The Supplement classifies AI by autonomy: support, augment, automate. Architecture classifies by certainty: deterministic, probabilistic, human. Governance is where the two meet. A probabilistic component inside a deterministic control structure is governable. A probabilistic component that is the control structure is a lawsuit with a timestamp.
The rule
Three territories, one design pattern.
Deterministic: same inputs, same output, every time, provably. This is about reproducibility, not about whether machine learning is involved. A trained model with fixed weights and a pinned version is deterministic at inference; a language model sampled at non-zero temperature is not. A GLM or gradient-boosted rating model that is filed, versioned, and locked belongs here. Anything that has to be filed, reconciled, reproduced for an examiner, or that moves money. Rating, binding, issuance, pro-rata math, billing, reserves, payouts, bordereaux. A rating algorithm that gives different premiums for identical inputs isn’t innovative. It’s unfileable.
Probabilistic: pattern work where 95% right at a tenth of the cost is a good trade because the failure mode is a correctable inefficiency. Extraction from unstructured documents, classification, triage, drafting, summarisation, anomaly flagging, similarity search.
Human: accountability joints. Adverse decisions, exceptions, anything irreversible, anything where the examiner will ask “who decided.”
The pattern that survives every regulatory regime: the model proposes, the rule disposes, the human owns the exceptions. The probabilistic component emits a score or a structured suggestion. A deterministic rule consumes it and routes. A human handles what the rule sends them. The score, the rule version, and the human action are all logged (Control 4).
The most common failure isn’t a bad model. It’s a model whose output nothing deterministic receives. That is Nolte’s architectural reading of what has been alleged in the nH Predict litigation, which is ongoing: not that a model was wrong, but that, as alleged, no mechanism existed to catch it being wrong.
The second most common failure is spending probability where the business needed certainty. A six-figure monthly inference bill on quote-bind-issue is what that looks like in production.
The map
For each workflow: the steps, the placement, the tier from Part 3, the Exhibit A operational area, and the failure mode if placement is wrong.
Submission and intake
| Step | Placement | Why |
|---|---|---|
| Receive submission (email, portal, ACORD form, broker PDF) | Deterministic ingest | Idempotent, logged, no interpretation |
| Extract fields from unstructured docs | Probabilistic | Pattern work; wrong field is caught downstream |
| Validate extracted fields against schema and business rules | Deterministic | Required fields, ranges, state eligibility |
| Human confirmation of low-confidence extractions | Human | Confidence threshold set in config (Control 3) |
| Appetite check and routing | Deterministic rule on the extracted, validated data | The rule can consume a model score, but the rule decides |
Tier: 1 (extraction) rising to 2 if the appetite routing consumes a model score that could decline a risk. Exhibit A: Underwriting/Eligibility, Producer Services. Failure if wrong: an LLM “deciding” a risk is out of appetite. That’s a declination with no rule to point at.
Quoting and rating
| Step | Placement | Why |
|---|---|---|
| Rating engine (factors, relativities, tiering, GLM outputs) | Deterministic, versioned, locked | Filed. Must reproduce. Same inputs, same premium, forever. |
| Quote assembly, forms selection, disclosures | Deterministic | Form applicability is rule-based per state |
| Quote explanation or summary for the customer or broker | Probabilistic, on top of deterministic outputs | Drafting from structured data; wrong wording is embarrassing, not unfileable |
| Referral decision (auto-quote vs refer to underwriter) | Deterministic rule, may consume a model score | The score is an input; the rule is the decision |
| Underwriter judgement on referrals | Human | Accountability joint |
Tier: 3 for the rating engine (automate, material financial impact, consumer impact). 1 for the summary. 2 for a model-scored referral rule. Exhibit A: Premium Quotes and Discounts, Ratemaking/Rate Classification. Failure if wrong: any probabilistic component between the validated inputs and the premium. It doesn’t matter how accurate it is; it cannot be filed.
Binding and issuance
| Step | Placement | Why |
|---|---|---|
| Bind eligibility checks (payment, signatures, KYC, state rules) | Deterministic | Compliance gate |
| Policy number, effective dates, document generation | Deterministic | Reproducible artifacts |
| Carrier notification, bordereaux row creation at bind | Deterministic | This is the data-model decision: structure it now, not at reporting time |
| Fraud or identity anomaly flag at bind | Probabilistic score → deterministic hold rule → human review | Model proposes, rule disposes |
Tier: 3 for bind itself. 2 for the fraud score if it can hold a bind. Exhibit A: Underwriting/Eligibility, Fraud. Failure if wrong: a probabilistic “should we bind” step. Binding is a contract formation event. It’s deterministic or it’s not binding.
Endorsements and mid-term changes
| Step | Placement | Why |
|---|---|---|
| Interpret the change request (email, call note, portal free text) | Probabilistic extraction to a structured change object | Pattern work; validated next |
| Validate the change against policy, product, and state rules | Deterministic | Which endorsements are allowed, when, with what forms |
| Re-rate and pro-rata premium calculation | Deterministic | Money math. Never probabilistic. |
| Adverse changes (coverage reduction, cancellation, non-renewal notice) | Deterministic rule → human approval | Adverse consumer outcome; human owns it |
| Endorsement document generation | Deterministic | Reproducible |
Tier: 1 for interpretation. 3 for re-rate. 3 for adverse changes. Exhibit A: Underwriting/Eligibility, Customer Service. Failure if wrong: the extraction silently changing a limit and the deterministic layer trusting it. Validation must be against the policy of record, not the extracted object.
Certificates of insurance
| Step | Placement | Why |
|---|---|---|
| Intake of COI request (broker email, portal, holder form) | Probabilistic extraction of holder, requirements, dates | Pattern work |
| Match requested coverages against policy of record | Deterministic | The certificate must reflect the policy exactly |
| Flag requests that exceed the policy (additional insured not on policy, limits above bound) | Deterministic rule → human or broker | Exceptions are where errors and E&O exposure live |
| Generate certificate | Deterministic templating from policy data | Reproducible, auditable |
| Renewal-driven reissue | Deterministic, scheduled |
Tier: 1 for intake. 2 for the whole flow because a wrong certificate is a misrepresentation to a third party. Exhibit A: Customer Service, Producer Services. Failure if wrong: an LLM drafting the certificate. The certificate is a statement of fact about a contract; it comes from the contract, not from a model’s reading of an email.
Claims
| Step | Placement | Why |
|---|---|---|
| FNOL intake (call, form, photos, documents) | Probabilistic extraction and classification | Pattern work; the biggest legitimate win in the stack |
| Coverage determination (is this loss covered under this policy, on this date, for this peril) | Deterministic against policy of record | Never a model. This is the contract. |
| Triage and routing (severity, complexity, fraud signal, assignment) | Probabilistic score → deterministic routing rule | The rule can be tuned; the score is logged |
| Reserve setting | Deterministic or actuarial method, versioned; model may propose | Financial statement impact (Exhibit C ref 11) |
| Adjudication: approve within authority | Deterministic rule on validated facts, possibly model-assisted | Automate only below a written authority limit |
| Adjudication: deny, partial, dispute | Human, model may summarise | Adverse consumer outcome; unfair claims settlement exposure |
| Payout calculation and payment | Deterministic | Money |
| Subrogation and salvage identification | Probabilistic flag → human | Recovery opportunity; wrong flag costs little |
| Customer communication drafting | Probabilistic draft → deterministic compliance check (required language, timelines) → human send for adverse | Timelines are statutory per state |
Tier: 3 for coverage determination, adjudication, reserves, payout. 2 for triage. 1 for FNOL extraction and comms drafting. Exhibit A: Claims/Adjudication. Failure if wrong: a model producing the coverage determination or the denial. This is exactly where the industry has already been sued, and exactly where the Supplement’s Exhibit C ref 12 points (unfair claims settlement laws).
Renewals, cancellations, non-renewals
| Step | Placement | Why |
|---|---|---|
| Renewal eligibility and re-rate | Deterministic | Filed |
| Retention risk scoring | Probabilistic | Marketing-adjacent; wrong score costs a call |
| Non-renewal or conditional renewal decision | Deterministic rule → human | Adverse; state notice periods are statutory |
| Notice generation and timing | Deterministic | Statutory timelines |
Tier: 3 for non-renewal. 1 for retention scoring. Exhibit A: Underwriting, Customer Service, Marketing.
Bordereaux and carrier reporting
| Step | Placement | Why |
|---|---|---|
| Row creation | Deterministic, at bind and at every transaction | Structure at the source |
| Reconciliation against premium and claims ledgers | Deterministic | Must balance |
| Anomaly detection across the file before submission | Probabilistic flag → human review | Catches what reconciliation doesn’t |
| Submission to carrier | Deterministic, versioned, receipted | Evidence of what was sent |
Tier: 3 (financial reporting impact). Exhibit A: Reinsurance, Legal/Compliance, Reserves. Failure if wrong: generating bordereaux from unstructured data with a model. Bordereaux is a data-model decision made at bind time, not a reporting feature bolted on after.
Billing and payments
Entirely deterministic except: dunning message drafting (probabilistic, compliance-checked) and payment fraud scoring (probabilistic → rule → human). Tier: 3 for money movement. Exhibit A: Other.
Customer service and producer support
| Step | Placement | Why |
|---|---|---|
| Answering policy questions from documents | Probabilistic, grounded in policy of record, with citations | Wrong answer costs a follow-up |
| Anything that sounds like a coverage confirmation | Deterministic lookup, or human | ”You’re covered” is a decision |
| Complaint intake and tagging | Probabilistic classification → deterministic tag to the AI system involved (checklist 3m) |
Tier: 1, rising to 2 the moment the bot confirms coverage. Exhibit A: Customer Service, Producer Services.
Reading the map
Count the cells. Probabilistic placements cluster at three points: intake (extraction, classification), scoring (triage, fraud, retention), and drafting (summaries, communications). Everything between those points and the money is deterministic. Everything adverse has a human at the end.
That is not a limitation on AI. It’s where the wins are. FNOL extraction, submission intake, and endorsement interpretation are where operators lose the most time, and they’re all Tier 1. The expensive part of the stack was never the part that needed a model.
Three tests for any proposed placement:
- Does the examiner need to reproduce this output? Deterministic.
- Does this output move money or form a contract? Deterministic.
- Is this output adverse to a consumer? Human at the end, deterministic rule before. If none apply, probabilistic is allowed, provided something deterministic receives the output and Control 4 logs it.
What we build this way to protect
In Nolte’s own delivery experience across a full MGA stack in all fifty states, the hard part was never the code. It was drawing this line and holding it under pressure from every vendor demo and every “just let the model handle it” suggestion. The rating engine was deterministic, versioned, locked. Quote-bind-issue was deterministic. Bordereaux was deterministic. The probabilistic tools lived at the intake edge where being wrong was cheap.
That line is what the Supplement now asks you to draw in writing. Exhibit A’s autonomy column, Exhibit C’s risk classification, and Exhibit B Q1g’s autonomy-and-reversibility question are all the same question: where did you allow the guessing, and what catches it.