A — Policy Identity v1.0

Policy Name

Version

Effective Date

Review Cycle

Product Type

Jurisdiction

Approval Authority (Role)

Author Role

Scope & reliance — 🔒 All inputs are processed locally in your browser. No data is transmitted. Do not enter real personal data — use synthetic or anonymised inputs only. Embedded rates, thresholds, and regulatory citations are static reference values that may age — verify against current primary sources and your own data before relying on any output for commercial, legal, or compliance decisions. Deterministic logic · no inference · zero PII · CC BY 4.0.

Educational Use Only This tool provides a self-assessment / educational framework for internal planning purposes only. It is not a regulatory audit, legal advice, or a substitute for a formal compliance review by a qualified advisor. Verify all interpretations against the official source text and applicable RTS/ITS/guidance published by the relevant authority.

B — Dimension Configuration

Primary Axis (Columns)

Secondary Axis (Rows)

C — Decision Matrix

Build matrix to begin

Configure dimensions above, then click Rebuild Matrix.

D — Applicant Simulator

FICO Score

680

DTI (%)

LTV (%)

Income Band

E — Fairness Analysis (ECOA / Reg B) ECOA flag layer

Build the decision matrix first to run fairness analysis.

F — Champion-Challenger Mode Enable Champion-Challenger

Enable Champion-Challenger to load a Challenger policy table and compare it cell-by-cell against the Champion (Panel C). Required for SR 11-7 model validation and governance committee review.

⚔ Challenger Policy Table

The Challenger uses the same dimensions as the Champion. Assign different outcomes below.

Build Champion matrix first.

Run & Export

Analysis Results

About this tool

Decision table logic Renders a 2D grid of outcome selects. Gap detection and conflict detection are O(n²) over the rule set (≤200 rules). Gaps = unassigned cells. Conflicts = duplicate condition-combinations with different outcomes (not applicable in a pure 2D grid but flagged in sub-rule layer).

Reg B adverse action For any Decline or Refer outcome, generates a specific, applicant-traceable reason list per CFPB Circular 2022-03, ready for direct insertion into an adverse action notice.

Champion-Challenger A second decision table runs in parallel. Every cell divergence is flagged. Net approval rate delta estimated across the matrix. Governance memo exported as SR 11-7-formatted Markdown.

Regulatory coverage ECOA / Reg B 12 CFR 1002 · CFPB Circular 2022-03 · SR 11-7 · EU CCD2 Art. 18 · FCA CONC 5

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  "version": "1.0.0",
  "title": "Credit Policy Decision Table Builder",
  "category": "ai",
  "ap2_export": true,
  "mandate_type": "credit_policy_mandate",
  "mcp_tool_definition": {
    "name": "build_credit_policy_decision_table",
    "description": "Build credit underwriting
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simulate applicants, generate Reg B adverse
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    "inputSchema": {
      "type": "object",
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        "product_type": { "type": "string" },
        "jurisdiction": { "type": "string" },
        "dimensions": { "type": "array" },
        "rules": { "type": "array" }
      }
    }
  },
  "execution": {
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}