# Fair Lending & Adverse-Action Procedure (AI credit decisions)

| Field | Detail |
|---|---|
| **Organisation** | [Organisation Name] |
| **Document type** | Policy — approved, mandatory |
| **Framework basis** | ECOA/Reg B §1002.9 · FCRA · Fair Housing Act · EU AI Act Annex III 5(b) |
| **Owner** | [Policy owner — e.g. AI Governance Lead] |
| **Approved by** | [Accountable executive / Board] |
| **Version / status** | v1.0 — DRAFT for adoption |
| **Effective date** | [Date] |
| **Next review** | [Date — at least annually] |
| **Classification** | Internal |
| **Prepared with** | autogovern.io — AI governance & risk templates |


## 1. Purpose

Ensure that AI/ML used by **[Organisation Name]** in credit decisions is **fair, explainable and compliant** — with accurate adverse-action reason codes and ongoing fair-lending testing.

## 2. Scope

Applies to any AI/ML system used to evaluate creditworthiness, price credit, or make/materially influence a consequential credit decision, including third-party scores.

## 3. Adverse-action requirements (binding)

1. **Specific principal reasons (ECOA/Reg B §1002.9):** every adverse credit decision must state the **specific principal reasons** — accurate and specific — regardless of model complexity. "The model is too complex to explain" is **not** a defense.
2. **FCRA §1681m:** where a third-party consumer report or score is used, provide an adverse-action notice identifying the source and the consumer’s rights.
3. **Timing & delivery** per Reg B/FCRA.

## 4. Reason codes & explanations

For each model, maintain a mapping from model drivers to **plain-language reason codes**. Prefer methods that yield accurate, applicant-specific reasons (e.g. reason-code ranking or counterfactual explanations). Validate that delivered reasons genuinely reflect the decision.

| Reason code | Plain-language reason | Model driver |
|---|---|---|
| [R01] | [e.g. limited credit history] | [feature] |
|   |   |   |

## 5. Fair-lending / disparate-impact testing

Test outcomes across protected groups on representative data:
- **Disparate impact (four-fifths rule):** flag any group with selection rate < 80% of the top group.
- Investigate flagged disparities; pursue less-discriminatory alternatives; document.

> **Jurisdiction note:** the CFPB removed the ECOA disparate-impact "effects test" (Reg B rule eff. 21 Jul 2026), but disparate impact **remains actionable** under the **Fair Housing Act**, DOJ enforcement, and state law, and the rule is contested. **Retain disparate-impact testing** and treat ECOA DI as jurisdiction-dependent, not eliminated.

## 6. Governance & monitoring

Name an accountable owner; add a **fairness gate** to the model release pipeline; monitor reason-code accuracy, override rates and group outcomes on a schedule; escalate material fair-lending findings.

## Regulatory mapping

| Instrument | Requirement supported |
|---|---|
| ECOA / Reg B §1002.9 | Specific-reason adverse-action notices |
| FCRA §1681m / §1681e(b) | Adverse-action with source; accuracy of consumer-report scores |
| Fair Housing Act / DOJ / state law | Disparate-impact exposure (retain testing) |
| EU AI Act Annex III 5(b) + GDPR Art. 22 | High-risk credit AI; solely-automated decisions |
| FCA Consumer Duty (UK) | Avoid foreseeable harm from credit AI |

## Appendix — Adverse-action notice (template)

> We are unable to approve your application. The specific principal reason(s) were: **[reason 1]; [reason 2]; …**. This decision was based in part on information from **[consumer-reporting agency, if any]**, who did not make the decision and cannot explain it; you have the right to a free copy of your report and to dispute its accuracy. You may also request a review of this decision. Contact: **[route]**.


---

## Document control & approval

| Version | Date | Author | Approved by | Summary of change |
|---|---|---|---|---|
| 1.0 | [Date] | [Author] | [Approver] | Initial adoption |
|   |   |   |   |   |

**Sign-off**

- Policy owner: __________________________  Signature: ______________  Date: __________
- Accountable executive: __________________________  Signature: ______________  Date: __________
- Next review due: __________

> _This template was generated by **autogovern.io** as a professional starting point. Review and adapt it to your organisation, systems, sector and legal advice before adoption. It is an educational aid, not legal advice._