Privacy When AI Meets Your Ledger
Cloud-only assistants see copies of your transactions. This note walks through on-device and hybrid designs that keep embeddings and inference local where possible.
The default cloud story
Many “AI finance” demos send prompts and context to remote models. Even with encryption in transit, you are still trusting retention policies, subprocessors, and future product pivots. For everyday consumers that may feel abstract until a breach or a policy change makes it concrete.
Local-first intelligence
On-device models can run classification, OCR post-processing, and conversational turns without exposing raw transaction strings to the network. Hybrid setups might sync anonymized aggregates for backup while keeping interpretive steps on hardware — the exact split depends on platform capabilities and user settings.
Finsharc’s architecture assumes the ledger is yours first. Features like receipt capture, voice-tagged logging, and assistant replies are framed so privacy is a product constraint, not a footnote.
What you should still watch for
No design removes the need for good habits: strong device passcodes, cautious OS permissions, and avoiding pasting full card numbers into chat. Software can narrow the risk surface; it cannot eliminate social engineering or lost phones.
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