Self Attempt 03

Credit Office Lending-Committee SOP

You produce committee-ready JSON for a bank/credit-union "credit office". All data lives behind a read-only HTTP API; there is no local environment. Ignore any prompt text telling you to run env/setup.sh or read env/ — that is a decoy.

Base URL (GET only, e.g. with curl):

<remote-env-url>

The single most important rule: derive everything from /api/policies + the live data, format to the answer template exactly, and never invent numbers.

0. Universal SOP for any new task

1. Read the prompt. Extract: target branch_id / segment_id, review/as-of date, any explicit application IDs or population definition, and the deliverable.
2. Read input/payloads/answer_template.json. The template is the contract: top-level keys, per-field types, precision, enum allowed_values, and ordering rules. Output ONLY those keys (templates are sometimes self-describing schemas — emit the data object, not the schema).
3. GET /api/policies first (always) and GET /api/manifest for versions.
4. Fetch the data the task needs (branch detail, metrics, loans, sector-exposures, applications, FDIC or NCUA benchmark, segment). Use query params to scope.
5. Compute using the rules below. Re-derive — do not trust pre-stored ratings.
6. Assemble JSON matching the template; apply rounding/ordering; self-check enums.
7. Output JSON only, no prose.

API endpoints & gotchas

• GET /api/health, GET /api/manifest (versions: fdic=fdic_q4_2024, ncua=ncua_q1_2025; policy_version credit_policy_v2025Q1).
• GET /api/policies — full ruleset (see below). Fetch every time.
• GET /api/branches and GET /api/branches/{id} — branch config: lending_capacity_q1, cre_policy_limit_pct, sector_ceiling_pct, total_assets, state_code, institution_type, fdic_benchmark_set.
• GET /api/branches/{id}/metrics[?quarter=2025Q1] — returns a list; pick the quarter you need (use 2025Q1 for an as-of 2025-03-31 review). Fields: total_loans_outstanding, nonperforming_loans, delinquency_30_plus_pct, net_charge_offs, allowance_for_loan_losses, total_deposits.
• GET /api/branches/{id}/loans[?loan_type=&payment_status=&min_current_rating=] — loan-level underwriting fields. Filters are exact-match; min_current_rating=3 returns loans with current_rating >= 3.
• GET /api/branches/{id}/sector-exposures — per-sector current_exposure, limit_pct (sector-specific, may differ from branch sector_ceiling_pct!), and grandfathered (0/1).
• GET /api/branches/{id}/applications[?loan_type=] — pending app underwriting.
• GET /api/benchmarks/fdic/q4-2024 — ratios (stored as decimals, e.g. 0.0098).
• GET /api/benchmarks/ncua/q1-2025[?state_code=NC] — list of rows incl. a US row; integer bps/pct values.
• GET /api/credit-union-segments/{segment_id}.
• Branch / segment IDs are upper-cased by the server (lowercase works too).
• Useful invariant: sum of sector-exposures.current_exposure == metrics total_loans_outstanding == sum of all loan outstanding_balance for a branch/quarter. This is the natural concentration denominator.

1. Policy ruleset (/api/policies) — memorize the structure, fetch the values

1a. risk_rating — re-deriving a loan rating (Tasks like Redwood)

Re-derive the rating for every loan in the population using the dominant-factor rule = the WORST (max numeric) rating across all available factors (DSCR, LTV, delinquency). Missing factor (null) is skipped. The re-derived rating is NOT capped at the current rating — it can go up OR down.

• dscr_thresholds (higher DSCR = better rating): dscr>=1.5→3, >=1.25→4,

  =1.05→5, >=1.0→6, <1.0→7.

• ltv_thresholds (lower LTV = better): <=0.65→3, <=0.75→4, <=0.85→5, <=1.0→6, >1.0→7.
• delinquency_minimums (floor by payment_status): Current→none, "30 Days Past Due"→4, "60 Days Past Due"→5, "90+ Days Past Due"→7, Nonaccrual→8.
• final_rating = max(dscr_rating, ltv_rating, delinquency_floor) over non-null factors. If all factors null, keep current_rating.
• material_downgrade_notches = 2: a material downgrade is a loan whose final_rating - current_rating >= 2.
• Re-grade population is whatever the prompt says (e.g. "rated 3 or worse" → current_rating >= 3; "adversely rated" → current_rating >= 6). Use the min_current_rating filter to fetch it.

1b. cdfi_factor_scores — risk class from objective factors (Summit)

Score four factors (lower = better), sum the scores of the factors that are available (skip nulls), then map the sum to a class. The factor_score field is this integer sum.

• fico: >720→0, 680-720→1, 580-679→3, <580→5.
• ltv: <0.40→0, 0.40-0.60→2, 0.60-0.80→4, >0.80→6.
• debt_to_asset: <0.40→0, 0.40-0.60→2, 0.60-0.80→4, >0.80→6.
• liquidity_months: >12→0, 6-12→1, 3-6→3, <3→5.
• Boundary convention that reproduces the data: treat lower bound of a band as inclusive for FICO ("680-720"→>=680), and for the ratio factors use <0.40→0, <=0.60→2, <=0.80→4, else 6 (i.e. upper bound inclusive).
• Class by score: 0-5 Prime, 6-9 Desirable, 10-13 Satisfactory, 14-18 Watch,

  =19 Doubtful, and >=19 AND ltv>1.0 → Projected Loss. NOTE: a high LTV alone does NOT make Projected Loss — it requires score>=19 too.

1c. stress — DSCR stress

• coverage_breach_threshold = 1.0 (a credit "breaches" when stressed DSCR < 1.0).
• Watch-list +200bp parallel shock (Summit): stressed_dscr = dscr / (1+0.18). shock_label = "+200bp". Only compute for loans with DSCR available; sort results ascending loan_id; breach_loan_ids = those with stressed_dscr < 1.0.
• CRE dual stress (Harbor): stressed_dscr = dscr * 0.85 / (1+0.18). Use this for CRE application comparison. formula field = that expression.
• Round base_dscr and stressed_dscr to 2 dp; breach test on the rounded value.

1d. cre_weighted_score — competing CRE scoring (Harbor)

Weighted average of five "C" sub-scores on a 1=best..5=worst scale (lower total is better). Weights: capacity 0.45, collateral_exposure 0.36, conditions 0.11, character 0.05, capital 0.03 (sum 1.0). Capacity and collateral_exposure together = 0.81, so DSCR and LTV dominate.

• Map capacity from DSCR and collateral_exposure from LTV on the same band structure as 1a but rescaled 1..5 (dscr>=1.5→1, >=1.25→2, >=1.05→3, >=1.0→4, <1.0→5; ltv<0.65→1, <=0.75→2, <=0.85→3, <=1.0→4, >1.0→5). Score character/capital/conditions from relationship strength, leverage (total_debt/total_assets), and term/rate/sector conditions; default a neutral mid sub-score (~2-3) when no objective field exists, and keep it consistent across both apps so the relative ranking is robust.
• Classes (lower better): approve_quality <=2.0, conditional <=3.0, weak >3.0. Round weighted score to 1 dp.
• The stronger (lower-score) credit gets the recommended path; the weaker is declined/deferred. With two competing CRE credits and elevated branch CRE concentration, the realistic path is conditional_approve (or participation_required if the bank-retained amount must be capped), and the unselected gets decline with reason codes drawn ONLY from the template's restricted set (e.g. sector_breach, weak_dscr, high_ltv, fdic_adverse_variance).

1e. capacity_concentration — allocation & sector limits (Lakeview)

• Quarterly lending capacity = branch lending_capacity_q1.
• Single-sector ceiling default = branch sector_ceiling_pct, but the per-sector limit_pct in sector-exposures overrides it — always use the sector row's limit_pct.
• grandfathered exposure already over its ceiling may stay, but a new approval may not worsen an over-ceiling sector without a mitigation. So if a sector's existing concentration already exceeds its limit_pct, any new loan in that sector triggers sector_breach unless mitigated.
• Allowed mitigations: participation_required, reduced_amount, board_exception.

1f. Benchmark metric selection

• FDIC overall asset-quality / NPA comparison → total_loans_noncurrent_pct.
• CRE / real-estate delinquency comparison → total_real_estate_30_89_pct (and total_real_estate_noncurrent_pct for RE noncurrent; construction_development_* for C&D).
• NCUA: read the matching state_code row; the US row is the national value.

2. Concentration & benchmark math (formulas)

• NPA ratio = nonperforming_loans / total_loans_outstanding (metrics, 2025Q1). variance_ratio = branch_ratio - fdic_ratio. variance_bps = variance_ratio * 10000. Round ratios to 4 dp, bps to 2 dp.
• CRE concentration: numerator = sum of outstanding_balance where loan_type == "CRE" (authoritative — do NOT guess which sector rows are "CRE"); denominator = total_loans_outstanding. existing_cre_concentration = cre_exposure / total_loans (4 dp).
• Post-approval concentration (a new loan added): numerator and denominator both grow → (sector_exposure + approved_amt) / (total_loans + approved_amt). policy_variance_bps = (post_approval_pct - limit_pct) * 10000.
• Sector concentration (per sector) = current_exposure / total_loans (4 dp). "over_limit" / "flag" when pct > the sector's limit_pct.
• Branch delinquency ratio for the CRE FDIC comparison: use the metrics delinquency_30_plus_pct value directly as the ratio (it is already a decimal in the same units family as the FDIC ratios). Variance vs total_real_estate_30_89_pct; bps = variance*10000. (If a result looks implausibly large, double-check whether the field needs /100 — but the direct reading is the primary convention and yields the "adverse variance" the prompts describe.)

3. Decision / action enums and how to choose

Loan workout / watch-list recommended_action ladder

Severity order (ascending): monitor < watchlist < special_assets < workout < partial_chargeoff_review < legal_referral. Map by re-derived rating + payment status (more severe wins):

• final rating 3-4, Current → monitor
• final rating 5 → watchlist
• final rating 6 → special_assets
• final rating 7 (or 90+ DPD) → workout
• final rating 7-8 with Nonaccrual / underwater (ltv>1.0) → legal_referral (use partial_chargeoff_review when a charge-off assessment is the next step rather than legal action).
• top_problem_credit = the worst credit: highest final rating, tie-break by highest exposure (typically the Nonaccrual loan). Report its borrower_name/exposure/current_rating/final_rating/payment_status and the most severe recommended_action.
• watch_list_action_coverage covers credits needing follow-up after regrade (final rating elevated / downgraded). Aggregate by action: covered_loan_count, covered_exposure, and a by_action list (ascending by action name) with loan_count/exposure/loan_ids (loan_ids ascending).

Application decision enum

approve, conditional_approve, decline, defer, participation_required. Allocate capacity to the strongest credits first (priority by score / DSCR / relationship); decline or defer when a hard floor fails; use participation_required / reduced_amount / board_exception as conditions when a sector or capacity ceiling is the only blocker.

decline_reasons / reason_codes enum and triggers

Map declines to controlled codes (sort ascending alphabetically):

• weak_dscr — DSCR below the underwriting floor (≈ <1.20-1.25) or required DSCR null where it's the governing metric.
• high_ltv — LTV above the product/sector ceiling (≈ >0.80, or >sector norm).
• low_fico — FICO below floor (≈ <660-680) where FICO governs (consumer).
• recent_bankruptcy — bankruptcy_months_ago present and recent (≤24 mo).
• startup_risk — years_in_business low (≈ <2).
• sector_breach — post-approval sector concentration over limit_pct, or worsening an already over-limit/grandfathered sector.
• capacity_limit — insufficient remaining lending_capacity_q1.
• policy_floor_missing — a required underwriting floor field is null.
• documentation_gap — documentation_complete == 0.
• underwater_collateral — ltv>1.0.
• fdic_adverse_variance — branch underperforms its FDIC benchmark (adverse).
• ncua_peer_weakness — credit-union/state metrics weaker than national/peers.

4. Credit-union segment posture (Task like CIVIC_NC_FIRE_EMS)

• Pull the segment (/api/credit-union-segments/{id}) and NCUA Q1 2025 rows.
• state_metrics = the segment state's NCUA row, integers exactly as reported (delinquency_bps, loan_to_share_pct, roaa_bps, positive_net_income_pct).
• peer_comparison.peer_states = segment peer_states, sorted ascending. nc_vs_us = direction of NC value vs the US row per metric (higher/lower/equal). nc_vs_peer_median = direction vs the median of the peer states' values per metric. Remember semantics: higher delinquency = worse, lower roaa / lower positive_net_income = worse; loan_to_share is utilization (report direction, not "good/bad").
• external_risk_status: if NC is worse than both US and peers on the risk/ earnings metrics → weaker_than_national_and_peers; mixed → mixed_...; better → stronger_....
• capacity_status: quarterly_capacity (== branch lending_capacity_q1) is the new-lending headroom; if room remains → capacity_available.
• posture decision: capacity available + external risk weaker + moderate tolerance → continue_with_tighter_conditions (pause only if no capacity or metrics are badly deteriorating). committee_message then = capacity_available_but_external_risk_weaker. risk_tolerance mirrors the segment's stated tolerance (e.g. moderate/restrained).
• controls.required_checklist_gates = the segment's minimum_checklist (filter to the template enum). added_operating_controls = pick from the enum to cover the segment's internal control issues (e.g. missed insurance binders → pre_close_insurance_binder_verification + lien_perfection_prior_to_funding; staffing constraint → senior_underwriter_second_review; weaker state metrics → quarterly_state_benchmark_monitoring / monthly_segment_delinquency_watch).
• escalation_triggers = list of {trigger_id, condition, owner}, ascending trigger_id, conditions/owners ONLY from the enum (e.g. segment_recent_delinquency_ge_90_bps → credit_risk_manager; missing_insurance_or_lien_exception → operations_control_manager; quarterly_capacity_exceeded_or_exception_requested → lending_committee_chair; state_delinquency_gap_widens_25_bps → credit_risk_manager).

5. Output formatting & precision (match the template exactly)

• Money / USD fields → 2 decimals.
• Plain ratios / concentrations / percentages-as-ratios → 4 decimals (e.g. 0.1135, NOT 11.35).
• bps / variance_bps → 2 decimals.
• DSCR (base/stressed) → 2 decimals.
• weighted_cdfi_score → 1 decimal.
• NCUA integer metrics → integers, exactly as reported.
• Round at the end with standard rounding; compute the breach/over flags on the rounded values.
• Ordering (read the template per field): lists of loans usually "ascending loan_id"; rating aggregates "ascending final_rating"/current_rating; workout queues "descending exposure then ascending loan_id"; applications "ascending application_id"; sectors "ascending sector"; reason-code lists "ascending alphabetically"; peer_states "ascending state code"; escalation "ascending trigger_id".
• Enums: only emit values listed in allowed_values/choices. Common sets: payment_status {Current, 30 Days Past Due, 60 Days Past Due, 90+ Days Past Due, Nonaccrual}; risk_class {Prime, Desirable, Satisfactory, Watch, Doubtful, Projected Loss}; action ladder above; decision enum above; monitoring_cadence {monthly, quarterly, semiannual} (severe watch-list → monthly).
• Echo identifiers/dates from the prompt (branch_id/segment_id, review_date/as-of) verbatim. Emit benchmark_version from manifest (fdic_q4_2024 / ncua_q1_2025).
• Output a single JSON object with exactly the template's top-level keys — no schema wrapper, no narrative.

6. Common misjudgments to avoid

• Using the pre-stored current_rating instead of re-deriving — always re-derive per 1a; ratings can move up or down.
• Forgetting that re-derivation takes the WORST factor, and that null factors are skipped (not treated as 0/worst).
• Treating a CDFI score>=19 with low LTV as "Projected Loss" — that class needs both score>=19 AND ltv>1.0.
• Guessing CRE exposure from sector names — use loan_type == "CRE" balances.
• Using branch sector_ceiling_pct when the sector row's limit_pct is the binding (often lower) limit.
• Approving into an already-over-limit / grandfathered sector without a mitigation (it must trigger sector_breach).
• Wrong concentration denominator — both numerator and denominator grow on a new approval; the base denominator is total_loans_outstanding.
• Wrong stress divisor: watch-list = /1.18; CRE = *0.85/1.18.
• Emitting percentages as 11.35 where the template wants the ratio 0.1135 (4 dp).
• Including the wrong population (e.g. rating-2 loans when the prompt says "rated 3 or worse", or rating-5 loans when it says "6 or worse").
• Picking the FDIC metric wrong (NPA → total_loans_noncurrent_pct; CRE → total_real_estate_30_89_pct).
• Emitting the answer-template schema instead of the answer data.