# AI Berkshire Financial Rigor Analysis: 6 Validation Methods Explained

> Explore AI Berkshire's financial rigor analysis. Discover 6 validation methods including fraud detection and valuation checks implemented with exact-decimal arithmetic. Learn more.

- Repository: [Xbt Lin/ai-berkshire](https://github.com/xbtlin/ai-berkshire)
- Tags: deep-dive
- Published: 2026-06-28

---

**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`](https://github.com/xbtlin/ai-berkshire/blob/main/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`](https://github.com/xbtlin/ai-berkshire/blob/main/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.

```bash
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.

```bash
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.

```bash
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.

```bash
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.

```bash
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.

```bash
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`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/quality-screen.md) file, Claude-Code skills automatically invoke the Financial Rigor Toolkit at validation checkpoints. The [`scripts/sync-codex-skills.py`](https://github.com/xbtlin/ai-berkshire/blob/main/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`](https://github.com/xbtlin/ai-berkshire/blob/main/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`](https://github.com/xbtlin/ai-berkshire/blob/main/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`](https://github.com/xbtlin/ai-berkshire/blob/main/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`](https://github.com/xbtlin/ai-berkshire/blob/main/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.