ASC 805
M&A Payment Classifier
M&A purchase agreements contain ambiguous payment structures → this workflow systematically applies an internal ASC 805 practice aid across 12 parallel analytical questions → auditors get structured work-paper entries with binary conclusions and supporting citations.
01 — The Problem
Classifying payments in M&A transactions as either purchase consideration or post-combination compensation is one of the most judgment-intensive tasks in acquisition accounting. Without a structured process, practitioners risk inconsistent application of ASC 805-10-55-18 through 55-25 — the same document reviewed by two professionals can yield different classifications depending on which indicators they prioritize. The stakes are high: misclassification affects goodwill, compensation expense, and earnout liability on the acquirer's financial statements.
02 — What the AI Does
Retrieves the target transaction document from a Vellum document index via a deployed subworkflow (get-document-by-id) Extracts contingent payment structures using a primary analysis node (BCPKIS301) scoped to a single gating question: are there payments contingent on future performance, employment, or timing? Routes conditionally — if contingent consideration is detected, the workflow branches into 12 sequential sub-analyses covering: units of account completeness, employment linkage, forfeiture provisions, golden parachute evaluation, settlement form (cash vs. equity), mandatory redemption, variable share obligations, equity indexation, and ASC 815 classification Scores each sub-analysis with a binary TRUE/FALSE sentiment node that applies ASC 805-10-55-18 and 55-25 indicators explicitly Generates structured work-paper entries for each question, formatted for a Big-4-level technical accounting audience Assembles a final consolidated analysis report combining all relevant entries Models used: gpt-4o-mini throughout (all prompt and sentiment nodes); structured JSON outputs enforced via json_schema with strict: true
03 — Design Decisions
Sub-analyses run sequentially with conditional gates — later questions only execute if earlier conditions are met (e.g., employment arrangements must exist before forfeiture provisions are evaluated)
Mirrors the actual decision tree in the firm's internal practice aid; asking "is consideration forfeited on termination?" is only meaningful if employment arrangements were first confirmed to exist. [Creator: add rationale if this also reflects a cost/token optimization decision]
Prevents irrelevant work-paper entries from being generated for inapplicable fact patterns
Every sub-workflow produces two outputs — a human-readable work-paper entry AND a machine-readable conditionMet boolean
The boolean drives routing logic; the text entry feeds the final report. Separating these prevents the routing logic from depending on parsing narrative text. [Creator: add rationale]
Enforced via json_schema with strict: true — the model cannot produce ambiguous outputs that break downstream routing
Each sub-analysis uses a dedicated second LLM call to act as an "independent judge" that renders the TRUE/FALSE verdict, rather than having the analysis node self-classify
Reduces anchoring bias — the analysis node is instructed to reason thoroughly; the judge node is instructed to apply binary standards against ASC 805 indicators specifically. [Creator: add rationale if this was validated against single-node approaches]
The judge prompt explicitly cites ASC 805-10-55-18 and 55-25(a-h) as the evaluation framework, bounding the verdict to codified standards
GETDocumentation passes a fixed document_id and dox_index_id rather than accepting them as workflow inputs
[Creator: add rationale — is this scoped to a single engagement? A demo document? Intentional for controlled testing?]
Limits the workflow to a single document without modification; not currently parameterized for multi-document use
All 40+ prompt nodes use gpt-4o-mini
[Creator: add rationale — cost optimization? Sufficient for structured extraction tasks? Speed for iterative testing?]
May underperform on highly ambiguous contractual language where nuanced legal interpretation is required
Several nodes contain state="DISABLED" blocks with the full ASC 805 framework instructions
[Creator: add rationale — A/B testing? Preserved for reactivation? Intentional scope reduction for specific nodes?]
These blocks do not execute but remain visible in the workflow for editorial review
All report entry nodes explicitly require three sections: Initial analysis goal, Summary of finding, Detailed analysis with citations
Matches the deliverable format expected in professional accounting engagements; output is designed to be inserted directly into work-paper documentation rather than summarized by a human
Outputs are verbose by design (max_tokens: 2000); not suitable for executive summary use cases without a downstream summarization step
05 — Key Insight
When AI is applied to regulated professional judgment tasks, the highest-value design decision is not prompt quality — it is encoding the decision framework (the Practice Aid, the codified indicators, the sequential logic) directly into the workflow architecture so the AI cannot skip steps that a human practitioner is also required to follow.