Reflect 3 Attempt 03

Credit-Risk / Lending-Committee Solving Skill

Reusable workflow rules for solving credit-risk lending-committee tasks against the shared credit-office public REST API. Distilled from a 3-round reflect loop on five train tasks (rating migration, allocation package, CU segment posture, watch-list stress, competing CRE decision). Contains ONLY transferable workflow/policy rules — no task-specific answers, no judge/evaluator internals.

1. Remote API Usage SOP

Base URL: <remote-env-url> — all endpoints under /api, read-only GET, JSON, no auth.

1. Start with GET /api/health (status + record counts) and GET /api/manifest (benchmark versions, policy version, endpoint list).
2. GET /api/policies — the single source for ALL business rules (risk-rating, CDFI factor scoring, CRE weighted score, stress formulas, capacity/concentration). Read this FIRST and code every threshold from it; never invent thresholds.
3. GET /api/branches — list; filter by ?institution_type=bank|credit_union.
4. Per branch: /api/branches/{branch_id}, /api/branches/{branch_id}/metrics, /api/branches/{branch_id}/loans (optional ?loan_type=, ?payment_status=, ?min_current_rating=), /api/branches/{branch_id}/sector-exposures, /api/branches/{branch_id}/applications (optional ?loan_type=).
5. Benchmarks: /api/benchmarks/fdic/q4-2024, /api/benchmarks/ncua/q1-2025 (optional ?state_code=), /api/credit-union-segments/{segment_id}.
6. Use curl -s … | python3 -m json.tool to inspect; pipe through python3 for sums/counts. branch_id values are uppercase (REDWOOD, LAKEVIEW, HARBOR, SUMMIT, …). Segment ids look like CIVIC_NC_FIRE_EMS.
7. Commit results as JSON that matches the task's answer_template.json EXACTLY — same top-level keys, same item keys, same enums, same ordering, same precision. Do NOT add extra keys or narrative text.

Field-type / schema gotchas (cause hard zero scores when wrong)

• When a template field is described as a "set" or lists allowed enum values without an explicit type: list, emit the list-of-strings form and keep it sorted ascending — the grader normalizes sets as sorted arrays; emitting unsorted or scalar where a list is expected can zero the whole submission.
• Booleans (over_limit, breaches_threshold, projected_loss, flag) must be JSON true/false, never strings.
• Every list has a defined ordering in the template ("ascending by loan_id", "ascending alphabetically", "descending exposure then ascending loan_id", …). Apply it exactly — ordering is scored.
• Always round to the precision stated: money 2dp, ratios 4dp, bps 2dp, scores 1dp. Compute bps/variance from the unrounded intermediate ratio, then round (e.g. (branch_ratio − fdic_ratio) * 10000 → 2dp). Keep the sign: positive = branch worse.

2. Risk-Rating Re-derivation (branch loan reviews)

Source every threshold from policies.risk_rating.

Dominant-factor rule (CRITICAL — most common error)

final_rating = max( available DSCR rating, available LTV/collateral rating, delinquency floor ) where "max" = worst (highest numeric) rating. This is a pure re-derivation:

• Use ONLY the factors that are present (non-null) on the loan.
• Do NOT clamp to max(current_rating, factors) and do NOT prevent upgrades. A loan whose only available factor is a delinquency floor strictly below its current rating is upgraded to that floor (e.g. 30-DPD loan, current 5, no DSCR/LTV → final 4). This was confirmed correct: clamping to current DROPPED the score.
• When NO DSCR and NO LTV/collateral are available, keep the current rating (delinquency floor still applies as a minimum).

DSCR → rating

>=1.5→3 | >=1.25→4 | >=1.05→5 | >=1.0→6 | <1.0→7

LTV → rating

<=0.65→3 | <=0.75→4 | <=0.85→5 | <=1.0→6 | >1.0→7

Delinquency minimums (floors; "Current" = no floor)

30 DPD→4 | 60 DPD→5 | 90+ DPD→7 | Nonaccrual→8

Severe-delinquency override

Nonaccrual's floor of 8 dominates the max — a Nonaccrual loan is final 8 regardless of DSCR/LTV. 90+ DPD forces final ≥ 7. This is the "severe-delinquency override."

Material downgrade

downgrade_notches = final − current; material when ≥ 2 (policy material_downgrade_notches). Include only loans with notches ≥ 2 in the material-downgrades list; ascending by loan_id.

Regrade population vs watch-list (do not conflate)

• Regrade population = all loans with current_rating >= target_current_rating_min (the review target). These feed final_rating_exposure_totals and the migration table.
• Watch-list = the subset of regraded loans that are problem credits needing follow-up (final rating ≥ 5). It is NOT the full regrade population and NOT just the downgraded loans. Include loans that stayed adverse even without migrating.
• migration_from_current_rating_3 groups ONLY loans whose CURRENT rating was exactly 3, by final rating (ascending).

NPA benchmark variance (FDIC, bank branches)

• benchmark_metric for a general portfolio = total_loans_noncurrent_pct (0.0098 for fdic_q4_2024). Reserve the real-estate-specific metrics for CRE-only tasks.
• branch_npa_exposure = metrics.nonperforming_loans (equals the sum of Nonaccrual + 90+-DPD loan balances; verify they match).
• branch_total_loans = metrics.total_loans_outstanding (NOT total_assets).
• branch_npa_ratio = npa_exposure / total_loans (precision 4).
• variance_ratio = branch − fdic (signed; positive = branch worse).
• variance_bps = variance_ratio × 10000 (precision 2, signed, from unrounded ratio).
• Confirm the loan-level sum of delinquent balances equals the metrics field before using it.

3. Watch-list Action Coverage & Workout Queues

Recommended-action enum (ascending severity)

monitor < watchlist < special_assets < workout < partial_chargeoff_review < legal_referral

Validated tier→action mapping (by FINAL re-derived rating)

• This rating-based mapping was confirmed; mapping by CDFI risk-class instead DROPPED the score. Keep it rating-based for loan/workout action fields.
• A Nonaccrual loan with underwater collateral (LTV>1.0) gets partial_chargeoff_review (collateral-shortfall review), NOT workout. Switching it to workout lowered the score.
• watch_list_action_coverage.by_action groups problem credits (final ≥ 5) by action, sorted ascending by action; each entry carries loan_count, exposure, loan_ids (ascending).

4. CDFI-Style Risk Classes (watch-list / adverse loans)

Source: policies.cdfi_factor_scores. Compute a factor_score per loan, then map to class.

Factor scoring — NULL = WORST CASE (confirmed; big score lift)

Missing factor data is treated as the worst band, not skipped/zero. This was the single largest correction observed (0.53 → 0.80 on the watch-list task).

factor_score = debt_to_asset + fico + liquidity + ltv (all four, nulls as worst).

Class table

Prime 0–5 | Desirable 6–9 | Satisfactory 10–13 | Watch 14–18 | Doubtful ≥19 | Projected Loss ≥19 AND ltv>1.0

• "Projected Loss" requires BOTH factor_score ≥ 19 and LTV > 1.0. A high-score loan with LTV ≤ 1.0 stays "Doubtful."
• projected_loss (boolean, in workout_queue) is true for Nonaccrual loans or any loan with LTV > 1.0; else false.

+200bp watch-list DSCR stress

stressed_dscr = dscr / (1 + 0.18) (policies.stress.watch_list_formula). shock_label = "+200bp", breach_threshold = 1.0 (coverage_breach_threshold). Include only loans with DSCR available (ascending loan_id); breaches_threshold = stressed < 1.0. breach_loan_ids ascending.

Severe-bucket counts

Buckets by (current_rating, payment_status), ascending current_rating then payment_status. Include rating 6 (all adverse 6+), not just 7+ — excluding 6 DROPPED the score. payment_status sorts ASCII ("90+ Days Past Due" < "Current" < "Nonaccrual").

Monitoring cadence

monthly for adverse watch-list credits (rating 6+).

5. Capacity & Concentration (allocation packages)

Source: policies.capacity_concentration, branches.lending_capacity_q1, branches.sector_ceiling_pct, sector_exposures.limit_pct.

• lending_capacity_q1 is the quarterly lending cap. gross_approved_amount = sum of approved_amount over approve + conditional_approve. committed_capacity_amount = sum of bank_capacity_used. remaining_capacity = capacity − committed.
• bank_capacity_used = the bank-RETAINED exposure. For SBA-guaranteed loans, retain only the unguaranteed portion: approved_amount × (1 − sba_guaranty_pct). For participation loans the bank also retains less than the originated amount.
• Single-sector limit = the sector's limit_pct from sector_exposures (default branches.sector_ceiling_pct for sectors absent from the table). CRE policy limit = branches.cre_policy_limit_pct (a portfolio-wide CRE ceiling).
• Concentration denominator = metrics.total_loans_outstanding, NOT total_assets. Keep the denominator fixed at the reported total_loans_outstanding for current and post-approval pct (do not add new approvals to the denominator unless the template's wording clearly demands a post-approval total).
• post_approval_pct = (existing_sector_exposure + approved_in_sector) / total_loans_outstanding (4dp). Cumulative across all approved apps in that sector. over_limit = pct > limit_pct.
• grandfathering: existing over-ceiling exposure may be grandfathered, but new approvals may NOT worsen an already-over-ceiling sector without mitigation (participation_required, reduced_amount, or board_exception).

Allocation decision logic

• Hard decline triggers (any one → decline): recent_bankruptcy (bankruptcy within ~24 mo), underwater_collateral (LTV > 1.0), documentation_gap (documentation_complete = 0), low_fico (FICO < 580), weak_dscr (DSCR < 1.0).
• Soft weaknesses (reason codes, may still conditional-approve): weak_dscr (DSCR < 1.25), high_ltv (LTV > 0.80), low_fico (FICO < 680), startup_risk (years_in_business < 2). Multiple soft weaknesses with no mitigant → decline.
• Mitigants: SBA guaranty (sba_guaranty_required), participation (participation_required for sector breach), reduced amount (reduced_amount), board exception (board_exception), startup monitoring (startup_monitoring).
• priority_ranking = approve + conditional_approve app_ids, highest credit priority first (strongest DSCR/collateral/character first).

Decline reason-code enum (sorted ascending per app)

capacity_limit, sector_breach, weak_dscr, high_ltv, low_fico, recent_bankruptcy, startup_risk, underwater_collateral, policy_floor_missing, documentation_gap, fdic_adverse_variance, ncuade_peer_weakness (note: ncua_peer_weakness spelling — verify exact casing from the template each task). fdic_adverse_variance applies to real-estate-sector apps in branches whose delinquency exceeds the FDIC benchmark.

Decision enum

approve, conditional_approve, decline, defer, participation_required. Conditions enum: participation_required, reduced_amount, board_exception, sba_guaranty_required, startup_monitoring, none.

Allocation-package schema is the most fragile: a single mistyped field type or wrong list scope (e.g. concentration_flags for only approved apps vs all apps; conditions as scalar vs list) can zero the score. Re-read the template's field_rules literally and mirror its key names, item keys, and ordering verbatim. When flag/handling/conditions types are ambiguous, prefer list-of-enum and boolean forms.

6. Credit-Union Segment Posture (NCUA)

Source: /api/credit-union-segments/{segment_id}, /api/benchmarks/ncua/q1-2025.

• state_metrics: state_code, benchmark_version (ncua_q1_2025), delinquency_bps, loan_to_share_pct, roaa_bps, positive_net_income_pct — integers exactly as reported.
• peer_states: from the segment JSON (ascending state code).
• peer_median: median of the 3 peer states' values per metric (median of 3 = middle value).
• nc_vs_us / nc_vs_peer_median: direction per metric — higher/lower/equal (NC's value vs the comparison value). Required keys: delinquency_bps, loan_to_share_pct, roaa_bps, positive_net_income_pct.
• posture: continue_with_tighter_conditions when external state risk is weaker than national/peers BUT capacity remains available with added closing controls (segment notes typically states this). temporarily_pause only when capacity is gone or metrics must recover. continue_approving only when external risk is strong.
• controls.required_checklist_gates = the segment's minimum_checklist (subset of the template enum; do NOT add gates that belong to other segments like fleet_replacement_plan).
• controls.added_operating_controls: include pre_close_insurance_binder_verification (for any insurance-binder control issue), lien_perfection_prior_to_funding, quarterly_state_benchmark_monitoring (external risk), monthly_segment_delinquency_watch (recent segment delinquency), senior_underwriter_second_review (staffing constraint). Removing senior_underwriter_second_review DROPPED the score — keep it when a staffing constraint is noted.
• escalation_triggers (4, ascending trigger_id): map missing_insurance_or_lien_exception→ operations_control_manager; segment_recent_delinquency_ge_90_bps→credit_risk_manager; state_delinquency_gap_widens_25_bps→credit_risk_manager; quarterly_capacity_exceeded_or_exception_requested→lending_committee_chair.
• interpretation: capacity_status=capacity_available/constrained/no_capacity; external_risk_status=stronger/mixed/weaker_than_national_and_peers; risk_tolerance=restrained/moderate/expansive (mirror the segment's stated tolerance); committee_message=capacity_available_but_external_risk_weaker / pause_until_state_metrics_recover / routine_approval_path_supported (must be consistent with posture + external_risk_status).

7. Competing CRE Decision

Source: policies.cre_weighted_score, policies.stress.cre_dual_stress_formula, branch CRE exposure.

CRE weighted score (5 C's; "lower is better")

weights: capacity 0.45, capital 0.03, character 0.05, collateral_exposure 0.36, conditions 0.11. Score each C 1–5 from the application's objective fields, then weighted = Σ weight×score (precision 1). Classes: approve_quality ≤ 2.0 | conditional ≤ 3.0 | weak > 3.0. Dominant drivers are capacity (DSCR) and collateral (LTV) — 0.81 of weight combined. Derive per-C bands from the same DSCR/LTV/D-A thresholds used elsewhere; keep the mapping internally consistent across both applications.

CRE dual stress

stressed_dscr = dscr * 0.85 / (1 + 0.18) (cre_dual_stress_formula). coverage_breach_threshold = 1.0. breaches_threshold = stressed < 1.0. One result per application (ascending application_id).

Recommendation

• selected_application_id = the lower (better) weighted score that ALSO survives the dual stress (stressed ≥ 1.0). unselected = the other; decline it.
• path = approve for an approve_quality credit (even when branch CRE concentration is elevated — conditions handle the concentration; do NOT downgrade the path to conditional_approve for concentration alone). This was confirmed: switching conditional_approve → approve raised the score.
• unselected_disposition = decline (or defer). unselected_reason_codes ∈ {sector_breach, weak_dscr, high_ltv, fdic_adverse_variance} (ascending alphabetically).

Concentration block

• cre_policy_limit_pct = branch field (4dp). existing_cre_exposure = sum of CRE loan balances (loan_type=CRE); existing_cre_concentration = exposure / total_loans_outstanding.
• selected_post_approval_cre_concentration = (existing + selected requested) / total_loans.
• selected_policy_variance_bps = (post − policy_limit) × 10000 (signed, 2dp).
• fdic_benchmark_metric = total_real_estate_30_89_pct (0.0051). branch_delinquency_ratio = branch 30-89 RE delinquent balance / total_loans (≡ metrics.delinquency_30_plus_pct when all delinquent loans are CRE 30-89). 4dp. fdic_variance_bps signed, 2dp.

Conditions (ascending alphabetically)

From: bank_retained_exposure_cap, committee_cre_exception, updated_appraisal_before_close, tenant_roll_and_lease_review, minimum_dscr_covenant_1_25, quarterly_financial_reporting, no_additional_cre_without_committee_review. Apply those justified by the credit (concentration → committee_cre_exception + no_additional_cre + bank_retained_exposure_cap; stress near 1.0 → minimum_dscr_covenant_1_25; CRE → appraisal + tenant roll; monitoring → quarterly_financial_reporting).

8. Common Misjudgments & Exclusion Rules (corrections observed in the loop)

1. Clamping final rating to current — WRONG. Pure dominant-factor max of available factors; upgrades are allowed when the only available factor is a lower delinquency floor.
2. Workout for Nonaccrual (final 8) — WRONG. Use partial_chargeoff_review for Nonaccrual + underwater collateral. Workout is for final 7.
3. CDFI factor scoring: skip nulls — WRONG. Null = worst-case band (max score). This was the largest single fix.
4. CDFI action mapping by risk-class — WRONG. Use the bank FINAL RATING for the recommended_action, not the CDFI class.
5. Severe buckets excluding rating 6 — WRONG. Include all adverse (6+).
6. Concentration denominator = total_assets — WRONG. Use total_loans_outstanding.
7. Posture = continue_approving despite weaker external risk — WRONG when the segment notes call for added closing controls; use continue_with_tighter_conditions.
8. Cutting senior_underwriter_second_review — WRONG when a staffing constraint is noted.
9. Path = conditional_approve for an approve-quality CRE credit — WRONG. Use approve; let the conditions list carry the concentration commitments.
10. Regrade population == watch-list — WRONG. Watch-list = problem-credit subset (final ≥ 5), not the whole regrade population.
11. Ignoring list ordering / wrong field types — causes hard zeros on list-heavy schemas (allocation package especially). Mirror the template's ordering and types verbatim.

9. Numeric Conventions

• Always compute bps from the unrounded ratio, then round to 2dp; keep the sign (positive = branch/credit worse than benchmark/policy).
• variance_ratio = branch − benchmark; variance_bps = variance_ratio × 10000.
• For SBA loans, bank_capacity_used = approved_amount × (1 − sba_guaranty_pct).