A Framework For Privacy-Preserving Artificial Intelligence In Modern Financial Services

Abstract

The financial services are being changed by Artificial Intelligence, allowing for improved fraud detection, credit scoring, and customer personalization, and introducing serious privacy issues. This privacy-preserving AI (PPAI) in financial services systems presents a stacked architecture that allows financial institutions to utilize AI without compromising the privacy or security of their data. Its framework uses the most modern approaches, such as Federated Learning, Differential Privacy, Homomorphic Encryption, and Secure Multi-Party Computation, to allow cooperation without exposing sensitive financial information. The article describes the powerful implementation model based on the cloud-edge hybrid strategy and containerized technologies, the gradual implementation strategy, the systematic design principles, and the strategic positioning of various stakeholders within the financial ecosystem. Next generation directions include quantum-safe cryptography integration, decentralized AI marketplaces where models can be exchanged, and cross-border privacy systems that negotiate thorny regulatory environments. The framework is a strategic dictum to financial institutions looking to strike a balance between data intelligence and data privacy amidst a highly regulated landscape.