Making AI Financial Models Deterministic: A Skill That Generates Editable DCF Excel Workbooks

2026-05-18 · Source: Towards AI - Medium · Field: Finance & Economics — Capital Markets & Investment Management, FinTech & Digital Financial Services · Depth: Intermediate, medium

Summary

The defeatbeta-dcf skill generates fully editable discounted cash flow (DCF) valuation spreadsheets from structured market data, addressing the challenge of trust and auditability in AI-generated financial models. Unlike free-form AI outputs that reside within a chat conversation, this skill produces a standard .xlsx file with live Excel formulas, allowing users to modify assumptions like growth rates, discount rates, and share counts to see immediate valuation updates. The design philosophy emphasizes AI as an orchestrator of a deterministic workflow rather than an improvisational model author, ensuring transparency, reproducibility, and user control over the financial model. The generated workbook includes sections for discount rate estimation, growth estimates, a 10-year projected free cash flow template, and calculations for enterprise value, equity value, and fair price, all driven by real financial statement and market data from defeatbeta-api.

Key takeaway

For financial analysts building valuation models, this approach offers a robust alternative to black-box AI tools. You can leverage AI to automate the initial setup of complex DCF models, ensuring the output is a transparent, formula-driven Excel workbook. This allows you to maintain full control over assumptions, audit calculations, and easily adjust variables to test different scenarios, thereby integrating AI assistance without sacrificing the critical human judgment required in valuation.

Key insights

AI should automate mechanical tasks in financial modeling while preserving human control and model transparency.

Principles

• Financial models must be editable and auditable.
• AI should orchestrate, not improvise, model generation.
• Deterministic workflows enhance AI reliability.

Method

The workflow involves an LLM calling a tool to fetch structured DCF data, saving it as JSON, running a fixed Python script to build an Excel workbook with formulas, and optionally recalculating it before returning the .xlsx file.

In practice

• Generate DCF models with editable assumptions.
• Integrate structured data into AI workflows.
• Use Python for deterministic Excel generation.

Topics

• AI Financial Modeling
• Discounted Cash Flow
• Excel Valuation Workbooks
• Deterministic AI Workflows
• Financial Model Auditability

Code references

• defeat-beta/defeatbeta-api

Best for: AI Engineer, Data Scientist, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards AI - Medium.