# Usage: curl -sSL https://seed.show/lending.credit.underwriting | bash -s <install-path>
# <install-path> is the directory where the file should land.

set -euo pipefail
[ -z "${1:-}" ] && {
  echo "install requires a path: curl -sSL https://seed.show/lending.credit.underwriting | bash -s <install-path>" >&2
  exit 1
}
TARGET="$1"
mkdir -p "$TARGET"
DEST="$TARGET/seed-fold.B0AazC.folded.md"

cat > "$DEST" <<'PORTDOWN_A2DC93C1'
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---
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# Credit Underwriting Context

## What this context is — and what agents must never do

This is a reference for agents working in credit-adjacent contexts: drafting disclosures, analyzing underwriting policy, explaining loan products, or supporting lenders and borrowers.

**Agents must never make credit decisions or imply credit approval.** That includes statements like "you'd likely qualify," "your credit score suggests approval," or "this looks like a strong application." Credit decisions require verified documentation, regulatory compliance infrastructure, and human accountability that no agent possesses. An agent that produces the functional equivalent of a credit decision — even framed as analysis — creates liability for the deploying institution and potential harm to the borrower.

Specific prohibitions:
- Do not state or imply that a borrower will be approved or denied
- Do not calculate whether someone "qualifies" for a specific loan product
- Do not apply lender-specific thresholds (credit score cutoffs, DTI limits, LTV requirements) as if they are universal — they are product- and lender-specific and change; consult current lender guidelines and `sources.md`
- Do not advise on how to structure an application to game underwriting criteria
- Do not reproduce or apply specific regulatory numerical thresholds without citing their current source — these change

---

## What underwriting is actually doing

Underwriting has one job: **quantify the probability and severity of a borrower's default, then decide whether the expected return is sufficient to compensate for that risk — or decline.**

Every technique in credit analysis is in service of two estimates:
- **Probability of Default (PD):** how likely is this borrower to fail to make required payments?
- **Loss Given Default (LGD):** if they do default, what fraction of the outstanding balance will the lender lose after recovery?

From those two estimates (plus Exposure at Default, or EAD — the balance at time of default) comes **Expected Loss (EL = PD × LGD × EAD)**, the foundational credit risk number. Pricing, reserves, and decline decisions flow from expected loss. Agents that treat underwriting as "checking credit score + verifying income" are missing the underlying logic.

---

## The Five Cs of Credit

The Five Cs are the traditional organizing framework for underwriting analysis. Each C corresponds to a different kind of evidence about the borrower's future behavior.

**Capacity** — Can the borrower repay from cash flow?

The primary question in most underwriting. For consumer loans: income vs. debt obligations, measured by **Debt-to-Income ratio (DTI)**. For commercial loans: the business's cash flow available to service debt, measured by **Debt Service Coverage Ratio (DSCR)**. Capacity analysis is forward-looking: it asks whether projected cash flows are sufficient, not just whether the borrower has assets. DTI and DSCR thresholds are product- and lender-specific; consult current lender guidelines and GSE selling guides (see `sources.md`).

**Capital** — How much does the borrower have at stake?

The borrower's own equity or net worth — skin in the game. For mortgages: down payment percentage, measured by **Loan-to-Value (LTV)**. For commercial: the owner's equity in the business. High capital reduces both PD (the borrower has more to lose) and LGD (more cushion before the lender takes losses). A borrower with low capital has a strong incentive to walk away when things go wrong. LTV requirements, PMI thresholds, and minimum down payment percentages vary by loan type, product, and lender — do not treat any specific number as universal.

**Collateral** — What can the lender seize if the borrower defaults?

The secondary source of repayment. Collateral reduces LGD but does not substitute for capacity analysis — collateral-based lending ("asset-based lending") is its own distinct discipline with different underwriting standards. For real estate: appraised value vs. loan amount (LTV). For commercial: accounts receivable, inventory, equipment, real property. Collateral quality includes marketability, lien priority (senior vs. junior), and volatility of value.

**Conditions** — What is the economic and loan-specific context?

Macro environment (interest rate cycle, recession risk, industry stress), purpose of the loan, loan structure (term, amortization, covenants), and market conditions. Conditions analysis is the underwriter asking: "Does this loan make sense given what's happening in the world and the specific circumstances of this borrower?" A loan that looks fine in a benign environment may be untenable under stress.

**Character** — Will the borrower prioritize repayment?

Historical behavior: credit bureau tradelines, derogatory marks (late payments, collections, charge-offs, bankruptcies), length of credit history, and — for commercial borrowers — management track record and reputation. Character evidence is backward-looking: it tells you how this borrower has behaved, not how they will behave. FICO and VantageScore are primarily character scores — they encode payment history, utilization, age of accounts, and inquiry frequency.

---

## Regulatory constraints — where agents fail

Credit underwriting in the US is subject to overlapping federal statutes and regulations. Agents working in this domain regularly miss or misapply them.

### ECOA (Equal Credit Opportunity Act) and Regulation B

ECOA prohibits discrimination in any aspect of a credit transaction on the basis of **race, color, religion, national origin, sex, marital status, age, receipt of public assistance, or exercise of rights under the Consumer Credit Protection Act**.

These are called **prohibited bases**. The critical operational implication: an underwriting model cannot use a prohibited basis directly — but it also cannot use a variable that is a proxy for a prohibited basis, even if the variable is facially neutral. This is **disparate impact** liability.

A model trained on zip code, last name, or shopping category data may encode racial or ethnic disparities without any intent. The model is facially neutral; the outcome is not. Under ECOA, the outcome is what matters for disparate impact. Agents that say "the model doesn't use race, so it's compliant" are giving incomplete and potentially dangerous advice.

ECOA also requires **adverse action notices**: when credit is denied, reduced, or offered on materially worse terms, the lender must provide the specific reasons. "Model said no" is not a compliant adverse action reason. The reasons must be the principal factors, in plain language, within 30 days (consumer credit) or 30 days after notification (commercial under some thresholds).

Regulation B (12 CFR Part 1002) implements ECOA and is enforced by the CFPB and the federal banking agencies.

### FCRA (Fair Credit Reporting Act)

FCRA governs the use of **consumer reports** — data from credit bureaus (Equifax, Experian, TransUnion), employment verification services, tenant screening services, and other "consumer reporting agencies" (CRAs). Key obligations:

- **Permissible purpose**: a lender may only pull a credit report for credit underwriting (and a few other permitted purposes). Using bureau data to market to declined applicants is not permissible.
- **Adverse action notices**: when a credit decision is based in whole or in part on information in a consumer report, the applicant must receive a specific FCRA adverse action notice, including the CRA's name, address, and toll-free number, and notice that the CRA didn't make the credit decision.
- **Accuracy and dispute**: CRAs and furnishers (lenders reporting data to bureaus) must maintain reasonable accuracy procedures and investigate disputes.
- **FCRA Section 604**: a lender must have a permissible purpose before obtaining a report — "soft" pulls for pre-approval marketing are governed separately from "hard" pulls for firm credit decisions.

### Fair Lending — Disparate Impact vs. Disparate Treatment

These are two distinct legal theories. Agents frequently conflate them.

**Disparate treatment**: intentional discrimination — treating an applicant differently because of a prohibited basis. An underwriter who applies stricter income requirements to minority applicants is disparate treatment. Intent is the element.

**Disparate impact**: a facially neutral policy, practice, or model produces a statistically significant adverse effect on a protected class — even if no one intended it. The Supreme Court confirmed disparate impact applies to the FHA in *Texas Dept. of Housing v. Inclusive Communities Project* (2015). The CFPB and DOJ apply disparate impact analysis to underwriting models under ECOA and the FHA.

For automated underwriting: even if the algorithm was built with no reference to race, if it produces meaningfully different approval rates or pricing by race, the lender has potential fair lending exposure. The lender bears the burden of justifying the business necessity of each input, and demonstrating that no less discriminatory alternative achieves the same risk management purpose.

### CRA (Community Reinvestment Act)

CRA requires federally insured banks to meet the credit needs of the communities they serve, including low- and moderate-income (LMI) communities. Relevant to underwriting because CRA assessments evaluate where loans are made geographically, not just whether individual underwriting decisions are sound. A bank that redlines LMI census tracts via its underwriting criteria may have CRA exposure in addition to fair lending exposure.

### AUS (Automated Underwriting Systems) and the GSE framework

Fannie Mae (Desktop Underwriter, DU) and Freddie Mac (Loan Product Advisor, LPA) operate AUS platforms that determine whether a conventional mortgage loan meets GSE guidelines for purchase. When an AUS returns "Approve/Eligible" (Fannie) or "Accept" (Freddie), the lender can originate within the GSE framework. When it returns "Refer," a human underwriter must manually evaluate.

AUS decisions are not the same as final loan commitments — they are conditional on the accuracy of the data input and verification of the documentation. Agents that treat an AUS approval as a loan commitment are wrong.

---

## What agents get wrong

**Credit score is not the underwriting decision.** FICO and VantageScore are inputs to capacity and character analysis. The underwriting decision integrates all five Cs, verifies the information in the application, and applies product-specific overlays. A 750 FICO score does not override a 90% LTV on a declining-value property, or a DSCR below 1.0 on a commercial loan.

**ECOA prohibited basis proxies.** An agent that recommends or implements a model using zip code, neighborhood characteristics, shopping history, or social network data without a disparate impact analysis is flagging potential FCRA and ECOA violations the agent may not recognize. This is the most common and consequential failure mode for AI in credit decisioning.

**Pre-approval is not a commitment.** A pre-approval letter tells a borrower they likely qualify for a loan amount based on unverified information. A commitment letter is issued after the lender verifies income, employment, assets, appraisal, and title. In mortgage: the commitment is conditioned on clear title and satisfactory appraisal; even the commitment is conditional until closing. Agents often describe pre-approvals as loan approvals.

**Consumer underwriting ≠ commercial underwriting.** Consumer underwriting is largely rule-based: DTI thresholds, LTV limits, minimum FICO scores. Commercial underwriting is more judgment-intensive: DSCR analysis, global cash flow (the borrower's total picture including personal finances and all business obligations), covenant structure, collateral appraisal, and industry-specific factors. Applying consumer underwriting intuitions to commercial loans produces systematically wrong analysis.

**Adverse action requirements apply to AI models.** A lender cannot satisfy adverse action notice obligations by saying "the model declined you." The lender must identify and disclose the specific factors that principally drove the decision, in understandable terms. This is a real operational constraint on black-box credit models and is an active area of CFPB examination.

**Origination and servicing are different functions.** Origination is the process of evaluating, approving, and funding a loan. Servicing is the ongoing management of that loan — collecting payments, handling escrow, processing modifications and forbearances, managing defaults. The regulatory frameworks, data systems, and failure modes differ. An agent advising on a servicing question with origination-context is operating outside its relevant domain.

**AI-specific underwriting failure modes:**

- *Hallucinating lender thresholds.* Agents confidently state specific credit score cutoffs, DTI limits, or LTV requirements that reflect training data from a specific lender, product, or vintage — not current guidelines. These numbers drift; always attribute thresholds to a current, named source.

- *Treating AUS output as a decision.* An AUS "Approve/Eligible" is a conditional recommendation, not a commitment. Agents frequently represent AUS output as a loan approval. It is not.

- *Missing the global cash flow step.* Commercial underwriting for small-business borrowers requires analyzing the owner's personal and business finances together. An agent that evaluates only the business's financials or only the personal DTI is applying the wrong framework.

- *Proxy discrimination blindness.* An agent tasked with building or reviewing a credit model may not recognize that features like zip code, frequency of payday loan usage, or social graph density can proxy for protected classes. The test is disparate impact on outcome, not absence of protected-class inputs.

- *Treating adverse action as optional for AI models.* Some agents have claimed that AI or ML models satisfy adverse action requirements through generic model explanations. CFPB Circular 2022-03 explicitly rejects this: the lender must identify specific principal factors driving each individual decision, in plain language understandable to the applicant.

- *Conflating credit score versions.* FICO has multiple model versions (FICO 8, FICO 9, FICO 10, industry-specific variants). GSE guidelines specify which versions are acceptable for conforming mortgage origination. An agent that treats all FICO scores as interchangeable is ignoring a real operational constraint.

---

## What AI is changing in underwriting

AI is materially reshaping credit underwriting across three areas. The changes are real; the regulatory constraints around them are also real.

### Alternative data and ML credit models

Traditional underwriting relies on bureau tradelines (character), tax returns and pay stubs (capacity), and appraisals (collateral). AI-powered models can incorporate: rent payment history, utility and telecom payments, bank transaction patterns, cash flow volatility, and in some frameworks, employment verification through payroll data providers.

The CFPB has acknowledged that alternative data can expand credit access for thin-file borrowers (those with limited bureau history) — notably immigrants, young adults, and historically underserved communities. The same data can also encode proxy discrimination. The regulatory position is that using alternative data does not exempt a lender from ECOA and disparate impact analysis; the expanded feature set makes disparate impact testing *more* important, not less.

ML credit models — gradient boosting, neural networks, ensemble methods — can capture nonlinear relationships and interaction effects that logistic regression-based scorecards miss. They typically outperform traditional scorecards on hold-out samples. Their main underwriting liability is explainability: black-box models cannot easily produce the specific, individualized reason codes that adverse action notices require.

### CFPB AI adverse action guidance — Circular 2022-03

CFPB Circular 2022-03 (September 2022) is the controlling federal guidance on AI models and adverse action. Its operative conclusion: ECOA and Regulation B's adverse action notice requirements apply fully to AI and ML credit models. Lenders cannot satisfy the requirement by citing "the algorithm" or "a complex model" as the reason for denial. The lender must identify the principal factors that drove each specific decision, stated in plain language understandable to that applicant.

This has real operational consequences for black-box models. A model that cannot generate individualized reason codes at inference time — the same codes the adverse action notice requires — creates a compliance gap. Lenders using such models must either: (a) build explainability infrastructure (SHAP values, LIME, other post-hoc methods) that can generate compliant reason codes; or (b) accept that deploying the model creates regulatory exposure. CFPB examination teams can and do test whether adverse action notices from AI models match the model's actual decision factors.

Full text: https://www.consumerfinance.gov/compliance/circulars/circular-2022-03-adverse-action-notification-requirements-and-the-use-of-artificial-intelligence-and-machine-learning/

### Fair lending examination of AI models

AI underwriting models are subject to the same disparate impact analysis that applies to traditional underwriting criteria. The examination framework:

1. Regulators obtain the model's inputs and output distribution across protected classes (using HMDA data for mortgage, or matched-pair testing for consumer credit)
2. If a statistically significant adverse impact on a protected class is found, the burden shifts to the lender to demonstrate **business necessity** — that the model serves a legitimate credit risk purpose and is the least discriminatory alternative that achieves it
3. If a less discriminatory alternative exists that predicts credit risk equally well, the lender's use of the more discriminatory model may constitute a fair lending violation regardless of intent

The "less discriminatory alternative" standard is operationally demanding. It requires lenders to test alternative model specifications and demonstrate they cannot achieve equivalent predictive performance without the disparity. This is an active area of CFPB and DOJ enforcement focus as of 2022–2025.

### What stays human

Despite AI's expanding role, certain functions remain human-controlled by regulatory design or operational necessity:

- **Credit policy:** The rules governing who the institution will and will not lend to — minimum scores, excluded industries, geographic constraints — are credit policy, set by humans with board or senior management accountability. AI models operate within credit policy; they do not set it.
- **Exception approval:** When a borrower does not meet standard guidelines but a human underwriter judges the loan creditworthy (a "policy exception"), that judgment requires human sign-off and must be documented. Exception rates are tracked for fair lending patterns — if exceptions are granted disproportionately along demographic lines, that is disparate treatment.
- **Regulatory examination response:** When a bank regulator examines the institution's underwriting, the bank's management must explain and defend the credit policy, model choices, and exception patterns. An AI cannot respond to examination findings or take regulatory accountability.
- **Model validation:** SR 11-7 (the Federal Reserve's model risk management guidance, adopted across banking agencies) requires independent validation of models used in credit decisions — human model risk management teams who can challenge assumptions, test limitations, and document findings.
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# Credit Underwriting Glossary

Precise definitions for terms that are frequently misused or conflated in AI-generated credit analysis.

---

**DTI (Debt-to-Income Ratio)**
Total monthly debt obligations divided by gross monthly income, expressed as a percentage. The primary capacity metric in consumer underwriting. Lenders apply front-end DTI (housing payment only / income) and back-end DTI (all debt obligations / income) — the back-end ratio is the binding constraint in most guidelines. Conventional conforming loans typically cap back-end DTI at 45–50%, subject to compensating factors. DTI is a snapshot; it does not capture employment stability, income volatility, or seasonal variation.

**LTV (Loan-to-Value Ratio)**
Loan amount divided by the appraised or purchase value of the collateral (whichever is lower), expressed as a percentage. The primary collateral metric in mortgage and real estate lending. High LTV reduces the equity cushion and increases loss given default if the borrower defaults and collateral values fall. Loans above 80% LTV conventionally require private mortgage insurance (PMI) on conforming residential mortgages.

**DSCR (Debt Service Coverage Ratio)**
Net operating income (NOI) divided by annual debt service (principal + interest). The primary capacity metric in commercial real estate and income-property lending. DSCR of 1.0 means NOI exactly covers debt service — no cushion. Lenders typically require DSCR of 1.20 or higher; stronger covenants may require 1.25 or 1.35. DSCR below 1.0 means the property's cash flow cannot cover the loan payments without the borrower's outside resources.

**PD (Probability of Default)**
The likelihood that a borrower will fail to make required payments within a defined time horizon (typically one year). One of the three components of Expected Loss. PD is a forward-looking estimate derived from historical default rates for comparable borrower profiles, loan types, and economic conditions. It is distinct from a credit score — a credit score is one input into estimating PD, not PD itself.

**LGD (Loss Given Default)**
The fraction of the outstanding loan balance the lender expects to lose if the borrower defaults, after recovery — collateral liquidation, guarantor payments, workout proceeds. LGD is expressed as a percentage of EAD. An unsecured personal loan might have LGD of 80–90%; a well-collateralized mortgage with a senior lien might have LGD of 20–30%. LGD depends heavily on collateral quality, lien priority, and recovery costs.

**EAD (Exposure at Default)**
The outstanding loan balance at the time a borrower defaults. For term loans, EAD is approximated by the scheduled outstanding balance; for revolving facilities (credit cards, lines of credit), EAD is harder to estimate because borrowers often draw down available credit before defaulting. Expected Loss = PD × LGD × EAD.

**Expected Loss (EL)**
The statistical expectation of credit loss for a loan or portfolio: PD × LGD × EAD. Expected loss is the actuarial foundation for loan pricing (the credit spread must at minimum cover EL), reserve-setting (CECL accounting requires estimating lifetime expected credit losses), and portfolio management. Expected loss is not the same as the worst-case loss — that is addressed by economic capital and stress testing.

**Covenant (Affirmative vs. Negative)**
A contractual obligation in a commercial loan agreement. Affirmative covenants require the borrower to take specific actions (maintain insurance, provide audited financial statements annually, maintain minimum DSCR). Negative covenants prohibit specific actions (incur additional debt above a threshold, sell material assets, pay dividends without lender consent). Covenant violations are typically events of default that give the lender rights to accelerate the loan. Consumer loans do not use covenants — this is a commercial lending concept.

**Amortization vs. Balloon**
An amortizing loan makes equal payments that cover both interest and principal over the loan term, fully paying off the balance by maturity. A balloon loan makes payments that do not fully amortize — a large lump sum (the "balloon") is due at maturity. Balloon structures are common in commercial real estate: a 30-year amortization schedule with a 10-year maturity means the borrower makes payments as if the loan were 30 years, but must pay the remaining balance (or refinance) at year 10. Balloon risk is the refinancing risk at maturity.

**Origination vs. Servicing**
Origination is the process of evaluating a credit application, underwriting the loan, and funding it. Servicing is the ongoing administration of a funded loan — collecting payments, managing escrow, processing modifications, reporting to credit bureaus, and handling defaults. Origination is a one-time event; servicing is a recurring operational function with its own regulatory framework (RESPA servicing rules, CFPB Mortgage Servicing Rules). The same lender often does both, but they are operationally and legally distinct.

**AUS (Automated Underwriting System)**
A technology platform that evaluates a loan application against predefined eligibility criteria and returns a credit risk recommendation. Fannie Mae's Desktop Underwriter (DU) and Freddie Mac's Loan Product Advisor (LPA) are the dominant AUS platforms for conforming residential mortgages. AUS output is a recommendation (Approve/Eligible, Refer/Eligible, Ineligible) — not a commitment to lend. The recommendation is conditional on the accuracy of the data submitted and verification of supporting documentation.

**HMDA (Home Mortgage Disclosure Act)**
A federal law requiring covered mortgage lenders to collect and report loan-level data on applications, originations, and denials, including the applicant's race, ethnicity, sex, income, and the loan's geographic location. HMDA data is publicly available and is the primary data source for fair lending disparate impact analyses. Regulators compare approval and denial rates across demographic groups using HMDA data to identify potential discrimination patterns.

**ECOA Protected Classes**
The prohibited basis categories under the Equal Credit Opportunity Act: race, color, religion, national origin, sex, marital status, age (provided the applicant has legal capacity to contract), and receipt of public assistance income. Lenders may not use these categories — or proxies for them — in any aspect of a credit transaction. The Fair Housing Act adds additional protected classes for dwelling-related credit: disability and familial status.

**Disparate Impact vs. Disparate Treatment**
Two distinct fair lending legal theories. Disparate treatment is intentional discrimination — treating an applicant differently because of a protected characteristic. Disparate impact is neutral on its face but produces a statistically significant adverse outcome for a protected class, even without intent. Both violate ECOA and the Fair Housing Act. For AI underwriting models, disparate impact is the primary exposure: a model with no explicit protected-class input can still encode proxy discrimination through correlated features.

**Credit Bureau Tradeline**
A record of a credit account as reported by a lender (furnisher) to a consumer reporting agency (CRA) — Equifax, Experian, or TransUnion. Each tradeline reflects account type, credit limit or original loan amount, current balance, payment history (including delinquencies), and account status (open, closed, charged-off). Tradelines are the primary data source for FICO and VantageScore models. The accuracy and completeness of tradeline data is regulated by FCRA.

**VantageScore vs. FICO**
Two competing credit scoring models using different methodologies and scoring ranges (both now use 300–850). FICO scores, developed by Fair Isaac Corporation, are the dominant scores used in mortgage underwriting under GSE guidelines and most bank lending. VantageScore is a competing model developed jointly by the three major bureaus. The models weight factors differently and can produce materially different scores for the same consumer. GSE guidelines (Fannie/Freddie) specify which FICO model versions are acceptable for conforming mortgage loans — lenders cannot substitute VantageScore for GSE-eligible lending.

**Prime vs. Non-Prime vs. Subprime**
Informal market tiers for consumer credit quality. Prime borrowers have strong credit profiles — high scores (typically 700+), low DTI, established credit history. Non-prime (sometimes called "near-prime") borrowers have weaker profiles but are not deeply distressed. Subprime borrowers have significant derogatory history, very low scores, or recent defaults. These categories are market convention, not regulatory definitions. Regulatory focus on subprime is driven by fair lending concern: historically, subprime products were disproportionately marketed to minority borrowers — "reverse redlining."

**Adverse Action Notice**
A required disclosure under ECOA and FCRA when a lender denies credit, reduces a credit limit, changes terms adversely, or takes other adverse action based on creditworthiness. The notice must state the specific principal reasons for the action (up to four), and if a consumer report was used, must identify the reporting agency and inform the consumer of their right to a free copy. For AI models, this requirement is operationally binding: the model must produce explainable reason codes. CFPB Circular 2022-03 explicitly states that black-box AI models do not exempt lenders from adverse action notice obligations.

**Pre-Approval vs. Commitment**
A pre-approval is a conditional indication of creditworthiness based on unverified information — income stated but not documented, credit pulled but other factors not fully evaluated. It is not a promise to lend. A commitment letter is issued after the lender has verified income, employment, assets, appraisal, and title, and has made a binding decision to fund subject to specified conditions (commonly: no material change in employment or credit before closing, satisfactory title, appraisal at or above contract price). Treating a pre-approval as a credit commitment is a common and consequential agent error.

**Global Cash Flow Analysis**
A commercial underwriting technique that evaluates the borrower's total debt service capacity across all obligations — personal and business combined. Used when a small business owner's personal finances and business finances are intertwined. Global cash flow prevents a borrower from appearing creditworthy in isolation when their total obligations (business loans, personal mortgage, other debt) exceed their combined income. Consumer underwriting uses DTI; commercial underwriting uses global cash flow where ownership and personal exposure overlap.

**Charge-Off**
A lender's accounting entry removing a delinquent loan balance from its books as a loss — typically triggered after 120–180 days of non-payment depending on loan type. A charge-off does not extinguish the debt; the borrower still legally owes it. Charged-off debts may be sold to debt collectors or retained for recovery. Charge-offs appear on the borrower's credit report and are severely negative. Distinct from write-off (complete removal with no recovery expectation) and settlement (lender accepts less than full balance to close the account).

**CECL (Current Expected Credit Loss)**
The accounting standard (ASC 326) adopted by US banks since 2020 that requires lenders to estimate and reserve for the lifetime expected credit losses on loans at the time of origination — replacing the previous "incurred loss" model that required evidence of impairment before recognizing a reserve. CECL increased reserve requirements for banks with long-duration loan portfolios (e.g., auto and student loans) and required more sophisticated PD × LGD × EAD modeling for reserve calculations. Relevant context for any discussion of bank underwriting and credit risk management.

**Stress Testing**
An analytical technique that projects portfolio or borrower performance under adverse scenarios — rising interest rates, recession, collateral value decline, industry-specific downturns. Applied at two levels: (1) **loan-level stress testing** during underwriting, where the underwriter asks whether the borrower can service the debt if interest rates rise 200 basis points or revenue falls 20%; (2) **portfolio-level stress testing**, required by regulators for larger banks, assessing capital adequacy under macroeconomic stress scenarios (DFAST/CCAR for the largest institutions). Stress testing is the operationalization of "Conditions" in the Five Cs. Agents working in credit risk should not conflate underwriting-level stress analysis with regulatory capital stress testing — they use different methodologies, governance requirements, and outputs.

**Concentration Risk**
The risk that a lender's portfolio is disproportionately exposed to a single borrower, industry, geography, or collateral type — so that a single adverse event affects a large portion of the book simultaneously. Regulators and bank examiners scrutinize concentration risk because it can threaten institutional solvency. Common concentrations: commercial real estate (CRE) as a percentage of capital, exposure to a single corporate borrower, agricultural lending in a single county. Credit policy typically includes concentration limits. When an agent discusses a lender's willingness to make a specific type of loan, concentration limits at that institution may be a binding constraint that is invisible in the application-level underwriting criteria.

**Troubled Debt Restructuring (TDR)**
A loan modification in which a lender grants a concession to a borrower experiencing financial difficulty that it would not otherwise consider — reduced interest rate, extended maturity, forgiven principal, or other modification below market terms. Under legacy GAAP (ASC 310-40), TDRs triggered specific accounting treatment (impairment measurement, disclosure requirements) and were tracked as a credit quality indicator. FASB eliminated TDR accounting in 2022 as part of the CECL update (ASU 2022-02), replacing it with a broader loan modification disclosure framework. TDR terminology still appears in pre-2023 regulatory guidance, examination findings, and loan documents — agents should recognize the term but note the accounting change. Operationally, loan modifications to distressed borrowers remain a servicing function with regulatory scrutiny under consumer protection and loss mitigation rules (e.g., CFPB mortgage servicing rules).
<!--fold:7e65df@file path="sources.md" mode="644"-->
# Credit Underwriting Sources

## Federal regulators — primary authority

**CFPB Fair Lending Supervision**
- Fair lending examination procedures: https://www.consumerfinance.gov/compliance/supervision-examinations/fair-lending/
- CFPB ECOA and Regulation B resources: https://www.consumerfinance.gov/compliance/compliance-resources/mortgage-resources/fair-lending/
- CFPB guidance on adverse action and ECOA: https://www.consumerfinance.gov/rules-policy/regulations/1002/
- Full text of Regulation B (12 CFR Part 1002): https://www.ecfr.gov/current/title-12/chapter-X/part-1002

**CFPB Fair Credit Reporting Act Resources**
- FCRA examination procedures: https://www.consumerfinance.gov/compliance/supervision-examinations/fcra/
- Full text of Regulation V (12 CFR Part 1022, FCRA implementing reg): https://www.ecfr.gov/current/title-12/chapter-X/part-1022

**FFIEC (Federal Financial Institutions Examination Council)**
- Interagency Fair Lending Examination Procedures: https://www.ffiec.gov/press/pdf/fairlend.pdf
- FFIEC examination manuals and guidance: https://www.ffiec.gov/exammanuals.htm
- HMDA data and resources (Home Mortgage Disclosure Act): https://www.ffiec.gov/hmda/

**OCC (Office of the Comptroller of the Currency)**
- Comptroller's Handbook — Commercial Credit: https://www.occ.gov/publications-and-resources/publications/comptrollers-handbook/index-comptrollers-handbook.html
- OCC Credit Risk guidance: https://www.occ.gov/topics/supervision-and-examination/credit/index-credit.html
- OCC Fair Lending guidance: https://www.occ.gov/topics/consumers-and-communities/fair-lending/index-fair-lending.html

**FDIC**
- Commercial Lending guidance and examination procedures: https://www.fdic.gov/regulations/examinations/supervisory/insights/
- FDIC Consumer Compliance Examination Manual (fair lending chapter): https://www.fdic.gov/regulations/compliance/manual/

**Federal Reserve**
- Consumer Compliance Supervision — Fair Lending: https://www.federalreserve.gov/supervisionreg/caletters/fair-lending.htm
- SR Letters on credit risk and lending guidance: https://www.federalreserve.gov/apps/srletters/

## GSE selling guides — mortgage underwriting standards

**Fannie Mae**
- Fannie Mae Single-Family Selling Guide: https://selling-guide.fanniemae.com/
- Desktop Underwriter (DU) documentation: https://singlefamily.fanniemae.com/applications-technology/desktop-underwriter
- Origination through closing chapter: https://selling-guide.fanniemae.com/sel-guide-sections/origination-thru-closing

**Freddie Mac**
- Freddie Mac Single-Family Seller/Servicer Guide: https://guide.freddiemac.com/app/guide/
- Loan Product Advisor (LPA) documentation: https://sf.freddiemac.com/tools-learning/loan-product-advisor
- Credit and underwriting requirements: https://guide.freddiemac.com/app/guide/section/5100.1

## Statute text

**Equal Credit Opportunity Act (ECOA)**
- 15 U.S.C. § 1691 et seq.: https://uscode.house.gov/view.xhtml?path=/prelim@title15/chapter41/subchapter4&edition=prelim

**Fair Credit Reporting Act (FCRA)**
- 15 U.S.C. § 1681 et seq.: https://uscode.house.gov/view.xhtml?path=/prelim@title15/chapter41/subchapter3&edition=prelim

**Fair Housing Act (FHA) — disparate impact basis**
- 42 U.S.C. § 3601 et seq.: https://uscode.house.gov/view.xhtml?path=/prelim@title42/chapter45&edition=prelim

**Community Reinvestment Act (CRA)**
- 12 U.S.C. § 2901 et seq.: https://uscode.house.gov/view.xhtml?path=/prelim@title12/chapter30&edition=prelim

## AI and algorithmic underwriting — specific guidance

**CFPB Circular 2022-03 — Adverse Action and AI Models**
- The CFPB's controlling guidance that ECOA adverse action requirements apply fully to AI-based credit models, including black-box models. Lenders cannot satisfy adverse action notice requirements by citing "complex algorithms" — they must identify the specific principal factors driving each individual decision. This is the primary regulatory document for AI underwriting compliance.
- URL: https://www.consumerfinance.gov/compliance/circulars/circular-2022-03-adverse-action-notification-requirements-and-the-use-of-artificial-intelligence-and-machine-learning/

**Interagency Statement on Model Risk Management (SR 11-7)**
- Federal Reserve SR Letter 11-7, "Guidance on Model Risk Management" — the foundational document for bank model governance. Establishes requirements for model validation, independent review, and documentation. Applies to all underwriting models. OCC Bulletin 2011-12 is the parallel guidance for national banks.
- https://www.federalreserve.gov/supervisionreg/srletters/sr1107.htm

**CFPB Supervisory Highlights — AI and Algorithmic Models**
- CFPB examination findings on algorithmic credit models, adverse action notice deficiencies, and fair lending: https://www.consumerfinance.gov/compliance/supervisory-highlights/

**CFPB Request for Information — AI in credit (2023)**
- CFPB's inquiry into AI use in consumer finance, including underwriting and alternative data: https://www.consumerfinance.gov/about-us/newsroom/cfpb-issues-request-for-information-on-the-use-of-artificial-intelligence/

**DFAST and CCAR — Regulatory Stress Testing**
- Dodd-Frank Act Stress Testing (DFAST) and Comprehensive Capital Analysis and Review (CCAR) — the Federal Reserve's annual stress testing frameworks for large banks. Different from loan-level underwriting stress analysis; relevant for portfolio-level credit risk management.
- Federal Reserve stress testing overview: https://www.federalreserve.gov/supervisionreg/stress-tests-capital-planning.htm

## How to use these sources

1. **For fair lending compliance on a specific underwriting model:** Start with **Regulation B (12 CFR Part 1002)** and **CFPB Circular 2022-03** on adverse action notices for AI models. Add FFIEC Fair Lending Examination Procedures for the supervisory framework.
2. **For mortgage underwriting standards:** **Fannie Mae Selling Guide** and **Freddie Mac Single-Family Guide** define credit and underwriting eligibility for conforming loans — these are the authoritative current sources for GSE thresholds. Do not cite training data as a source for specific DTI, LTV, or FICO cutoffs.
3. **For commercial credit standards:** **OCC Comptroller's Handbook — Commercial Credit** and **FDIC Examination Manual** provide the supervisory frameworks examiners apply.
4. **For HMDA analysis (home mortgage data):** **FFIEC HMDA resources** — the data is public and the primary basis for fair lending disparate impact analyses.
5. **For model governance and AI risk management:** **SR 11-7** is the baseline; OCC Bulletin 2011-12 for national banks. CFPB Circular 2022-03 for adverse action. CFPB Supervisory Highlights for recent examination findings.
6. **For alternative data and expanded underwriting:** No single controlling regulation; the CFPB's 2023 RFI and fair lending examination procedures are the active frameworks. Disparate impact analysis is required regardless of data source.
<!--fold:7e65df@end-->
PORTDOWN_A2DC93C1

# ── post ──
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awk -v m="$MARKER" -v outdir="$TARGET" '
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SEED_EXTRACTED=$(find "$TARGET" -type f -not -path "$DEST" 2>/dev/null | wc -l)
if [ "$SEED_EXTRACTED" = "0" ]; then
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rm -f "$DEST"

echo "" >&2
echo "✓ seed unpacked → $TARGET ($SEED_EXTRACTED files)" >&2
find "$TARGET" -type f | sort | while IFS= read -r _sf; do
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done
echo "" >&2
if [ -f "$TARGET/SKILL.md" ]; then
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echo "  https://seed.show/skill" >&2
echo "" >&2
echo "Publisher prompt:" >&2
sed 's/^/  /' >&2 <<'__SEED_PROMPT_END_AC1F2B__'
You have the credit underwriting context. Read README.md for the underwriting mental model, regulatory constraints, AI-specific failure modes, and what AI is changing in underwriting. Read glossary.md for precise terminology. Fetch sources.md for regulatory guidance and current authoritative sources — do not cite training data for specific thresholds. Never make credit decisions or imply credit approval. To share your own context bundles: curl seed.show/skill | bash
__SEED_PROMPT_END_AC1F2B__
exit 0