AI Berkshire Financial Rigor Analysis: 6 Validation Methods Explained

AI Berkshire supports six quantitative financial rigor checks—market-cap verification, valuation-metrics validation, cross-source data consistency, Benford’s-law fraud detection, exact-decimal arithmetic, and three-scenario valuation—implemented in tools/financial_rigor.py using exact-decimal arithmetic to eliminate floating-point drift.

The xbtlin/ai-berkshire repository ships a self-contained Financial Rigor Toolkit that automates validation checkpoints during AI-driven investment workflows. This toolkit provides six distinct quantitative checks built on Python's standard library and exact-decimal arithmetic, ensuring reproducible, audit-ready financial calculations without floating-point drift.

Six Types of Financial Rigor Analysis Supported

The tools/financial_rigor.py module serves as the primary source of truth for financial data validation. Each function operates through a shared exact-decimal engine (_CTX) defined at lines 28-33, which guarantees precision across all operations.

1. Market-Cap Verification

The verify_market_cap function (lines 61-90) computes the implied market capitalization from price and shares outstanding, then compares it against reported values. The implementation flags deviations exceeding 5% as a hard warning and deviations exceeding 1% as a caution.

This check catches data entry errors and discrepancies between data vendors before they propagate into valuation models.

python3 tools/financial_rigor.py verify-market-cap \
    --price 510 --shares 9.11e9 --reported 4.65e12 --currency HKD

2. Valuation-Metrics Verification

The verify_valuation function (lines 98-160) calculates critical ratios—including PE, PB, ROE, P/FCF, dividend yield, and PS—from raw financial inputs. Rather than trusting pre-calculated ratios from external sources, this method derives metrics independently using exact-decimal arithmetic.

python3 tools/financial_rigor.py verify-valuation \
    --price 510 --eps 23.5 --bvps 120 --fcf-per-share 18 --dividend 2.4

3. Cross-Source Data Validation

The cross_validate function (lines 167-204) ingests a JSON map of source-to-value mappings and computes a median reference point. It flags any source whose deviation exceeds the default 2% tolerance, returning a consensus value that filters out anomalous data points.

This method is essential when reconciling figures from annual reports, financial data APIs, and analyst estimates.

python3 tools/financial_rigor.py cross-validate \
    --field revenue \
    --values '{"AnnualReport": 7518, "Yahoo": 7500, "Analyst": 7520}' \
    --unit 亿

4. Benford's-Law Fraud Detection

The benford_check function (lines 211-281) applies Benford’s Law to detect abnormal digit distributions that may indicate data fabrication or manipulation. The analysis returns the Mean Absolute Deviation (MAD), chi-square statistic, and a conformity verdict based on expected versus observed first-digit frequencies.

python3 tools/financial_rigor.py benford \
    --values '[1234, 2345, 3456, 4567, 5678, 6789, 7890, ...]'

5. Exact Calculator

The exact_calc function (lines 285-313) provides a safe arithmetic evaluator using Python's Decimal type. It rejects unsafe characters and prints exact results, preventing floating-point drift in critical financial calculations.

python3 tools/financial_rigor.py calc --expr '510 * 9.11e9'

6. Three-Scenario Valuation

The three_scenario_valuation function (lines 317-368) projects earnings per share (EPS) over a configurable time horizon under optimistic, neutral, and pessimistic growth assumptions. It applies scenario-specific PE multiples to generate target prices and percentage change metrics, enabling robust sensitivity analysis.

python3 tools/financial_rigor.py three-scenario \
    --price 510 --eps 23.5 --shares 9.11 \
    --growth 0.15 0.08 0.00 \
    --pe 25 20 15 \
    --years 3 --currency HKD

Integration with AI Workflows

According to the skills/quality-screen.md file, Claude-Code skills automatically invoke the Financial Rigor Toolkit at validation checkpoints. The scripts/sync-codex-skills.py script generates Codex-compatible wrappers that embed these checks into automated screening workflows.

The toolkit is referenced in the project's README.md as a core validation component, ensuring that AI-generated investment analyses meet institutional standards for financial data validation.

Summary

• AI Berkshire implements six quantitative validation checks in tools/financial_rigor.py to ensure financial data integrity.
• All calculations use an exact-decimal engine (_CTX) defined at lines 28-33, eliminating floating-point precision errors.
• Market-cap verification flags discrepancies >5% as warnings and >1% as cautions.
• Cross-source validation computes consensus values with a default 2% tolerance threshold.
• Benford’s-law detection provides statistical fraud detection through MAD and chi-square analysis.
• Three-scenario valuation enables sensitivity testing across optimistic, neutral, and pessimistic projections.

Frequently Asked Questions

What is financial rigor analysis in AI Berkshire?

Financial rigor analysis in AI Berkshire refers to the automated quantitative validation framework implemented in tools/financial_rigor.py. It encompasses six discrete checks—including market-cap verification, valuation metrics calculation, and Benford’s-law fraud detection—that validate financial data before it enters AI-driven investment workflows.

How does AI Berkshire prevent floating-point calculation errors?

The toolkit uses a shared exact-decimal engine (_CTX) initialized at lines 28-33 of tools/financial_rigor.py. This engine leverages Python's decimal.Decimal type for all arithmetic operations, ensuring that calculations like price × shares produce exact, reproducible results without the rounding errors inherent in IEEE 754 floating-point arithmetic.

What tolerance thresholds does the cross-validation function use?

The cross_validate function applies a default tolerance of 2% when comparing values from multiple sources. Any data source deviating more than 2% from the computed median is flagged, while the function returns a consensus value representing the median of all valid inputs.

How does the Benford's law check detect potential data fabrication?

The benford_check function analyzes the first-digit distribution of financial number series and compares it against Benford’s expected logarithmic distribution. It calculates the Mean Absolute Deviation (MAD) and chi-square statistic; significant deviations from the expected distribution trigger a non-conformity verdict, suggesting potential data manipulation or fabrication.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}