Abstract
Systems for personal financial management may centralize data on remote servers, which can expose sensitive information, such as user credentials and personal messages, to potential security vulnerabilities. Described systems and techniques provide for a financial management architecture that can operate locally on a user's client device (e.g., a smartphone, smart watch, wearable device, or augmented reality glasses). The architecture may utilize on-device listeners to capture unstructured financial alerts, and a lightweight, on-device large language model may parse these alerts to extract structured transaction data. A predictive analytics engine can then forecast cash flow to calculate a suggested liquidity value for autonomous liquidity management. By performing sensitive data processing and storage locally, this on-device approach can reduce the transmission of personally identifiable information to external systems, which can support user privacy.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Subramanian, Thirumagal Dhivya, "Secure, On-Device Financial Aggregation and Autonomous Liquidity Management", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/12390