Genemo, Musa Dima; Kumar, Kotha Anil; Sadat, Quazi Taif; Santosh, Kathari; Sreeram, G.; Raj, I. Infant; Dash, R.; Rautray, R.; Mishra, S.K.

Digital wallets have been provided by FinTech businesses thus reengineering the financial services but nonetheless there is a possibility of keyloggers, bogus personas and assaults of the server with conventional systems using PIN authentication. The current multi-factor authentication configuration usually leaves sensitive biometric information exposed as the logins are processed. To address these security issues, the paper proposes the MHESecAuth that employs Multi-Factor Homomorphic Encryption and Smart Contracts to authenticate. The permission to make transactions is secure since the system operates with PINs, OTPs and no one can view the biometrics. As per experimental research, the system can be accurate at 96.4 percent, false acceptance rate is 0.08 percent, false rejection rate is 0.6 percent and encryption consumes 160ms. The framework optimizes security, privacy and performance against both MFA, encryption-only and combined solutions and is therefore suitable in the development of quantum-secure digital wallets and still blockchain-based audit and transparency.

Bhandarkar, Vishakha D; Genemo, Musa Dima; Muda, Iskandar; Murugan, Rekha; Sreeram, G.; Dharani, N.

Generative AI in a financial context poses risks to the areas of regulatory compliance, accuracy in the content being delivered, and appropriateness in terms of emotion. This research proposes ReFiGen, a framework that can be used to create compliant financial content that is compliant with the regulatory framework and the sentiment of the users. Following domain-specific fine-tuning, ReFiGen blends sentiment alignment with reinforcement learning to optimize on compliance. It demonstrates better performance than baseline models with a high data accuracy rate of 98.35 percent and can generate a fluent output that is emotionally congruent, and regulation-minded. The results of experiments indicate that ReFiGen is both clear and coherent and exactly follows financial guidelines. This practice affirms that the combination of unstructured compliance modeling and affective control is a successful aspect of LLMs. ReFiGen is one of the milestones on the journey of trustworthy and ethically aligned AI in the financial advisory market.