reflect-3-attempt-03

name	reflect-3_attempt_03
description	Validated credit-risk / lending-committee SOP for the credit office environment (rating re-derivation, CDFI scoring, capacity/concentration, DSCR stress, CRE decisions, segment posture).

Credit Office Committee SOP

You produce a single JSON object that conforms exactly to the task's input/payloads/answer_template.json. All data comes from the remote public API (NOT local files). This skill encodes the business rules, API usage, and output formatting confirmed for this environment.

0. Golden rules (read first)

• Conform to the answer_template: top-level keys, nested required keys, enum value sets, list ordering, and per-field precision. A wrong shape or a value outside an enum set is scored as wrong.
• Use the public API only. Branch and segment ids go in the URL path in UPPERCASE (e.g. REDWOOD, LAKEVIEW, SUMMIT, HARBOR, CIVIC_NC_FIRE_EMS).
• Always start: GET /api/manifest then GET /api/policies. policies carries every scoring table you need (risk_rating, cdfi_factor_scores, cre_weighted_score, stress, capacity_concentration). Read it before computing.
• Precision: money 2dp; ratios 4dp; basis points (bps) 2dp; DSCR 2dp; weighted_cdfi_score 1dp; counts / ratings / factor scores are integers.
• bps conversion: variance_bps = (branch_ratio - benchmark_ratio) * 10000, rounded to 2dp. ratio differences are rounded to 4dp.

1. Public API endpoints

Base: <remote-env-url>

• GET /api/health, GET /api/manifest, GET /api/policies
• GET /api/branches[?institution_type=bank|credit_union]
• GET /api/branches/{BRANCH_ID} — branch facts: lending_capacity_q1, sector_ceiling_pct, cre_policy_limit_pct, total_assets, fdic_benchmark_set, state_code, institution_type.
• GET /api/branches/{BRANCH_ID}/metrics[?quarter=YYYYQn] — returns a list (latest first). Use the latest quarter row. Fields: total_loans_outstanding, nonperforming_loans, delinquency_30_plus_pct, etc.
• GET /api/branches/{BRANCH_ID}/loans[?loan_type=&payment_status=&min_current_rating=] — min_current_rating=N returns loans with current_rating >= N (use for "rated 3 or worse" = min_current_rating=3; "6 or worse" = min_current_rating=6).
• GET /api/branches/{BRANCH_ID}/sector-exposures — per-sector current_exposure, limit_pct (override), grandfathered flag.
• GET /api/branches/{BRANCH_ID}/applications[?loan_type=]
• GET /api/benchmarks/fdic/q4-2024
• GET /api/benchmarks/ncua/q1-2025[?state_code=XX] — rows list; query the target state, the US row, and each peer state.
• GET /api/credit-union-segments/{SEGMENT_ID}

Gotchas:

• The sum of sector-exposures.current_exposure equals the latest metrics.total_loans_outstanding. Use this as the branch loan-portfolio denominator for any concentration ratio.
• Branch metrics.nonperforming_loans equals the sum of balances of loans in Nonaccrual payment_status.
• Some loans/applications have null factors (dscr/ltv/fico/collateral). Handle nulls explicitly per the rules below; never crash, never silently drop a loan from population counts.

2. Risk-rating re-derivation (from policies.risk_rating)

For each loan compute a rating from EACH available factor, then take the WORST (maximum numeric) rating across available factors. Higher number = worse.

• DSCR band: >=1.5 -> 3; >=1.25 -> 4; >=1.05 -> 5; >=1.0 -> 6; <1.0 -> 7.
• LTV band: <=0.65 -> 3; <=0.75 -> 4; <=0.85 -> 5; <=1.0 -> 6; >1.0 -> 7.
• Delinquency minimum (payment_status): "30 Days Past Due" -> 4; "60 Days Past Due" -> 5; "90+ Days Past Due" -> 7; "Nonaccrual" -> 8; "Current" -> none (no delinquency factor).
• final_rating = max(rating over available factors).
• IMPORTANT: if a loan has NO available objective factor (no DSCR, no LTV/ collateral, and Current status), it RETAINS its current_rating as the final rating. Do not drop it and do not leave it ungraded — it still counts in population counts and exposure totals.
• Material downgrade: final_rating - current_rating >= 2 (policies.risk_rating.material_downgrade_notches = 2). Report only these in a material_downgrades list; include downgrade_notches = final - current.
• Top / most-severe problem credit: the loan with the worst final_rating, ties broken by largest exposure. For a Nonaccrual + underwater (LTV>1) worst credit, recommended_action is the most severe enum value the template allows.

3. NPA / delinquency benchmark variance

• NPA exposure = branch nonperforming_loans (= sum of Nonaccrual balances).
• branch_total_loans = latest metrics.total_loans_outstanding.
• branch_npa_ratio = NPA exposure / total_loans (4dp).
• For an "NPA / noncurrent" comparison the FDIC metric is total_loans_noncurrent_pct. For a real-estate delinquency (30-89) comparison the FDIC metric is total_real_estate_30_89_pct. Pick the metric that matches what the task is measuring (noncurrent vs 30-89 delinquency).
• branch_delinquency_ratio for a 30-89 comparison = the branch metric delinquency_30_plus_pct, used as reported (4dp).
• variance_ratio = branch - benchmark (4dp); variance_bps = *10000 (2dp).
• benchmark_version strings: FDIC = "fdic_q4_2024"; NCUA = "ncua_q1_2025".

4. CDFI factor scoring (from policies.cdfi_factor_scores)

Sum band scores of four factors; lower is better:

• debt_to_asset: <0.40 ->0; 0.40-0.60 ->2; 0.60-0.80 ->4; >0.80 ->6.
• ltv: <0.40 ->0; 0.40-0.60 ->2; 0.60-0.80 ->4; >0.80 ->6.
• fico: >720 ->0; 680-720 ->1; 580-679 ->3; <580 ->5.
• liquidity_months: >12 ->0; 6-12 ->1; 3-6 ->3; <3 ->5.
• debt_to_asset for an applicant when not given = total_debt / total_assets. Risk class from total factor_score:
• Prime 0-5; Desirable 6-9; Satisfactory 10-13; Watch 14-18; Doubtful >=19; Projected Loss when score>=19 AND ltv>1.0. Missing-factor handling: treat a missing factor as 0 (score only available factors — "assign classes from available objective factors"). Be aware this is the band most likely to need a per-task sanity check; if a clearly distressed loan (nonaccrual + underwater) lands in a benign class, reconsider whether the task wants missing factors scored at the worst band instead. factor_score is an integer.

5. Watch-list / adverse population and DSCR stress

• Adverse / watch-list population = loans with current_rating >= the minimum the task names (e.g. 6 or worse). adverse_rating_min = that minimum.
• monitoring_cadence for an adverse watch-list = "monthly".
• Watch-list parallel +200bp stress (policies.stress.watch_list_formula): stressed_dscr = base_dscr / (1 + 0.18). shock_label = "+200bp". breach_threshold = coverage_breach_threshold = 1.0; breaches if stressed_dscr < 1.0. Only loans WITH a DSCR appear in stress results; breach_loan_ids ascending.
• CRE dual-stress (policies.stress.cre_dual_stress_formula): stressed_dscr = base_dscr * 0.85 / (1 + 0.18); same 1.0 breach threshold.
• workout_queue ordering = descending exposure, then ascending loan_id.
• severe_bucket_counts: group by (current_rating, payment_status); order ascending current_rating then payment_status in the canonical status order (Current, 30 Days Past Due, 60 Days Past Due, 90+ Days Past Due, Nonaccrual).
• projected_loss flag: true for a loan that is underwater (ltv>1.0) and Nonaccrual / in the Projected Loss class.
• Action enum ladder (more severe with rating/status): monitor < watchlist < special_assets < workout < partial_chargeoff_review < legal_referral. Map current/low-severity to monitor or watchlist; 90+ past due to special_assets; nonaccrual/loss to workout / legal_referral. (Exact per-rating mapping is the least certain piece; keep it monotonic with severity.)

6. Capacity + concentration ALLOCATION tasks (pending applications)

This is the single biggest pitfall: an allocation/decision task is driven by CAPACITY and SECTOR CONCENTRATION, NOT by aggressive per-loan credit floors. Do NOT decline applications merely for moderate DSCR or LTV. Decline only for severe, objective disqualifiers.

• Lending capacity = branches.lending_capacity_q1.
• Sector ceiling = sector_exposures.limit_pct for that sector if present, else branches.sector_ceiling_pct (default).
• Concentration ratio denominator = total branch loan portfolio (= sum sector_exposures = total_loans_outstanding). Post-approval ratio uses the denominator grown by the approved amounts.
• Grandfathering (policies.capacity_concentration): a sector already over ceiling may be grandfathered, BUT a new approval that worsens an over-ceiling sector requires a mitigation. allowed_mitigations: participation_required, reduced_amount, board_exception. Such an approval becomes participation_required or conditional_approve (with the mitigation as a condition), not a plain approve.
• Capacity: rank applications, allocate until lending_capacity_q1 is exhausted; applications that no longer fit get a capacity_limit decline (or defer).
• priority_ranking lists application_ids highest-priority first, including only approved and conditionally-approved (and participation_required) applications.
• Severe credit declines (objective): recent bankruptcy within the policy window, FICO below the bottom band (<580), underwater collateral (ltv>1.0) on weak credits, documentation_complete=0 -> documentation_gap. Startup (years_in_ business < ~2) -> conditional_approve with startup_monitoring, or sba_guaranty_required if an SBA guaranty is present.
• decline_reasons maps each declined application_id to a sorted list of reason codes from the template enum.

7. Competing CRE decision tasks

• weighted_cdfi_score uses policies.cre_weighted_score.weights (capacity .45, collateral_exposure .36, conditions .11, character .05, capital .03); lower is better. Map the 5 "C" inputs to factor band scores (capacity from DSCR strength, collateral_exposure from LTV, capital from debt_to_asset, character from FICO). Classes: approve_quality <=2.0; conditional <=3.0; weak >3.0. Report score to 1dp. (The exact numeric mapping is the least certain piece; the RELATIVE ranking and the selection are what matter most — see next bullets.)
• Select the stronger credit = the LOWER weighted_cdfi_score (better DSCR, lower LTV, passes stress).
• Run the CRE dual-stress on each; a stressed_dscr < 1.0 is a weak_dscr reason for that application.
• Concentration: existing_cre_exposure = sum of loan_type=CRE outstanding balances; existing_cre_concentration = that / total_loans_outstanding (4dp); selected_post_approval = (existing_cre + selected_requested) / (total_loans + selected_requested) (4dp); selected_policy_variance_bps = (post - cre_policy_limit_pct) * 10000 (2dp).
• When the post-approval CRE concentration blows past cre_policy_limit_pct, the recommended path is participation_required (cap bank-retained exposure), and the selected application's decision in applications_compared must match the path. Do not output a plain "approve" while the path is participation_required.
• FDIC underperformance: fdic_benchmark_metric = total_real_estate_30_89_pct; branch_delinquency_ratio = delinquency_30_plus_pct; if branch > benchmark the branch underperforms -> add fdic_adverse_variance as a reason code.
• Typical reason codes here: both apps carry fdic_adverse_variance (branch underperforms FDIC) and sector_breach (CRE over limit); the stress-failing / unselected app adds weak_dscr (and high_ltv only if ltv>0.80). unselected_disposition is decline or defer (restricted enum). reason-code lists are sorted alphabetically.

8. Credit-union segment posture page

• state_metrics: copy the target state's NCUA row EXACTLY as integers (delinquency_bps, loan_to_share_pct, roaa_bps, positive_net_income_pct); benchmark_version = "ncua_q1_2025".
• peer_states = segment.peer_states, sorted ascending.
• Direction fields compare the target state's RAW value to the comparison value: higher / lower / equal (NOT good/bad). Compute nc_vs_us against the US row, and nc_vs_peer_median against the median of the peer-state rows (median of 3 = the middle value, per metric).
• external_risk_status: weaker_than_national_and_peers if the state is worse on the risk-relevant metrics vs both US and peers (higher delinquency, lower roaa, lower positive_net_income, etc.); otherwise mixed / stronger.
• capacity_status: capacity_available if the segment's quarterly_capacity has room (segment notes usually state this); else constrained / none.
• risk_tolerance (in interpretation) = the segment's STATED risk_tolerance value (copy it, e.g. "moderate"); do NOT derive it from the posture.
• posture: continue_with_tighter_conditions when capacity is available but external risk is weaker; temporarily_pause only when metrics demand a halt; continue_approving when clean.
• committee_message: capacity_available_but_external_risk_weaker (capacity but weak external), pause_until_state_metrics_recover, or routine_approval_path_supported — match it to posture/capacity/risk.
• controls.required_checklist_gates = segment.minimum_checklist (as a set).
• controls.added_operating_controls: driven by the segment's internal_context. A missed-insurance-binder control issue -> pre_close_insurance_binder_ verification and lien_perfection_prior_to_funding; external delinquency above national -> quarterly_state_benchmark_monitoring and monthly_segment_delinquency_watch; plus senior_underwriter_second_review. Only add committee_exception_for_capacity_overrun if capacity is actually constrained/overrun.
• escalation_triggers: trigger_id ascending; map conditions to owners: delinquency thresholds and state-gap widening -> credit_risk_manager; insurance/lien exceptions -> operations_control_manager; capacity overrun / exception requests -> lending_committee_chair.

9. Step-by-step SOP for a NEW task

1. Read the prompt and the answer_template fully; list every required key, enum, ordering rule, and precision.
2. GET /api/manifest, GET /api/policies. Identify which policy tables the task needs.
3. Pull the target branch/segment + metrics + (loans | applications | sector- exposures) + the right benchmark (FDIC for banks, NCUA for credit unions).
4. Build the correct population (apply min_current_rating / sector / loan_type filters exactly as worded; keep null-factor loans in counts).
5. Compute per the relevant section above (rating re-derivation / CDFI / stress / concentration / posture). Round at the END to the template precision.
6. Assemble JSON in template order; sort every list by its stated ordering key; restrict every enum to allowed_values.
7. Run the self-check, then emit JSON only (no narrative).

10. Self-check before emitting

• All required top-level and nested keys present; no extras that break shape.
• Every enum value is in the template's allowed_values.
• Population counts and exposure totals include null-factor loans; no loan double-counted or dropped.
• No-objective-factor loans retained current_rating; material downgrade uses >=2 notches.
• Denominators correct: portfolio = total_loans_outstanding = sum sector exposures; post-approval denominators grown by approvals.
• Stress formulas: watch-list = dscr/1.18; CRE = dscr*0.85/1.18; breach<1.0.
• Allocation tasks: decisions driven by capacity + concentration, NOT moderate-credit declines; over-ceiling new approvals carry a mitigation; selected/path/decision are internally consistent.
• Precision applied: money 2dp, ratio 4dp, bps 2dp, dscr 2dp, weighted 1dp, counts/ratings integer.
• Lists sorted by the exact ordering key (loan_id/application_id/sector asc; workout_queue exposure desc then loan_id asc; reason codes alphabetical).
• benchmark_version strings exact: "fdic_q4_2024" / "ncua_q1_2025".
• Output is valid JSON and nothing else.