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Finance Semantic System — Starter Guide (v0.1)

artifacts/incoming/finance_semantic_system_starter_guide_v_0.md

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Finance Semantic System — Starter Guide (v0.1)

Status

Introductory guide for practitioners.

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1. Why this exists

Finance already looks structured:

  • ledgers
  • reports
  • reconciliations

But the language inside finance is not machine-reliable.

Example:

EBITDA increased by 12%

Questions a machine cannot safely answer:

  • Which EBITDA?
  • What exclusions?
  • Which policy version?
  • Which entity?

Humans resolve this implicitly. Machines guess.

That gap is why AI adoption in finance lags.

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2. Why machine-readable meaning matters

Most AI systems today operate on:

patterns in language

But finance requires:

binding between language and reality

Without machine-readable meaning:

  • AI hallucinations look plausible
  • auditability breaks
  • numbers lose trust

With machine-readable meaning:

  • AI becomes deterministic
  • outputs are explainable
  • audits become traceable
  • systems interoperate cleanly

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3. The core problem

Finance terms are often:

  • reused
  • overloaded
  • context-dependent

Example:

"Revenue" may differ by:

  • GAAP vs management
  • entity vs consolidated
  • time recognition

This creates a hidden failure mode:

same word, different reality

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4. The solution: explicit semantic grounding

Instead of assuming meaning, we bind meaning explicitly.

Example:

{Revenue@GAAP}

This means:

  • this is not just a word
  • it is a reference to a defined object

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5. The three-layer system

The system has three core components:

FSGF — Grounding Framework

Defines what a term means

FSP — Parser

Detects and structures semantic references

SRE — Resolution Engine

Keeps meaning valid at runtime

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6. Mental model

  • FSGF = dictionary + rules
  • FSP = reader
  • SRE = interpreter + safety system

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7. Notation (how you write it)

Grounded term

{EBITDA}

Means: resolve this term

With context

{Revenue@GAAP}

With version or policy

{EBITDA@Board#V3}

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Ambiguous term

[EBITDA]

Meaning unclear or intentionally unbound

Proposed term

<Adjusted EBITDA>

Draft definition

Narrative term

(core profitability)

Not authoritative

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8. What happens under the hood

Step 1 — Parsing

Input:

{EBITDA@Board}

FSP converts it into:

  • term = EBITDA
  • context = Board
  • status = grounded

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Step 2 — Resolution

SRE:

  • finds canonical definition
  • checks policy
  • checks freshness
  • checks lineage

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Step 3 — Decision

SRE returns:

  • proceed
  • warn
  • pause
  • reject

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Step 4 — Execution / AI use

AI receives:

  • definition
  • exclusions
  • allowed actions

So it does not guess meaning.

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9. Example: without system

EBITDA increased 12%

Problems:

  • ambiguous
  • not auditable
  • not reproducible

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10. Example: with system

{EBITDA@Board#V3} increased 12%

Now:

  • definition is known
  • exclusions are known
  • lineage is traceable
  • AI can reason safely

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11. Real-world usage examples

Board report

{Revenue@GAAP} grew 8% while {EBITDA@Board} declined due to restructuring costs.

Analysis

Compare {FreeCashFlow@Mgmt} to [Free Cash Flow] reported last quarter.

Drafting

<Normalized EBITDA> may exclude one-time litigation costs.

Narrative explanation

(core profitability) improved despite lower revenue growth.

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12. Why this changes AI adoption

Without grounding:

  • AI guesses meaning
  • finance rejects it

With grounding:

  • AI operates on defined objects
  • outputs are auditable
  • trust increases

Result:

AI becomes safer than manual interpretation

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13. Why this feels “expensive”

Because the system must:

  • track context
  • detect drift
  • maintain meaning
  • revalidate continuously

But this cost replaces:

  • audit failures
  • misinterpretation
  • semantic drift

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14. What you actually get

  • consistent terminology
  • cross-system alignment
  • AI-safe workflows
  • audit-ready reasoning
  • reduced ambiguity

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15. Minimal adoption path

Start small:

  1. Define 20–50 key terms
  2. Use {} in one report (e.g. board deck)
  3. Map those terms to definitions
  4. Let AI reference them

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16. Key rules

  • Do not assume meaning
  • Use {} for anything important
  • Use [] when unsure
  • Never upgrade ambiguity silently

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17. Final takeaway

This system does one thing:

It turns financial language into something machines can understand without guessing.

And that is the missing step that allows AI to safely operate in finance.

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Anchor note

This guide is intentionally simple. It introduces the system without requiring full technical depth. It should evolve alongside FSGF, FSP, and SRE as the system matures.