FinTech platforms are now relying on cloud-hosted machine learning more and more for various purposes like fraud detection, credit scoring, underwriting, and portfolio guidance - however, this kind of infrastructure requires the transfer of sensitive financial data from the user's device which in turn widens the breach surfaces and increases the regulatory exposure (GDPR, PCI-DSS, Reg BI, MiFID II). The forthcoming discussion introduces a totally different concept: carrying out AI inference and compliance checks entirely on the user's device, meaning that risk scoring, anomaly detection, or suitability validation is done locally, and only cryptographic proofs or minimum metadata are sent. The session will discuss practical use cases with federated learning, secure enclaves, differential privacy, and zero-knowledge proofs to create real-time insights without the need to export raw data. Participants will discover that on-device AI not only lowers latency but also reduces the burden of audits and legal liabilities in high-risk financial markets - all this is done with model accuracy and scale maintained.