Tag
#principles
6 articles
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Building Trustworthy AI for Investing
Trustworthy financial AI rests on four disciplines: grounding every claim in filings, showing provenance, computing numbers deterministically, and knowing what it does not know.
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Building an AI That Understands Financial Statements
Understanding a financial statement is not reading its words. It means normalising the data, respecting the accounting identities, and cross-checking every number against the other statements.
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Structuring Decades of Filings So an AI Can Actually Use Them
A language model cannot reason over a messy pile of filings. Labels drift, statements get restated, formats change, and history is not what it looks like today.
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The Engineering Challenges Behind Institutional AI
Institutional-grade financial AI is hard for five reasons: data quality, point-in-time correctness, citations, deterministic outputs, and coverage at scale. Here is each one.
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Why Citations Are Non-Negotiable in Financial AI
Every number a financial AI reports must link to the source document. An unsourced but plausible figure is worse than no answer, because it invites a costly error.
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Why Deterministic Forecasting Beats LLM Guesses
A forecast used for capital must be reproducible and auditable. A language model's free-form guess is neither, which is why serious forecasts come from an explicit method, not a prompt.
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