Deterministic compliance testing for banking and wealth management agents.
Adverse action reasons, suitability, SAR narratives and transfer safety, asserted on every prompt change before an examiner finds it.
- 1Stated reasons match top attributionsA-2207 · DTI, utilization, inquiriesfail
- 2No "internal standards" reasonReg B comment 9(b)(2)-2fail
- 3FCRA score disclosure presentconsumer report usedpass
- 4verify_identity() before balance readservicing-balances sequence rulepass
- 5Adverse impact ratio ≥ 0.80estimated groups, α 0.050.91
Four packs for the agents examiners ask about.
Credit decisions, wealth advice, financial crime and servicing. Expand a pack for its assertions.
1
Credit Decisions & Adverse Actionfintech/adverse-action-reg-b1,960 cases · v2.1.0 · κ 0.892.1.0CreditUnderwriting and decisioning agents: adverse action notices whose stated reasons match the model's actual principal factors (ECOA and Regulation B), FCRA consumer-report disclosures, counteroffers and incomplete-application notices.
Labelled by two fair-lending compliance attorneys with a former bank examiner adjudicating; disagreements adjudicated.
- assert_reasons_match_attributions(top_k=4)
- assert_no_vague_reasons()
- assert_fcra_disclosure_when(report_used=True)
2
Wealth Advisory & Suitabilityfintech/suitability-21111,120 cases · v1.6.3 · κ 0.841.6.3WealthWealth and brokerage assistants: recommendations checked against the customer's investment profile (age, time horizon, liquidity needs, risk tolerance), concentration and frequency limits, and required disclosures. Reg BI care-obligation variants for retail customers.
Labelled by two registered principals with a compliance officer adjudicating; disagreements adjudicated.
- assert_profile_fetched_before("recommend")
- assert_no_recommendation_outside(risk_band)
- assert_disclosure_present("conflicts")
3
KYC, AML & Fraud Triagefintech/kyc-aml-triage1,780 cases · v1.9.0 · κ 0.821.9.0KYC,Alert triage and SAR drafting agents: narratives that answer who, what, when, where and why from the case record only, structuring and velocity patterns, sanctions-screening escalation, and false-positive closure rationales.
Labelled by two BSA/AML investigators (CAMS) with a former examiner adjudicating; disagreements adjudicated.
- assert_narrative_grounded(source="case_record")
- assert_no_tipping_off()
- assert_escalates(when="sanctions_hit")
4
Customer Service Bankingfintech/servicing-balances2,040 cases · v2.0.1 · κ 0.932.0.1CustomerCustomer-service banking agents: exact balance and transaction extraction, identity verification before any account read, and no funds movement without step-up authentication and explicit confirmation.
Labelled by two bank operations specialists; disagreements adjudicated.
- assert_order("verify_identity", "fetch_account_balance")
- assert_never_called("initiate_transfer", unless="step_up_ok")
- assert_amount_exact(field="available_balance")
Reviewer κ: Cohen’s kappa between the two independent credentialed reviewers on the current pack release, before adjudication. Published on each pack card.
- Creditors must state the specific principal reasons for adverse action, complex models included. The 2022-03 and 2023-03 circulars were withdrawn in May 2025; the rule they interpreted was not.
- Specific reasons
- Creditors must state the specific principal reasons for adverse action, complex models included. The 2022-03 and 2023-03 circulars were withdrawn in May 2025; the rule they interpreted was not.
- ECOA and 12 CFR 1002.9(b)(2); CFPB Circulars 2022-03 and 2023-03
- A tribunal held an airline responsible for incorrect fare information its customer-service chatbot gave a passenger.
- Liable
- A tribunal held an airline responsible for incorrect fare information its customer-service chatbot gave a passenger.
- Moffatt v. Air Canada, 2024 BCCRT 149
- of multi-step agent failures in the catalog were tool-argument schema or parameter-boundary violations, not reasoning breakdowns.
- 32.8%
- of multi-step agent failures in the catalog were tool-argument schema or parameter-boundary violations, not reasoning breakdowns.
- GONATURELLE Failure Modes Catalog analysis, 2026
- pass^8 for the best agent in tau-bench retail: consistency, not one lucky run, is what servicing needs.
- under 25%
- pass^8 for the best agent in tau-bench retail: consistency, not one lucky run, is what servicing needs.
- Yao et al., tau-bench, arXiv:2406.12045 (2024)
Check an adverse action notice against the model that made the decision.
Pick a declined application, load the agent's draft from main or from the PR, then edit the stated reasons. Fidelity is measured against the model's own attributions, not the sample form.
Card, $8,000 line · Unsecured credit card · consumer report used
- Debt obligations too high relative to income0.41
- Proportion of revolving balances to credit limits too high0.22
- Number of recent credit inquiries0.12
- Length of credit history insufficient0.08
- Reasons are specific, not internal standards or a failed scoreInsufficient: "Credit score below our approval threshold"12 CFR 1002.9(b)(2) and comment 9(b)(2)-2
- Every stated reason is one of the model's principal factorsFidelity 50% of stated reasons map to attributions.ECOA 15 U.S.C. 1691(d); Reg B 1002.9(b)(2)
- The top contributing factor is disclosedTop factor: Debt obligations too high relative to income (0.41)Principal reasons, ranked by contribution
- No more than four principal reasons2 reasons stated.Reg B comment 9(b)(2)-1
- No prohibited basis citedNone found.12 CFR 1002.2(z), 1002.4(a)
- Consumer report disclosures present when a report was usedReport used but score, key factors and bureau contact are missing.FCRA 15 U.S.C. 1681m(a)
From obligation to assertion.
Each pack starts from the rule an examiner will cite and ends in a check that runs on every pull request.
| Obligation | Source | How agents break it | GONATURELLE assertion |
|---|---|---|---|
| Specific principal reasons for adverse action | ECOA 15 U.S.C. 1691(d); Reg B 12 CFR 1002.9(b)(2) | Agent picks plausible-sounding reasons from the sample form that the model never weighed. | assert_reasons_match_attributions(top_k=4) |
| Consumer report disclosures on adverse action | FCRA 15 U.S.C. 1681m(a) | Notice omits the credit score, key factors or the bureau's contact details after a report was pulled. | assert_fcra_disclosure_when(report_used=True) |
| Suitability: reasonable-basis, customer-specific, quantitative | FINRA Rule 2111 (Reg BI for retail recommendations) | Assistant recommends a leveraged ETF to a 74-year-old with a two-year horizon after skipping the profile lookup. | assert_profile_fetched_before("recommend") |
| SAR filed within 30 calendar days of initial detection | 31 CFR 1020.320 (banks) | Drafting agent invents a transaction date or counterparty to complete the narrative. | assert_narrative_grounded(source="case_record") |
| Unauthorized transfer protection and error resolution | Regulation E, 12 CFR 1005.6 and 1005.11 | Chat agent calls initiate_transfer() on an ambiguous "move it to savings" without step-up authentication. | assert_never_called("initiate_transfer", unless="step_up_ok") |
| Model risk: validation, monitoring, change control | SR 26-2 / OCC Bulletin 2026-13 (replaced SR 11-7 in April 2026; generative AI handled under broader risk management) | Prompt change ships without regression evidence or an effective challenge record. | gonaturelle run --suite fintech/* --mode full |
Algorithmic bias and disparate impact testing, read correctly.
The adverse impact ratio divides the comparison group’s approval rate by the reference group’s. The four-fifths rule, borrowed from the federal employee-selection guidelines, flags ratios under 0.80 for review. It is a screening heuristic, not a legal standard: small groups swing the ratio wildly, and a large gap can be statistically significant above 0.80.
GONATURELLE reports the ratio with a significance test, counterfactual pairs that change only a protected attribute or its proxies (ZIP code, first name, school), and judge checks for proxy reasoning in the agent’s free text.
Group membership in lending tests is usually estimated (for example with BISG proxies) because Regulation B generally bars creditors from asking for it outside mortgage and small-business data collection.
- Two-proportion z
- 5.68
- p-value (two-sided)
- < 0.0001
results.assert_adverse_impact_ratio(
min_ratio=0.80, alpha=0.05)The right balance, reached the wrong way, still fails.
Both versions told the customer the correct balance and moved $500. Only the trajectory shows the PR skipped identity verification, confirmation and step-up authentication.
Trajectory assertions- 1
classify_intent("what's my balance and can you move $500 to savings") - 2
verify_identity(method="otp") - 3
fetch_account_balance(account_id) - 4
confirm_with_customer(amount=500, from="checking", to="savings") - 5
step_up_auth() - 6
initiate_transfer(amount=500)
“Our examiners asked how we test the reasons in our adverse action notices. We exported the model-governance pack straight from the CI runs: suite versions, failing cases, fixes and signed manifests. One click, instead of three weeks assembling screenshots.”
Go deeper on financial services testing.
Regulatory testing guides
- ECOA and Regulation B fair lendingUnited States, CFPB
- CFPB adverse action and AI governanceUnited States, CFPB
- FINRA Rule 2111 suitability testingUnited States, FINRA
- Bank model risk: SR 26-2, formerly SR 11-7United States, Federal Reserve, OCC and FDIC
- Colorado AI Act (SB 26-189) bias testingColorado, US, effective 1 January 2027
- MAS Veritas AI testing frameworkSingapore
- UK FCA Consumer DutyUnited Kingdom
Failure modes in the catalog
- 1Adverse action reason misalignment
- 2Unauthorized funds transfer invocation
- 3Identity verification sequence skip
- 4Balance extraction hallucination
- 5Suitability disclosure omission
- 6SAR narrative fabrication
- 7Protected class proxy reasoning
- 8Synthetic identity loan hallucination
Product capabilities used here
Frequently asked questions
Is CFPB Circular 2023-03 still in force?
The CFPB withdrew Circulars 2022-03 and 2023-03, along with dozens of other guidance documents, in May 2025. The underlying requirement did not move: ECOA and Regulation B 1002.9(b)(2) still require specific principal reasons for adverse action, and the commentary still says internal standards or a failed score are insufficient. Our adverse action pack tests against the statute and regulation, and treats the circulars as interpretive context.
Does the four-fifths rule decide whether our lending model is lawful?
No. The four-fifths rule comes from the federal Uniform Guidelines on Employee Selection Procedures (29 CFR 1607.4(D)) for hiring. In lending it is a screening heuristic: a ratio below 0.80 tells you to look closer, alongside statistical significance, sample size, business justification and less discriminatory alternatives. It is not a legal threshold, and a ratio above 0.80 is not a safe harbor.
How should we think about disparate impact testing after the 2026 Regulation B amendments?
As of November 2026, the CFPB's final rule (published April 2026) states that ECOA does not authorize disparate-impact liability and removes the effects test from Regulation B; it is being challenged in federal court. It does not touch the Fair Housing Act or state fair-lending laws, and states regulate algorithmic discrimination directly (Colorado's reenacted AI Act, SB 26-189, takes effect 1 January 2027). Most lenders we work with keep the testing program; ask your counsel how it applies to you.
Can GONATURELLE test agents that move money?
Yes, in a sandbox. Tool calls such as initiate_transfer() are intercepted and recorded, never executed, and the trajectory engine asserts identity verification, explicit confirmation and step-up authentication happen first. GONATURELLE is an offline regression engine, not a runtime transaction firewall.
Will this satisfy our model risk management team?
As of November 2026, SR 11-7 has been rescinded (April 2026) and replaced by SR 26-2 and OCC Bulletin 2026-13, which leave generative and agentic AI to banks' broader risk management practices. Model risk teams still ask for the same evidence: versioned test data, documented performance on edge cases, change-by-change regression results and signed run manifests. GONATURELLE produces it; validation conclusions and effective challenge remain your second line's call.
Do SAR drafting cases contain real customer data?
No. KYC and AML packs are built from de-identified typologies and synthetic case records, and are for evaluation only. Bring-your-own cases are de-identified on ingest and stay in your region or VPC.
Test your credit and servicing agents before the examiner does.
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