Reflect 3 Attempt 01

Credit-Office Committee SOP

You produce committee-ready JSON for a shared "credit office" environment. All data comes from a remote HTTP API. Each task ships an answer_template.json that defines the EXACT output shape; conform to it precisely (top-level keys, nested keys, enum value sets, list ordering, per-field rounding). The template is authoritative — read it first and last.

1. Remote API — how to fetch data

Base URL: <remote-env-url> (use the API, NOT any local env/ files).

Endpoints (all GET, JSON):

• /api/health, /api/manifest — sanity + record counts + benchmark versions + policy_version.
• /api/policies — THE rulebook (risk_rating, cdfi_factor_scores, cre_weighted_score, stress, capacity_concentration). Always fetch this early.
• /api/branches[?institution_type=bank|credit_union], /api/branches/{branch_id}.
• /api/branches/{branch_id}/metrics[?quarter=2025Q1] — returns a list of quarters; pick the as-of quarter (usually 2025Q1).
• /api/branches/{branch_id}/loans[?loan_type=&payment_status=&min_current_rating=].
• /api/branches/{branch_id}/sector-exposures — existing sector exposure + per-sector limit_pct + grandfathered flag.
• /api/branches/{branch_id}/applications[?loan_type=].
• /api/benchmarks/fdic/q4-2024, /api/benchmarks/ncua/q1-2025[?state_code=].
• /api/credit-union-segments/{segment_id}.

Gotchas:

• branch_id and segment_id must be UPPER-CASED exactly as given in the prompt (e.g. REDWOOD, LAKEVIEW, SUMMIT, HARBOR, CIVIC_NC_FIRE_EMS).
• Branch metrics is a list (multiple quarters). Filter to the review quarter.
• Verified invariant: for a branch, sum(all loan outstanding_balance) == metrics.total_loans_outstanding == sum(sector_exposures.current_exposure). Use this to cross-check and to choose denominators.
• Many loan/application fields are null (dscr, ltv, fico, debt_to_asset, liquidity_months, collateral_value). Null-handling is decisive — see each rule below.
• Benchmark versions: fdic=fdic_q4_2024, ncua=ncua_q1_2025 (from manifest). Use these literal strings when a field wants benchmark_version.

2. Output formatting conventions (apply unless template says otherwise)

• Money / exposures / balances: round to 2 decimals.
• Ratios / concentrations / percentages-as-ratios: round to 4 decimals (e.g. 0.1135, NOT 11.35).
• Basis points (bps): round to 2 decimals; bps = (ratio_a - ratio_b) * 10000.
• DSCR (base and stressed): round to 2 decimals.
• Weighted CDFI score (CRE): 1 decimal. Factor sum scores (watch-list CDFI): integer.
• Ratings, loan_count, factor_score: integer.
• Lists: follow the template's ordering exactly (e.g. "ascending loan_id", "ascending by final_rating", "descending exposure then ascending loan_id", "sort by sector then application_id", "ascending alphabetically" for reason codes, "ascending state code").
• Enum/"set" fields: emit ONLY allowed values; sets are scored per-member (a missing correct item and an extra wrong item both cost). Do not invent values.
• Echo identifiers verbatim (branch_id, segment_id, review/as-of date as YYYY-MM-DD).

3. Risk-rating re-derivation (CONFIRMED, high value)

From policies.risk_rating. Re-derive each loan's rating as the worst (max numeric) of the rating implied by each AVAILABLE factor:

• DSCR thresholds (use >=): >=1.5 -> 3; >=1.25 -> 4; >=1.05 -> 5; >=1.0 -> 6; <1.0 -> 7.
• LTV thresholds (use <=): <=0.65 -> 3; <=0.75 -> 4; <=0.85 -> 5; <=1.0 -> 6; >1.0 -> 7.
• Delinquency minimums (floor by payment_status): Current -> none; 30 DPD -> 4; 60 DPD -> 5; 90+ DPD -> 7; Nonaccrual -> 8.
• final_rating = max(of the factors that are present). If a factor is null, drop it.
• If NO factor is available, keep the loan's current_rating as final.
• Material downgrade: final_rating - current_rating >= policies.risk_rating.material_downgrade_notches (=2). List ascending by loan_id with current/final/downgrade_notches/exposure.
• Regrade population is prompt-defined (e.g. "rated 3 or worse" => current_rating >= 3).
• top_problem_credit = highest final_rating, tie-break highest exposure.
• Recommended-action banding (watch-list/regrade), best estimate: final 5 -> watchlist, 6 -> special_assets, 7 -> workout, 8 (and/or Nonaccrual) -> legal_referral, <=4 -> monitor.

4. Benchmark variance (CONFIRMED, high value)

Pick the benchmark metric matching the task's exposure type:

• Portfolio NPA / noncurrent review -> FDIC total_loans_noncurrent_pct.
• Real-estate / CRE delinquency review -> FDIC total_real_estate_30_89_pct.
• (Also available: construction_development_noncurrent_pct, construction_development_30_89_pct, total_real_estate_noncurrent_pct.)

NPA ratio:

• branch_npa_exposure = metrics.nonperforming_loans; branch_total_loans = metrics.total_loans_outstanding.
• branch_npa_ratio = nonperforming_loans / total_loans_outstanding (4dp).
• variance_ratio = branch_ratio - fdic_benchmark_ratio (4dp); variance_bps = variance_ratio * 10000 (2dp).
• Cross-check: nonperforming_loans usually equals the summed balance of Nonaccrual (+90+) loans.

Real-estate delinquency basis is ambiguous between two readings (document/compute both, prefer the metric-consistent one):

• (a) all 30-89 DPD balance / total_loans_outstanding (this equals metrics.delinquency_30_plus_pct when all delinquencies are RE), or
• (b) RE-loan 30-89 DPD balance / total RE-loan balance (true RE-specific ratio, comparable to the FDIC RE metric).

NCUA benchmarks: rows keyed by state_code (one row per state plus a US row). Use the row for the segment/branch state; values are integers reported verbatim (delinquency_bps, loan_to_share_pct, roaa_bps, positive_net_income_pct).

5. Concentration / capacity (CONFIRMED data mechanics)

From policies.capacity_concentration:

• Per-sector limit = sector_exposures[sector].limit_pct (override table). For a sector with NO row, use the branch default branches.sector_ceiling_pct.
• CRE limit = branches.cre_policy_limit_pct.
• Lending capacity = branches.lending_capacity_q1.
• Concentration denominator = total_loans_outstanding. sector_pct = sector_current_exposure / total_loans_outstanding (4dp). (Verified: sum of sector exposures == total_loans_outstanding.)
• Existing CRE exposure = sum of branch loans with loan_type == 'CRE'; CRE concentration = that / total_loans_outstanding.
• Grandfathering rule: a sector may already be OVER its ceiling (check before any approval). New approvals may NOT worsen an already-over sector WITHOUT a mitigation from allowed_mitigations = {participation_required, reduced_amount, board_exception}.
• Post-approval concentration: adding a new loan grows BOTH numerator (sector) and denominator (total), since loans sum to total. Two defensible forms — be explicit and consistent:
  • (existing_sector + new) / (total_loans + new) [loan added to book], or
  • (existing_sector + new) / total_loans [against current book].
  • The "+new in denominator" form is the more internally consistent default; if a result looks off, try the other.

6. CRE weighted CDFI score (decision tasks) — USE WITH CAUTION

policies.cre_weighted_score: weights capacity 0.45, collateral_exposure 0.36, conditions 0.11, character 0.05, capital 0.03. Lower is better. Classes: approve_quality <=2.0; conditional <=3.0; weak >3.0. The five C's are scored on a 0-6 scale and weighted-summed. The exact C->factor mapping and null handling are NOT fully pinned down; the most plausible mapping is:

• collateral_exposure -> LTV (cdfi ltv table), character -> FICO, capital -> debt_to_asset, conditions -> liquidity_months,
• capacity -> DSCR (no DSCR table in cdfi_factor_scores; derive a 0-6 DSCR score, e.g. >=1.5->0, 1.25-1.5->2, 1.05-1.25->4, <1.05->6).
• Null factors (common for CRE: fico, liquidity): either treat as 0, treat as worst, OR renormalize weights over present factors. THIS CHOICE MATERIALLY CHANGES THE SCORE AND CLASS — pick the one consistent with the template's precision and any prompt hint; flag the assumption.
• Pick the stronger (lower-score) credit as selected; the other is unselected with disposition decline/defer and a restricted reason-code set.

7. Stress formulas (CONFIRMED math)

From policies.stress:

• CRE dual stress (applications): stressed_dscr = dscr * 0.85 / (1 + 0.18).
• Watch-list +200bp stress (existing loans): stressed_dscr = dscr / (1 + 0.18); shock_label "+200bp".
• coverage_breach_threshold = 1.0; a loan/application breaches if stressed_dscr < 1.0.
• Round base and stressed DSCR to 2dp; compute breaches only for records where DSCR is present; emit breach_loan_ids ascending.

8. Watch-list CDFI factor classes (sum-based) — USE WITH CAUTION

policies.cdfi_factor_scores. SUM the factor scores (NOT weighted) over factors:

• debt_to_asset: <0.40 ->0, 0.40-0.60 ->2, 0.60-0.80 ->4, >0.80 ->6.
• ltv: same bands as debt_to_asset.
• fico: >720 ->0, 680-720 ->1, 580-679 ->3, <580 ->5.
• liquidity_months: >12 ->0, 6-12 ->1, 3-6 ->3, <3 ->5. Classes by the sum: Prime 0-5, Desirable 6-9, Satisfactory 10-13, Watch 14-18, Doubtful >=19, Projected Loss (>=19 AND ltv>1.0).
• Null factor handling is UNRESOLVED (skip-missing vs missing-as-worst). Note: skip-missing on these adverse loans rarely reaches Doubtful/Projected Loss, which is implausible for a watch list and conflicts with loan notes that flag underwater/nonaccrual credits as projected-loss; missing-as-worst makes those credits reach Doubtful/Projected Loss. Decide deliberately and state the assumption.
• Adverse population for watch-list tasks is prompt-defined (e.g. "current_rating 6 or worse" => >=6).
• Monitoring cadence for an adverse watch list defaults to monthly.
• severe_bucket_counts: group the population by (current_rating, payment_status); order ascending current_rating then payment_status. Deterministic — get this exactly right.
• workout_queue: order descending exposure, then ascending loan_id; projected_loss = (risk_class == 'Projected Loss').

9. Credit-union segment posture (CONFIRMED, high value)

For /api/credit-union-segments/{segment_id} + NCUA Q1 2025:

• state_metrics = the NCUA row for segment.state_code (integers verbatim); benchmark_version ncua_q1_2025.
• peer_comparison.peer_states = segment.peer_states sorted ascending.
• nc_vs_us = direction of the segment-state metric vs the US row; nc_vs_peer_median = vs the MEDIAN of the peer-state values. Direction: higher if state>other, lower if state<other, equal.
• external_risk_status: weaker_than_national_and_peers when the state has higher delinquency AND lower roaa AND lower positive_net_income vs both US and peer median; mixed/stronger otherwise.
• capacity_status / risk_tolerance / posture / committee_message come from the segment object and its notes:
  • risk_tolerance = segment.risk_tolerance.
  • If notes say capacity remains available but external risk is weaker -> posture continue_with_tighter_conditions, capacity_status capacity_available, committee_message capacity_available_but_external_risk_weaker.
• controls.required_checklist_gates = segment.minimum_checklist verbatim.
• controls.added_operating_controls derive from internal_context (each item is scored separately, so include all justified and exclude unjustified ones):
  • insurance-binder control_issue -> pre_close_insurance_binder_verification + lien_perfection_prior_to_funding.
  • single/constrained senior underwriter -> senior_underwriter_second_review.
  • state delinquency above national -> quarterly_state_benchmark_monitoring.
  • elevated recent_delinquency_bps (near/above ~90) -> monthly_segment_delinquency_watch.
  • include committee_exception_for_capacity_overrun ONLY if capacity is constrained/overrun (omit when capacity available).
• escalation_triggers: map each real concern to a condition + owner (credit_risk_manager / operations_control_manager / lending_committee_chair); order ascending by trigger_id. (Owner/condition exactness for this section was the hardest to confirm — double check each pairing against the internal_context and use the lending_committee_chair for capacity/committee items, operations_control_manager for insurance/lien items, credit_risk_manager for delinquency/benchmark items.)

10. Decline / reason codes & decisions (allocation & CRE tasks)

Reason-code enum (allocation): capacity_limit, sector_breach, weak_dscr, high_ltv, low_fico, recent_bankruptcy, startup_risk, underwater_collateral, policy_floor_missing, documentation_gap, fdic_adverse_variance, ncua_peer_weakness. Map each only when its trigger is met:

• weak_dscr: DSCR below the policy floor (DSCR rating <1.25 is the strongest signal; a minimum_dscr_covenant_1_25 condition exists).
• high_ltv: LTV above ceiling (>0.80 or >0.85 — choose one and apply consistently); underwater_collateral: LTV > 1.0.
• low_fico: FICO below floor (<580 is clearly low; <620 plausible).
• recent_bankruptcy: bankruptcy_months_ago is not null.
• startup_risk: years_in_business < ~2.
• documentation_gap: documentation_complete == 0.
• sector_breach: approving would put the sector over its limit (or worsen an already-over sector).
• fdic_adverse_variance: the branch underperforms the relevant FDIC benchmark.
• Sort reason-code lists alphabetically; map declined application_id -> sorted reason list.

Decisions/allocation: lending_capacity_q1 caps approvals; when total requested exceeds capacity, capacity binds (rank survivors and decline the remainder with capacity_limit). bank_capacity_used for SBA loans excludes the guaranteed portion (amount * (1 - sba_guaranty_pct)). priority_ranking includes only approved + conditionally-approved (highest priority first).

11. SOP for a new task

1. Read the prompt + its answer_template.json. List every required key, enum set, ordering rule, and precision. The template is the contract.
2. GET /api/manifest and /api/policies. Identify which policy blocks apply (risk_rating, cdfi, cre_weighted, stress, concentration).
3. Pull the branch/segment data needed: branch, the correct-quarter metrics, loans, sector-exposures, applications, and the named benchmark.
4. Identify the POPULATION from the prompt's explicit rule (e.g. rating>=3, rating>=6, the two named applications). Apply in/out rules before any math.
5. Compute with the confirmed formulas (sections 3-10). Honor null-handling and denominator choices; cross-check with the sum invariant.
6. Build the JSON to the template: exact keys, enum values, ordering, and rounding.
7. Self-check (below), then emit ONLY the JSON (no prose).

12. Self-check list

• Output has exactly the template's top-level keys and nested required keys; no extras.
• Every enum value is from the allowed set; every "set"/list has no missing or extra members.
• All lists sorted per the template's stated ordering.
• Rounding: money 2dp, ratios 4dp, bps 2dp, DSCR 2dp, weighted CDFI 1dp, factor sums/ratings integer.
• Population in/out rules applied exactly as the prompt states; null factors handled per rule.
• Ratings re-derived as worst-of available factors; material downgrade uses >=2 notches.
• Benchmark metric matches the exposure type; variance_bps = variance_ratio * 10000.
• Concentration denominator = total_loans_outstanding; sector limit from override table else branch default; grandfathered/over-ceiling sectors handled.
• Stress: CRE = dscr*0.85/1.18; watch-list = dscr/1.18; breach if <1.0; only for present DSCR.
• branch_id/segment_id upper-cased; dates YYYY-MM-DD; benchmark_version strings literal.
• For composite scoring-table fields (CRE weighted, CDFI sum, allocation decisions): state and apply ONE consistent factor-mapping + null convention; these are the highest-risk fields — re-derive carefully and verify each keyed item, since errors here cascade across dependent sections.