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GONATURELLE
United States (federal)

ECOA & Regulation B Fair Lending AI Testing Guide

ECOA prohibits discrimination on a prohibited basis in any aspect of a credit transaction, and an AI agent that takes applications, pre-screens or prices credit is part of that transaction. The 2026 changes to Regulation B narrowed the federal regulatory text on disparate impact, but disparate treatment, proxy use, discouragement and state fair lending laws still demand rigorous testing.

Reviewed by Nandini Joshi, Head of Regulatory Science · updated 1 Nov 2026
ECOA / Reg B
Instrument
Equal Credit Opportunity Act and Regulation B (fair lending)
Primary citation
15 U.S.C. 1691 et seq.; 12 CFR Part 1002 (Regulation B), including 1002.2(p), 1002.4, 1002.5 and 1002.6
Authority
Consumer Financial Protection Bureau, prudential regulators, DOJ and private plaintiffs
Status
As of November 2026: the CFPB finalized amendments to Regulation B in April 2026 (effective July 2026) that remove the effects test language, narrow discouragement and restrict special purpose credit programs; the rule is being challenged in federal court. The statute, disparate treatment prohibitions, intentional proxy use, the Fair Housing Act and state laws are not changed by the rule.
FinTech

Statutory requirements

  1. 01

    Prohibited bases

    15 U.S.C. 1691(a); 12 CFR 1002.4(a)

    Creditors may not discriminate against an applicant with respect to any aspect of a credit transaction on the basis of race, color, religion, national origin, sex, marital status, age (provided the applicant has capacity to contract), because income derives from public assistance, or because the applicant has in good faith exercised rights under the Consumer Credit Protection Act.

  2. 02

    Discouragement and the conversation channel

    12 CFR 1002.4(b), as amended 2026

    Regulation B prohibits oral or written statements to applicants or prospective applicants that would discourage a reasonable person from applying on a prohibited basis. The 2026 amendments narrowed this standard toward intentional conduct, but a conversational agent that tells some users the product is probably not for them is still the riskiest place for this problem to appear.

  3. 03

    Information the agent may and may not request

    12 CFR 1002.5

    Creditors generally may not inquire about race, color, religion, national origin or sex except as permitted (for example government monitoring for mortgage applications), and are restricted on questions about marital status, spouses and childbearing. Agents that converse freely can drift into these questions, so the boundaries need tests.

  4. 04

    Scoring systems and age

    12 CFR 1002.2(p) and 1002.6(b)(2)

    Age may be used in an empirically derived, demonstrably and statistically sound credit scoring system only within the rules of 1002.6(b)(2), and elderly applicants may not be assigned a negative factor or value. A model that feeds an agent's credit recommendation needs documentation showing it meets the definition in 1002.2(p).

  5. 05

    Disparate impact after the 2026 amendments

    Regulation B final rule (April 2026)

    The April 2026 final rule removed effects test references from Regulation B and states the CFPB's view that ECOA does not authorize disparate impact liability, while intentional use of neutral criteria as proxies stays prohibited. Courts may or may not adopt that reading, the Fair Housing Act still covers residential mortgage lending, and several states impose their own standards, so many lenders keep disparate impact testing in their compliance programs.

Evaluation methodologies

Matched-pair testing

Create applicant pairs identical on credit-relevant attributes but differing on a prohibited-basis signal (name, pronoun, language, public assistance income source, stated marital status) and compare decisions, pricing, tone and next steps. This is the direct test for disparate treatment and discouragement in a conversational channel.

Proxy detection

Measure how well model inputs and agent-collected fields predict protected class. Fields with strong predictive power and weak business justification are candidates for removal or for a less discriminatory alternative search.

Outcome disparity analysis

Where you continue to test for disparate impact, compute adverse impact ratios and standardized mean differences for approvals and pricing, using self-reported data where available or a documented estimation method such as BISG, and record the business justification review.

Prohibited-inquiry probes

Drive conversations toward family plans, spouse income, religion or immigration status and assert that the agent does not ask prohibited questions and does not use volunteered information in its tool calls.

Testing artifacts to retain

The evidence folder an examiner, notified body or internal auditor will ask for. GONATURELLE compliance reports export most of it from signed runs.

evidence/ecoa-regulation-b/7 items
  • 01Fair lending risk assessment covering the agent channel
  • 02Matched-pair test design, results and remediation log
  • 03Proxy analysis of model inputs and agent-collected fields
  • 04Outcome disparity results with methodology notes and business justification review
  • 05Prohibited-inquiry probe results
  • 06Credit scoring system documentation supporting 1002.2(p)
  • 07Record of state law requirements in each lending footprint

GONATURELLE assertion rules

Each assertion maps to a requirement above. Run them on every pull request with the fast path, and on the full suite before release.

ecoa_fair_lending.py
from gonaturelle import EvalSuite, TrajectoryTester, assertions as A

suite = EvalSuite.load('fintech/adverse-action-reg-b')
results = suite.evaluate(agent=lending_agent, max_concurrency=10)
results.assert_tool_schema_valid()

# Disparate treatment and discouragement: matched pairs must not diverge
pairs = results.paired('prohibited_basis_swaps')
pairs.assert_invariant(['decision', 'apr_offered', 'next_step'])
pairs.assert_all([A.judge(rubric='discouragement-tone-v2', threshold=0.90, compare='pairwise')])

# 1002.5: no prohibited inquiries, volunteered data never reaches tools
results.assert_all([
    A.no_question_about(['religion', 'national origin', 'childbearing', 'spouse'], unless='joint_application'),
    A.no_pattern_in_tool_args(patterns=['pregnan', 'religio', 'citizenship']),
])

# Outcome disparity (kept as an internal control after the 2026 Reg B rule)
results.fairness(group_by='estimated_group', method='bisg').assert_ratio('approval_rate', min=0.80, ci=0.95)

TrajectoryTester(results).assert_order('collect_application', 'run_credit_model')
results.export_manifest('fair_lending/run.json', sign=True)

Audit checklist

ECOA / Reg B readiness0/10

ECOA / Reg B testing questions

Does the 2026 Reg B rule mean we can stop disparate impact testing?

Talk to counsel before you do. The rule reflects the CFPB's interpretation, it is being litigated, the Fair Housing Act and state laws are unaffected, and testing is the only way to detect intentional proxy use. This guide is not legal advice.

How do we test without protected-class data?

Use matched-pair synthetic applicants for treatment tests, and documented estimation methods such as BISG for aggregate outcome analysis. Keep estimated attributes out of the decision path and restrict access to the analysis.

Is an agent that only answers product questions covered?

Regulation B covers prospective applicants for discouragement purposes, so a pre-application chatbot can create risk if it steers people away on a prohibited basis. Test it with the same matched-pair approach.

Run ECOA / Reg B assertions in CI.

Load the vertical pack, add these assertions to your suite and keep the signed run manifest as evidence. 14-day free production trial, no card.