{
  "$schema": "https://aigovernanceengineer.com/schemas/model-card.v1.json",
  "name": "Consumer loan affordability model",
  "version": "3.2.0",
  "registry_id": "credit-afford-03",
  "developer": "Example Retail Bank, retail credit models team",
  "contact": "credit-models@bank.example.org",
  "summary": "Scores the affordability of consumer loan applications for a credit analyst.",
  "description": "A gradient-boosted tree model that estimates the probability that an applicant repays a consumer loan of up to 30k EUR within its first 12 months, with reason codes. The analyst decides every application.",
  "license": "other",
  "languages": [
    "es",
    "pt"
  ],
  "model_type": "gradient-boosted trees",
  "task": "binary classification",
  "architecture_family": "tree ensemble",
  "architecture": "gradient-boosted decision trees, 400 trees, depth 6",
  "learning_approach": "supervised",
  "library": "lightgbm",
  "tags": [
    "credit",
    "affordability",
    "tabular"
  ],
  "repository": "https://git.example.org/credit/affordability",
  "intended_uses": [
    "Support the credit analyst's affordability decision on consumer loan applications up to 30k EUR in Spain and Portugal"
  ],
  "users": [
    "credit analysts",
    "credit risk validation"
  ],
  "out_of_scope_uses": [
    "Fully automated approval or decline without an analyst",
    "Mortgages, business loans or applications above 30k EUR",
    "Fraud detection"
  ],
  "datasets": [
    {
      "name": "loan-apps-2019-2025",
      "role": "training",
      "url": "https://evidence.example.org/dataset-cards/loan-apps-2019-2025",
      "description": "412,000 applications with 12-month outcomes, 2019-01 to 2025-03; self-employed applicants under-represented."
    },
    {
      "name": "loan-apps-holdout-2025",
      "role": "testing",
      "description": "Out-of-time holdout, 2025-04 to 2025-12."
    }
  ],
  "training_data": "Historical applications from the origination and servicing systems, admitted by dataset admission record dar-2026-031.",
  "preprocessing": "Income and debt features winsorised at the 1st and 99th percentiles; age not used as a feature.",
  "training_procedure": "Five-fold cross-validation for hyper-parameters, then a single fit on the full training window.",
  "inputs": [
    "application record (JSON, 42 fields)"
  ],
  "outputs": [
    "affordability score 0 to 1 with up to four reason codes"
  ],
  "evaluation_data": "Out-of-time holdout with the same product mix as production; age bands and employment types sliced.",
  "metrics": [
    {
      "type": "AUC",
      "value": "0.81",
      "lower_bound": "0.80",
      "upper_bound": "0.82",
      "dataset": "loan-apps-holdout-2025"
    },
    {
      "type": "approval-rate ratio",
      "value": "0.86",
      "slice": "applicants over 60 vs 30 to 45",
      "dataset": "loan-apps-holdout-2025"
    }
  ],
  "metrics_rationale": "AUC tracks ranking quality for the analyst's queue; the approval-rate ratio is held at 0.80 or above by the fairness eval gate.",
  "fairness_assessments": [
    {
      "group_at_risk": "applicants over 60",
      "benefits": "Faster decisions on complete applications.",
      "harms": "Age-correlated features could lower scores.",
      "mitigation": "Fairness eval gate on age bands; weekly monitoring in the post-market plan."
    }
  ],
  "limitations": [
    "Less accurate for self-employed applicants with under 24 months of history"
  ],
  "ethical_considerations": [
    {
      "name": "An applicant cannot understand or contest a decline influenced by the score",
      "mitigation": "Reason codes in the decline letter and a human review on request."
    }
  ],
  "human_oversight": "The analyst sees the score, the reason codes and the data, and decides; overrides are logged and sampled monthly.",
  "explainability": "Up to four reason codes per score, from SHAP values mapped to plain-language reasons.",
  "output_interpretation": "A score below 0.35 flags an affordability concern for the analyst to check; it is not a decision.",
  "predetermined_changes": "Quarterly retraining on a rolling window within the tested feature set and thresholds.",
  "compute_and_lifetime": "CPU inference, under 50 ms per score; reviewed at least yearly and retired by 2028-12-31 at the latest.",
  "logging": "Each score is logged with its inputs hash, model version and reason codes to the decision log, retained six years.",
  "cybersecurity": "Model artefact signed in the registry; inputs schema-validated; evasion tests on income features in the test report.",
  "applicability": {
    "high_risk": true,
    "gpai": false
  },
  "links": {
    "instructions_for_use": "https://evidence.example.org/ifu/credit-afford-03-3.2.0",
    "risk_management": "https://evidence.example.org/risk-register/credit-afford-03",
    "impact_assessment": "https://evidence.example.org/fria/fria-2026-004",
    "post_market_monitoring": "https://evidence.example.org/pmm/credit-afford-03",
    "aibom": "https://evidence.example.org/aibom/credit-afford-03/3.2.0.cdx.json"
  },
  "change_log": "3.2.0: added the self-employed slice to the test plan; retrained on data to 2025-03.",
  "last_updated": "2026-09-15"
}