From pm-banking
Generates a structured lending risk brief with concentration tables, migration narrative, scenario analysis, and recommended actions for portfolio risk reports.
How this skill is triggered — by the user, by Claude, or both
Slash command
/pm-banking:lending-risk-briefThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
A loan book fails in patterns before it fails in names. This skill writes the portfolio-level brief that makes the patterns visible: where the book is concentrated, which vintages are misbehaving, which way the grades are migrating, what the macro could do to it, and which names need decisions now.
A loan book fails in patterns before it fails in names. This skill writes the portfolio-level brief that makes the patterns visible: where the book is concentrated, which vintages are misbehaving, which way the grades are migrating, what the macro could do to it, and which names need decisions now.
Ask for what's available; compute what the data supports and mark the rest [data gap — request from portfolio systems]:
1. Concentration. Three cuts, each vs its limit (or vs a stated reference norm if no limit exists — and flag the missing limit as a finding): top sector and top-3 sector share; geographic share; single-name — top-10 and top-20 obligor share, largest single exposure vs capital. Concentration that grew via passive drift (runoff elsewhere) deserves the same flag as active growth — say which it was. Correlated concentrations count together (e.g. construction lending + commercial-real-estate collateral is one bet, not two).
2. Vintage performance. Compare cohorts at the same age on-book (delinquency/default at month 12, 24…), not calendar snapshots — a young book always looks clean. A vintage underperforming its age-matched predecessors signals an underwriting-standards question for that origination period; name the period and what changed in criteria then, if known.
3. Migration narrative. Report net migration by exposure, not count. The narrative must answer: is movement drift (broad one-notch slippage → macro/sector pressure) or jumps (multi-notch falls → underwriting or monitoring misses)? Which sectors drive the downgrades? Are downgrades arriving before delinquency (grading works) or after (grading lags — a finding in itself)?
4. Macro scenarios. Base / adverse / severe. For each: the named driver (rates, unemployment, property values, sector shock) and its transmission channel into this specific book ("+200bps hits the 34% of book on floating rate at refinance; DSCR<1.2x share rises from X to Y [compute from data]"). Severity framing over precision — label all scenario numbers as estimates.
5. Watch names. Top 10 by exposure-weighted concern: name/ref, exposure, grade and recent movement, the concern in one sentence, the action and its owner and date.
6. Actions. Each tied to a finding: limit proposals, sector pause/tighten, deep-dive reviews, provision considerations, data fixes. An observation without an action is a gap — either act or state why watching is the action.
1. Headline read — 3–4 sentences: direction of book quality and the one thing committee must decide. 2. Concentration — table per cut: segment | exposure | share % | limit | headroom | trend. 3. Vintage performance — cohorts at matched age, worst vintage named. 4. Migration — net migration by exposure + the drift-vs-jumps narrative. 5. Scenarios — base/adverse/severe: driver | transmission channel | estimated impact. 6. Top-10 watch names — ref | exposure | grade Δ | concern | action | owner | date. 7. Actions — numbered, each tied to its finding, with owner.
End with: "This brief is analytical support, not a credit, provisioning, or capital determination. Decisions follow your institution's risk policy and applicable regulation."
[data gap]npx claudepluginhub mohitagw15856/pm-claude-skills --plugin pm-bankingBuilds an institutional-quality CRE debt fund portfolio monitoring framework with traffic-light dashboard, watchlist with objective triggers, maturity wall analysis, concentration tracking, CECL-based loss reserves, rate exposure monitoring, facility covenant dashboard, and LP-reportable package.
Analysiert Leasingportfolios auf Ausfall-, Restwert- und Konzentrationsrisiken mit juristischer Prüfung (Sachverhalt, Norm, Beweislast) und liefert ein Arbeitsprodukt mit Prüfpunkten und nächstem Schritt.
Writes a complete credit memo for lending decisions: borrower story, facility structure, repayment sources, ratio spread with covenant headroom, risk factors with mitigants, and risk-rating rationale. For credit committee submissions.