slug: ledger-investing
provider: Ledger Investing
generated_by: planning/capability-mapping/scripts/classify_capabilities.py
model: claude-opus-5
frame:
- Insurance
min_confidence: 0.7
capability_model:
  source: https://github.com/vincentmakes/turbo-ea-capabilities
  license: CC-BY-4.0
  attribution: Turbo EA Capabilities by Vincent Verdet — Turbo EA, https://github.com/vincentmakes/turbo-ea-capabilities,
    CC BY 4.0
  notice: NOTICE
edge_count: 5
edges:
- tag: Cashflow Models
  spec_file: ledger-investing-cashflow-models-api-openapi.yml
  capability_id: BC-2180
  capability_id_l1: BC-2180
  capability_name: Actuarial Management
  confidence: 0.82
  evidence: Bayesian actuarial models for insurance loss triangles ... Development, tail, forecast and
    cashflow models are then fit against a named triangle
  reason: Cashflow models fitted to insurance loss triangles are actuarial modelling compute (payout pattern
    / cashflow projection). Clearly Actuarial Management; the specific L2 (reserving vs capital/ALM use)
    is not pinned down by the operations, so only the L1 is asserted.
- tag: Development Models
  spec_file: ledger-investing-development-models-api-openapi.yml
  capability_id: BC-2180.20
  capability_id_l1: BC-2180
  capability_name: Reserving Actuarial Management
  confidence: 0.82
  evidence: POST /development-model  fitDevelopmentModel  Fit a development model — models for insurance
    loss triangles
  reason: Fitting loss development models to loss triangles and predicting ultimate losses is loss triangulation
    / IBNR estimation, the core of Reserving Actuarial Management.
- tag: Tail Models
  spec_file: ledger-investing-tail-models-api-openapi.yml
  capability_id: BC-2180.20
  capability_id_l1: BC-2180
  capability_name: Reserving Actuarial Management
  confidence: 0.8
  evidence: POST /tail-model  fitTailModel  Fit a tail model ... models for insurance loss triangles
  reason: Tail factor models extend loss development beyond the observed triangle to estimate ultimate
    losses — reserving actuarial modelling (loss triangulation / IBNR).
- tag: Forecast Models
  spec_file: ledger-investing-forecast-models-api-openapi.yml
  capability_id: BC-2180
  capability_id_l1: BC-2180
  capability_name: Actuarial Management
  confidence: 0.75
  evidence: POST /forecast-model  fitForecastModel  Fit a forecast model ... Bayesian actuarial models
    for insurance loss triangles
  reason: Actuarial forecast models fitted to insurance loss triangles — plainly Actuarial Management,
    but forecasting future period loss ratios could serve either reserving or pricing actuarial work,
    so no L2 is asserted.
- tag: Triangles
  spec_file: ledger-investing-triangles-api-openapi.yml
  capability_id: BC-2180.20
  capability_id_l1: BC-2180
  capability_name: Reserving Actuarial Management
  confidence: 0.75
  evidence: '"Bayesian actuarial models for insurance loss triangles"; "Loss triangles are uploaded and
    managed as first-class `triangle` resources. Development, tail, forecast and cashflow models are then
    fit against a named triangle."'
  reason: The tag manages loss-triangle resources that are the input to development/tail/forecast actuarial
    models — i.e. loss triangulation and reserving actuarial work. BC-2180.20 explicitly names "Loss triangulation,
    IBNR estimation". Some ambiguity remains because triangles also feed pricing models (BC-2180.10),
    so confidence is moderate.