Article

Captive pricing tightly links premiums, reserves, and the parent’s balance sheet—far more directly than in traditional carriers. It must simultaneously satisfy the owner, fronting partners, and regulators, ensuring premiums are defensible and arm’s‑length. The key decision is how much to rely on experience versus exposure rating, often blended, depending on data credibility and portfolio shifts.

Actuarial pricing building blocks in captives

For a captive, the actuary’s workflow looks familiar but with different constraints than a commercial book.

• Data and segmentation: Compile historical claim and exposure data by line, year, layer, and relevant risk segments (e.g., location, class, fleet, revenue band). In captives, data may be sparse or contaminated by prior program changes, so early effort goes into cleaning, on‑leveling, and normalizing for exposure and policy changes.
• Loss development and trend: Apply LDFs and trend factors to bring historical losses to an ultimate, current‑cost basis; for long‑tailed lines, this often dominates the indication and must be defended to reinsurers and regulators.
• Expected loss cost: Derive frequency and severity assumptions by segment; where data is credible, this will be heavily experience‑driven, but for thin segments the actuary leans on industry or exposure curves.
• Loads and capital: Add fixed and variable expenses (fronting, captive management, frictional costs), a risk load consistent with the captive’s risk appetite, and assess whether indicated premiums are sufficient to support target capital and rating/solvency metrics.
• Scenario and stress: Test pricing adequacy under adverse loss and exposure scenarios, particularly where the captive is supporting volatile layers or novel coverages.

That framework is agnostic to whether you are experience‑ or exposure‑rating; the distinction lives in how you estimate the expected loss component.

Experience rating: when your own loss history leads

In formal terms, experience rating is any system where the premium for a specific risk depends, at least in part, on that risk’s own historical loss experience. In practice for captives, this means using the captive’s or member’s loss history, adjusted for trend, development, as the primary driver of indicated loss costs, with credibility‑weighted blending to other sources as needed.

Key characteristics in a captive context:

• Data dependence: Works best where you have multiple years of reasonably stable exposure and enough claim count to pass basic credibility thresholds (e.g., WC, GL, auto for larger programs).
• Mechanics:
  • Compute historical loss ratios or loss costs by year and segment.
  • Adjust to ultimate and to current cost level.
  • Compare to expected (e.g., manual or benchmark) loss ratios.
  • Translate deviations into premium debits/credits, often via credibility formulas or prospective experience rating factors.
• Use cases:
  • Mature captives with stable operations and several accident years in the same structure.
  • Group captives adjusting member contributions based on each member’s own performance.
  • Loss‑sensitive plans (corridors, swings) where captive premium varies with actual losses.

Strengths:

• Directly reflects the captive’s unique risk management, safety culture, and claims practices—critical where those materially outperform industry.
• Provides strong behavioral signals internally; better loss performance quickly translates into lower required funding or more surplus distribution.

Limitations:

• Unstable when claim counts are low or when there have been major structural changes (acquisitions, new geographies, line expansion), which is common in captives.
• Can overreact to short‑term volatility if credibility is overstated; regulators, auditors, and reinsurers may challenge aggressive reductions not supported by broader benchmarks.

For example, a captive with five years of WC data and hundreds of claims per year can heavily experience‑rate base layers, while still blending to industry for rare, high‑severity excess events.

Exposure rating: when benchmarks and curves do the heavy lifting

Exposure rating calculates premium based on the risk’s exposure profile and external or portfolio‑level data, rather than the insured’s own loss history. The actuary uses exposure measures (payroll, revenues, vehicle count, TIV, limits) and external frequency/severity patterns, ILFs, excess loss factors, and size‑of‑loss curves to derive expected losses by layer.

Typical mechanics for captives:

• Map exposures: Build an exposure profile by line and layer (e.g., per‑occurrence limits, aggregates, attachment points, schedule of locations, TIV distribution).
• Apply exposure tools:
  • Liability: Use increased limits factors and exposure curves to scale from ground‑up loss costs to specific limit/attachment structures.
  • WC: Use excess loss factors to allocate expected losses between captive layer and reinsurance.​
  • Property: Use industry loss curves and pricing models based on TIV, geographic concentration, and PML expectancy
• Calibrate to benchmarks: Draw on industry studies, reinsurer manuals, or broader book data to set base loss costs and adjust for differences in risk control, occupancy, geography, etc.

Use cases in captives:

• New captives or new lines with little or no internal loss data (e.g., adding cyber, supply chain, or higher excess layers).
• Thin experience where individual‑captive data is not credible but comparable industry data exists.
• Pricing layers where the captive has never had a loss (e.g., CAT excess layers, shock‑loss casualty layers), so experience‑only methods are meaningless.

Strengths:

• Provides a coherent pricing framework even with sparse or zero captive‑specific losses, which is common in high layers and new programs.
• Naturally handles structural changes—limit profile, attachment shifts, portfolio mix—because it works off current exposures rather than past program design.

Limitations:

• Highly sensitive to the quality and relevance of external benchmarks; mis‑matched exposure curves or ILFs can produce systematic mispricing.
• Can miss captive‑specific improvements in risk management; you may overpay relative to your actual loss potential if you rely too heavily on market averages.

A typical example is pricing a captive’s first excess casualty layer: no historical captive losses exist at that attachment, so the actuary uses industry ILFs and exposure curves, then cross‑checks the exposure‑rated premium against lower‑layer experience and reinsurer quotes.

Blending exposure and experience in practice

In most captives, the right answer is not “experience or exposure” but a credibility‑weighted blend. The actuary will:

• Compute an experience‑based expected loss cost from captive data.
• Compute an exposure‑based expected loss cost from curves/benchmarks.
• Assign credibility to the experience indication based on volume, stability, and how comparable historical conditions are to the coming year.
• Blend to a final indicated loss cost, often with conservative bias when data is thin or the captive is adding new coverages.

For captives, additional overlays matter:

• Capital and rating: Regulators expect conservative pricing and robust rationale for deviations from market benchmarks; overly aggressive experience credits can create rating pressure.
• Transfer pricing and tax: Where premiums must be arm’s‑length, exposure rating and external comparables often play a larger formal role, with captive experience used as a support rather than the primary determinant.

For a risk‑savvy captive owner or independent consultant, the takeaway is that pricing methodology is itself a lever: you can push towards experience rating where your performance is genuinely superior and credible, and accept more exposure‑based conservatism where volatility, sparse data, or regulatory optics demand it.