managing-credit-risk-models
Evaluates and monitors credit risk models (PD, LGD, EAD) with calibration and discrimination metrics. Use when validating credit models, assessing model performance, or calibrating default models.
Best use case
managing-credit-risk-models is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Evaluates and monitors credit risk models (PD, LGD, EAD) with calibration and discrimination metrics. Use when validating credit models, assessing model performance, or calibrating default models.
Teams using managing-credit-risk-models should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/managing-credit-risk-models/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How managing-credit-risk-models Compares
| Feature / Agent | managing-credit-risk-models | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Evaluates and monitors credit risk models (PD, LGD, EAD) with calibration and discrimination metrics. Use when validating credit models, assessing model performance, or calibrating default models.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Managing Credit Risk Models ## When To Use - Periodic validation of Probability of Default (PD), Loss Given Default (LGD), or Exposure at Default (EAD) models - Annual or triggered model performance reviews required by internal model governance or regulatory mandate (e.g., SR 11-7, CRD/CRR, IFRS 9 ECL frameworks) [VERIFY jurisdiction-specific regulatory requirements] - Recalibration following material portfolio shifts, macroeconomic regime changes, or post-merger data integration - Comparing challenger models against incumbent production models before promotion - Preparing model risk management reports for MRC/board-level review ## Inputs To Gather - **Development sample and out-of-time/out-of-sample validation data** — confirm vintage coverage, default definition consistency, and exclusion criteria - **Model documentation** — original methodology paper, variable selection rationale, and any prior validation findings - **Current production scorecards or parameter estimates** — PD term structures, LGD cure/workout assumptions, EAD CCF tables - **Realized outcome data** — observed default flags, recovery cashflows, drawn/undrawn balances at default - **Portfolio segmentation** — rating grades, facility types, collateral categories, geographic or industry cuts - **Regulatory and policy thresholds** — minimum discrimination (e.g., Gini > 0.40), calibration tolerance bands, override rate caps [VERIFY institution-specific thresholds] ## Workflow 1. **Scope the validation exercise** - Identify which model components are in scope (PD only, full PD/LGD/EAD suite, segment-level vs. portfolio-level) - Confirm the observation window and default/loss outcome definitions match the model's design assumptions - Document any data limitations or exclusions upfront 2. **Assess discrimination performance** - Compute Gini coefficient (Accuracy Ratio), AUC-ROC, and Kolmogorov-Smirnov statistic on the validation sample - Generate CAP (Cumulative Accuracy Profile) and ROC curves - Segment discrimination by key risk drivers (vintage, industry, geography) to detect pockets of weakness - Compare current-period metrics against development-sample benchmarks and prior validation results 3. **Evaluate calibration accuracy** - Run Binomial test, Hosmer-Lemeshow test, or traffic-light approach (Basel) across PD buckets - For LGD: compare predicted vs. realized loss severity by collateral type and workout path - For EAD: compare predicted CCFs against observed utilization at default - Assess calibration across economic cycles — flag if model was calibrated to benign conditions and current environment is stressed [VERIFY whether TTC vs. PIT calibration applies] 4. **Evaluate stability and concentration** - Population Stability Index (PSI) on score distributions between development and recent periods - Characteristic Stability Index (CSI) on key input variables - Herfindahl index or grade-concentration analysis to detect rating migration clustering - Flag PSI > 0.25 or CSI > 0.25 as material shifts requiring deeper investigation [VERIFY institution-specific PSI thresholds] 5. **Stress-test and sensitivity analysis** - Perturb key macro drivers (GDP, unemployment, HPI) and assess PD/LGD migration under stressed scenarios - Identify variables with outsized sensitivity — single-variable stress contributions exceeding a defined threshold - Cross-check stressed outputs against institution's CCAR/DFAST or ICAAP submissions if applicable [VERIFY regulatory stress testing framework] 6. **Document findings and recommend actions** - Classify findings by severity: Tier 1 (material, requires remediation before next use), Tier 2 (significant, remediation within defined timeline), Tier 3 (minor, monitor) - Provide specific recalibration or redevelopment recommendations with target timelines - Draft executive summary for Model Risk Committee or board reporting ## Output - **Model Validation Report** containing: - Executive summary with overall model rating (e.g., Satisfactory / Needs Improvement / Unsatisfactory) - Discrimination metrics table (Gini, AUC, KS) with trend comparison across validation periods - Calibration test results by grade, segment, and time horizon - Stability analysis with PSI/CSI tables and heatmaps - Findings register with severity tier, description, and remediation action/owner/deadline - Appendices: data quality notes, exclusion log, detailed statistical output ## Quality Checks - Confirm default and loss definitions used in validation match the model's training definitions exactly — misalignment here invalidates all downstream metrics - Verify that validation data has no lookahead bias (outcomes must post-date the score assignment) - Cross-check sample sizes per rating grade — bins with fewer than 30 defaults produce unreliable calibration test results - Ensure discrimination and calibration metrics are computed on the same population; filtered vs. unfiltered samples can yield contradictory conclusions - Validate that any override or judgmental adjustment rates are reported separately and not mixed into statistical performance metrics - Confirm findings are mapped to specific model components — avoid blanket "model is weak" conclusions without identifying which parameter (PD, LGD, or EAD) and which segment drives the issue
Related Skills
managing-wound-care
Guides wound assessment, classification, and treatment selection with documentation requirements. Use when managing surgical wounds, classifying wound types, or selecting wound care protocols.
managing-wound-assessment-nursing
Structures wound assessment with measurement, staging, and treatment plan documentation. Use when assessing wounds, staging pressure injuries, or documenting wound care.
managing-workplace-safety-healthcare
Tracks OSHA healthcare requirements including bloodborne pathogen, TB, and violence prevention programs. Use when managing OSHA compliance, implementing safety programs, or documenting exposure incidents.
managing-workers-compensation-rehabilitation
Structures workers comp rehab documentation with functional capacity evaluation and return-to-work planning. Use when managing work injury rehab, performing FCEs, or documenting return-to-work status.
managing-vestibular-rehabilitation
Structures vestibular assessment with positional testing and customized exercise programs. Use when evaluating vestibular disorders, performing Dix-Hallpike testing, or designing vestibular exercise programs.
managing-venous-thromboembolism-prophylaxis
Applies VTE risk assessment (Padua, Caprini) with appropriate prophylaxis selection. Use when assessing VTE risk, selecting prophylaxis regimens, or documenting DVT prevention.
managing-valvular-heart-disease
Guides valve disease severity assessment with intervention criteria and surveillance schedules. Use when evaluating valve disease, assessing surgical/interventional timing, or monitoring valve function.
managing-vaccine-schedules
Applies CDC immunization schedules with catch-up protocols and contraindication screening. Use when managing vaccinations, creating catch-up schedules, or documenting immunization decisions.
managing-vaccination-campaigns
Plans mass vaccination campaigns with logistics, cold chain management, and adverse event monitoring. Use when planning vaccination drives, managing immunization logistics, or monitoring VAERS.
managing-traumatic-brain-injury-rehabilitation
Structures TBI rehab with Rancho Los Amigos scoring and cognitive rehabilitation protocols. Use when managing TBI rehab, tracking Rancho levels, or implementing cognitive therapy.
managing-trauma-assessments
Conducts structured primary and secondary trauma surveys following ATLS methodology. Use when assessing trauma patients, documenting trauma workups, or coordinating trauma team activations.
managing-transplant-evaluations
Guides transplant candidacy evaluation with organ-specific criteria and listing documentation. Use when evaluating transplant candidates, documenting listing criteria, or coordinating transplant workups.