Tag
#principles
5 articles
- AI & Finance
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.
- AI & Finance
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.
- AI & Finance
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.
- AI & Finance
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.
- AI & Finance
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.