabilities had a significant positive relationship with AI-based fraud detection and prevention (β = .838, R² = .702, p < .001). AI-based fraud detection and prevention was significantly and positively related to deepfake financial fraud prevention (β = .858, R² = .737, p < .001), and Generative AI capabilities were also directly related to deepfake financial fraud prevention (β = .852, R² = .726, p < .001). Hypotheses H1 to H3 were therefore supported. Data Privacy and Security did not significantly moderate these relationships (interaction p = .588–.857), so H4 was not supported, although it was positively associated with perceived deepfake financial fraud prevention. Perceptions differed most by age group (χ²(2) = 13.64, p = .001). Since the study measures perceptions and the sample is dominated by young respondents, the results show associations, not causal effects or actual fraud outcomes.



Keywords

Generative AI; Deepfake; Financial Fraud Prevention; AI-Based Fraud Detection; FinTech; Data Privacy and Security



Full Text:

Download Paper PDF


References


  1. Shan, W. (2025). AI-powered fraud detection in banking: Innovations, challenges and preventive strategies [Master's thesis, Saint Mary's University].
  2. Rath, M. K. (2026). Adversarial resilient and explainable AI framework for deepfake-aware fraud detection in financial systems. International Journal of Computer Information Systems and Industrial Management Applications, 18(8s), 1072–1085. https://doi.org/10.70917/ijcisim-2026-3363
  3. Rahat, Y. O., Islam, M. K., & Rabbani, S. F. (2026). Machine learning for identifying deepfake-driven identity abuse, authentication evasion, and customer impersonation in U.S. banking. Frontiers in Computer Science and Artificial Intelligence, 5(4), 54–67. https://doi.org/10.32996/fcsai.2026.4.3.7x
  4. Amaresam, R. K. (2024). Deepfake detection and AI's role in preventing digital fraud. International Journal of Research and Applied Innovations, 7(4), 11096–11107. https://doi.org/10.15662/IJRAI.2024.0704011
  5. Marwick, A. E., Schiffrin, A., & Williams, K. (2026). Governing the scam ecosystem: Policy responses to generative AI-enabled deepfake financial fraud [Preprint]. SSRN. https://ssrn.com/abstract=6866865
  6. Kurshan, E., Mehta, D., & Balch, T. (2024). AI versus AI in financial crimes & detection: GenAI crime waves to co-evolutionary AI. In Proceedings of the 5th ACM International Conference on AI in Finance (pp. 745–751). Association for Computing Machinery. https://doi.org/10.1145/3677052.3698655
  7. Delva Benavides, J. E., Leos Navarro, J. A., & García Hernández, A. P. (2026). Deepfakes in onboarding, KYC, and financial fraud: Authenticity standards and liability framework for digital banks and FinTech. Journal of FinTech and Sustainable Finance, 2, 1–9. https://doi.org/10.31875/2755-8398.2026.02.01
  8. Zeng, Q., Zhao, Z., Yang, Y., Zhu, Y., Liu, F., Bi, Z., Wynn, M. T. K., Choo, K.-K. R., & Chen, H. (2026). When AI meets Wall Street: A survey on trustworthy AI in fintech (arXiv:2605.30650). arXiv. https://arxiv.org/abs/2605.30650
  9. Adegoke, K. R., & Adegoke, T. B. (2026). AI-enabled fraudulent schemes and their effects on consumer trust and digital financial adoption in Nigeria. SSR Journal of Multidisciplinary, 3(1), 20–36. https://doi.org/10.5281/zenodo.18228379
  10. Kumar, S. N. P. (2025). Fraud detection in banking using generative AI. Sarcouncil Journal of Engineering and Computer Sciences, 4(11), 133–145. https://doi.org/10.5281/zenodo.17634095
  11. Dixit, S. (2024). Advanced generative AI models for fraud detection and prevention in FinTech: Leveraging deep learning and adversarial networks for real-time anomaly detection in financial transactions [Preprint]. TechRxiv. https://doi.org/10.36227/techrxiv.172978266.60563345/v1
  12. Shan, K., Wang, J., Wang, Q., Chen, G., & Mansoor, A. (2026). Leveraging generative AI for financial fraud detection in the platform economy: A GAN-based data augmentation method. Humanities and Social Sciences Communications. Advance online publication. https://doi.org/10.1057/s41599-026-07552-x
  13. Senevirathna, L., & Sumanathilaka, D. (2025). Efficient financial fraud detection on mobile devices using lightweight large language models. In Proceedings of Recent Advances in Natural Language Processing (pp. 1090–1098). https://doi.org/10.26615/978-954-452-098-4-126
  14. Hacini, A. D., Benabdelouahad, M., Abassi, I., Houhou, S., Boulmerka, A., & Farhi, N. (2025). LLM-assisted financial fraud detection with reinforcement learning. Algorithms, 18(12), Article 792. https://doi.org/10.3390/a18120792
  15. Rawat, R., Dibouliya, A., Chaturvedi, N., Rawat, A., Rawat, H., & Dangi, C. S. (2026). A data analytics based federated biometrics for deepfake fraud detection in financial multimedia systems. Discover Computing, 29, Article 215. https://doi.org/10.1007/s10791-026-10073-5
  16. Jayakannan, S. M. (2025). Securing voice-based financial authentication in the era of AI voice cloning: Challenges, vulnerabilities, and counter-measures. Journal of Computer Science and Technology Studies, 7(4), 515–520. https://doi.org/10.32996/jcsts.2025.7.4.60
  17. Gokul, P., Janarthan, R., Makeshwaran, M. A., Karuppasamy, K., Thenmalar, R., & Jayaraman, S. (2026). Deepfake voice based scam detection in banking calls using AI and ML. International Journal of Scientific Research and Engineering Development, 9(2), 2292–2298. https://www.ijsred.com/volume9/issue2/IJSRED-V9I2P337.pdf
  18. Igba, E., Olarinoye, H. S., Nwakaego, V. E., Sehemba, D. B., Oluhaiyero, Y. S., & Okika, N. (2025). Synthetic data generation using generative AI to combat identity fraud and enhance global financial cybersecurity frameworks. International Journal of Scientific Research and Modern Technology, 4(2), 1–19. https://doi.org/10.5281/zenodo.14928919

 

 

Share your valuable work from Social Media Buttons