Building an insurance illustration engine — the edge cases that break most validators

Insurance illustrations are, in the regulatory imagination, simple: a vendor produces a projected policy ledger; a consumer (or their advisor) reviews it; the ledger reflects the policy's actual mechanics; the consumer makes an informed decision. In engineering practice, illustrations are the single most edge-case-dense calculation in personal finance — they encode policy mechanics, tax mechanics, mortality and lapse assumptions, regulatory non-guaranteed-element rules, and cash-value behavior across decades of compounding, against a population of policyholders whose underwriting status, behavior, and tax situation interact non-linearly with all of the above.

Illustration engines are the systems that produce these projections. They live inside carriers, brokerage platforms, advisor tools, and increasingly inside InsurTech startups trying to disintermediate the traditional sales process. Most of them work fine for the median case and break, often silently, on the edge cases. This guide is for the engineers and product teams building those engines — and for the validators trying to certify them before they reach a consumer.

The regulatory baseline — NAIC and AG-49

Insurance illustrations in the U.S. operate under the NAIC Life Insurance Illustrations Model Regulation, with state-by-state adoptions that add their own variations. The model regulation prescribes what an illustration must include (guaranteed and non-guaranteed values, mid-point assumptions where applicable), what marketing language is permitted, and how the illustration must be reconciled to actual policy performance over time.

For indexed universal life (IUL) — the product class that has produced the most regulatory attention in the past decade — Actuarial Guideline 49 (AG-49) and its 2020 successor AG-49-A constrain the assumptions an illustration may use. AG-49-A specifically restricts the credited rates an IUL illustration may project, caps the multiplier and bonus assumptions, and requires the illustrated rate to bear a documented relationship to the historical performance of the underlying index.

An illustration engine operating on IUL products must:

• Compute the maximum illustrated rate per AG-49-A's lookback methodology
• Restrict the bonus assumption per the AG-49-A multiplier cap
• Reconcile illustrated cash values against the constraint set every time the illustration is regenerated
• Produce the disclosure language the model regulation requires

A surprising number of in-market illustration engines have been found, on examination, to be non-compliant with AG-49-A — not because the engineers didn't intend to comply, but because the compliance is conditional on the underlying index, the rider configuration, the policyholder's payment timing, and the carrier's specific filed product, and engines that hard-code one carrier's assumptions tend to fail when applied to another carrier's products.

What your data model has to capture

An illustration engine's input space is large. Per policy, the engine must accept:

A defensible illustration engine carries each of these in its policy record and produces an audit trail per illustration showing the inputs that produced the output. Engines that abstract this into a "preferred non-smoker, age 35, $1M face" parameter set can illustrate the median case but fail on any policy whose specifics deviate.

The edge cases that break most engines

Across our review of illustration engines and the validators that test them, the same edge-case categories recur:

Substandard underwriting with rider stacking. A policy issued at Table 4 with a long-term care rider, a waiver-of-premium rider, and an accelerated death benefit. The COI charges on the base policy are non-standard. The rider charges are also non-standard. The interaction effects (the LTC rider's accelerated benefit reduces the death benefit, which changes the COI calculation, which changes the cash value, which changes the LTC rider's available benefit) are non-trivial. Engines that compute COI and rider charges in parallel rather than as a coupled system produce illustrations that don't reconcile to the carrier's actual policy mechanics.

Modified Endowment Contract crossings. A policy that is a non-MEC at issuance but, due to additional premium payments, becomes a MEC mid-life. The illustration must show pre-MEC and post-MEC tax treatment correctly, and must handle the §7702A "material change" test that resets the seven-pay calculation. Engines that don't track the running seven-pay calculation produce illustrations with incorrect tax assumptions from the MEC-crossing point forward.

Tax-free cash value loans that become taxable. A policy where the policyholder has been taking tax-free loans for income, the policy lapses, and the outstanding loan balance becomes taxable as a §72(e) distribution. The illustration must show the cliff-tax event correctly. Most illustration engines we've reviewed cannot represent the lapse-and-loan-becomes-taxable cliff at all.

Indexed crediting with multi-segment averaging. An IUL policy with multi-segment crediting where each premium establishes a new index segment with its own start date and crediting basis. The illustration must compute the weighted-average crediting per anniversary, accounting for partial-year segments, segment maturity, and segment renewal at potentially-different cap and participation rates.

Premium-funded LTC rider conversions. Hybrid life-LTC products where premium funds both the death benefit and the LTC rider. When the LTC rider triggers, the death benefit is accelerated, the cash value is consumed, and the projected cash-value sustainment changes. Illustrations need to show both the no-LTC-trigger and LTC-triggered scenarios, with reconciliation between them.

Special-needs dependent annuity projections. A policy or annuity intended to fund a special-needs trust. The projection must reflect that the beneficiary's needs persist for the beneficiary's lifetime, that the funding source has specific tax characterizations, and that distributions interact with the beneficiary's means-tested benefits. Engines designed for general annuity projections often have no concept of these constraints.

Section 7702 compliance under the new (2021) interest rate environment. The 2021 Consolidated Appropriations Act changed the §7702 interest-rate assumptions, which changed the maximum premium that a non-MEC policy can accept. Policies issued before the change have grandfathered limits; policies issued after have new limits. Illustration engines that haven't updated to handle the new §7702 regime produce non-compliant projections.

Collateral-assigned or split-dollar arrangements. A policy held by an individual but collateral-assigned to a business under a split-dollar arrangement. The illustration must account for the imputed-income tax treatment under the split-dollar regulations, the ownership transfer at termination, and the changed cost basis post-transfer.

The validation problem

The structural challenge in illustration engine validation is that the test cases that matter are precisely the cases that are rare in production. A general-population sample drawn from a carrier's in-force book under-represents substandard issues, MEC crossings, special-funding arrangements, and the other edge cases above. Validating an engine against such a sample produces high apparent accuracy and quietly fails on the edge cases.

The right approach is a synthetic test corpus deliberately seeded with edge cases at the rates required to validate the engine's behavior on each.

Our InsurTech Illustration Validator is a 240-household pack purpose-built for exactly this validation problem. Each household has the underwriting status, structural complexity, and behavioral pattern required to exercise an illustration engine on a specific edge case category. The pack covers substandard underwriting, dependent special-needs, blended-family ownership structures, and recent claims history — and the methodology PDF documents how each test case maps to the AG-49-A and §7702 compliance requirements an illustration must satisfy.

What examiners and reinsurers actually ask

Illustration engines are validated by carriers' internal actuarial and compliance teams, by state insurance department examiners, and by reinsurers reviewing the carriers' practices. Across the three audiences, the questions cluster:

A defensible illustration engine produces documentation answering each of these questions automatically. An engine that requires human assembly of the answer post-hoc fails the practical validation test, even if the underlying numbers are correct.

A defensible build sequence

1. 1
   Lock down the policy data model first
   Underwriting class, rider catalog, charge tables, crediting parameters, tax-compliance flags, behavioral assumptions.
2. 2
   Build the §7702 / §7702A compliance layer
   The tax-compliance overlay that determines what the engine is allowed to illustrate. Runs on every policy regardless of product type.
3. 3
   Build the AG-49-A compliance layer for IUL
   Crediting-rate constraint, multiplier cap, lookback methodology.
4. 4
   Build the per-product crediting engine
   WL dividend, IUL crediting, VUL subaccount projection, traditional UL declared rate. Each product has its own mechanics.
5. 5
   Build the charge engine
   COI, M&E, expense, rider, surrender. Consumes the policy data model and rating class to produce per-period ledger.
6. 6
   Build the projection engine
   Combine premium inflows, charges, and crediting. Handle MEC crossings, lapse events, and rider triggers explicitly.
7. 7
   Build the illustration formatter
   Generate the output document with the disclosures the model regulation requires. The formatter is the consumer-visible artifact regulators read.
8. 8
   Build the validation harness
   Synthetic corpus + expected-output verification + regression testing across product changes. Most teams build this last; we'd argue it should be built second.

Closing

Insurance illustration engines are a deceptively complex software domain. The mechanics of any individual policy are tractable; the combinatorial complexity across underwriting, riders, tax compliance, and behavioral assumptions is what causes engines to fail in production. Validating an engine against a population that doesn't include the structurally hard cases produces an engine that works fine until the day a substandard hybrid LTC policy hits the market.

The synthetic-data approach to validation is, in our experience, the only practical path. Real in-force data under-represents the edge cases that matter; randomly generated data has no policy coherence and fails to exercise the rule logic at all. A purposefully constructed corpus that seeds each edge-case category at the rate required to validate the engine — that's what produces an engine the actuarial team, the compliance team, and the reinsurer all sign off on.

The InsurTech Illustration Validator is the corpus we'd test against. The free sample on GitHub lets you inspect the schema and the household structure before you commit to anything.

Related reading:

• InsurTech Illustration Validator (Data Set)
• Healthcare Benefits & HSA Pack
• Insurance Claims & Cybersecurity Risk Pack
• Pre-Launch Synthetic-Data Fidelity QA Checklist

This document is general guidance for engineering teams building illustration software. It is not actuarial or legal advice. Carriers and InsurTech firms operating in this space must engage qualified actuarial and compliance counsel for product-specific validation.