Vinoo Ganesh explains that AI can generate financial work quickly, but the speaker argues that citations and model evaluations alone do not establish whether a figure is correct or whether a firm's rules were followed. The central challenge is making the process behind a result checkable.
The proposed architecture has three parts. Atomic provenance lets a model identify a source figure while deterministic software retrieves and validates it. Scope determinism assigns planning to the model and extraction and arithmetic to code. Derivation chains preserve the inputs and transformations behind calculated figures such as margins and valuations.
In the Q&A, the speaker identifies filings, deterministic calculations and internal documents as inputs to the provenance chain. The speaker says customers want an AI analyst for repetitive reading and first-pass modeling tasks, while portfolio managers retain responsibility for investment decisions.
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