Automating underwriting decisions is no longer a technical challenge for most carriers and MGAs. Extracting submission data, applying rating plans, checking appetite rules, and producing a quote can all be done by software. The harder question is organizational: who is willing to sign off on decisions a machine made?
The chief underwriting officer is accountable for the quality of the book. The chief risk officer is accountable for the risk the company takes on. Compliance is accountable to regulators for how decisions are made. For an MGA, the delegating carrier is watching every bound risk against the authority agreement. None of them will endorse automated underwriting on the strength of a demo.
What they will endorse is a control framework — one that makes automated decisions as governed, evidenced, and reviewable as decisions made by a human underwriter with a written authority letter.
Treat automation like an underwriter with an authority letter
Every human underwriter operates within explicit limits: lines, classes, territories, maximum limits, premium thresholds, and conditions that require referral. That same structure is the right starting point for automation.
Write a formal automated authority statement for each program or segment:
- Eligible business: lines, classes of business, territories, and coverage forms the automation may handle.
- Limits: maximum TIV, limit of liability, premium, and aggregate exposure.
- Data requirements: what information must be present and verified before automation may proceed.
- Mandatory referrals: conditions that always route to a human — prior losses above a threshold, certain occupancies, adverse third-party data, sanctions hits, any requested manuscript wording.
- Prohibited actions: what automation may never do — for example, declining without human review, deviating from filed rates, or binding outside the delegated authority from a carrier.
This document should be approved through the same governance process used for human authority, and reviewed on the same cycle. Executives sign off on the rules, not on the algorithm in the abstract.
Separate the decision layers
Automated underwriting is rarely a single decision. Breaking it into layers makes each one easier to control.
Layer 1: Data intake and validation
Submissions are extracted, cleaned, and validated against source documents and third-party data. Controls here focus on accuracy: confidence thresholds, mandatory human verification of low-confidence fields, and evidence links for every value.
Layer 2: Eligibility and appetite
Rules determine whether the risk is within appetite. These should be transparent, versioned rules — not opaque model outputs — so that underwriting leadership can read and approve them.
Layer 3: Risk assessment
Models may score risk quality or predict loss propensity. This is where explainability matters most. Each score should come with the factors driving it, and models should be validated before use and monitored afterward.
Layer 4: Pricing and terms
Filed rating plans are applied. Any judgment-based adjustments, such as schedule rating, stay within approved ranges, with the rationale recorded.
Layer 5: Decision and action
The system quotes, refers, or — where permitted — binds. Every decision references the version of every rule and model involved.
Layering means that when something goes wrong, you can identify which layer failed and fix it without shutting everything down.
Evidence executives can inspect
Sign-off depends on being able to see what the system did. For every automated decision, keep a decision record containing:
- The submission data used, with links to source documents
- The rules and model versions applied
- Scores and the factors behind them
- Which referral triggers were checked and their results
- The final decision and the terms offered
- Any human review or override, with the reviewer and reason
These records should be immutable and retrievable by policy number. When a regulator asks how a risk was underwritten, or a carrier auditing an MGA asks for files, the answer is a report, not an investigation.
Monitoring: the control that makes everything else credible
Approving the rules once isn’t enough. Executives need assurance that automation keeps behaving as intended. Build a monitoring pack reviewed monthly by underwriting leadership and quarterly by risk committees.
Portfolio measures
- Volume and mix of automated versus referred business
- Premium, limits, and aggregates written through automation, against authority
- Early loss indicators on automated business compared with similar human-underwritten business
Decision quality measures
- Referral rate, and whether referrals were appropriate
- Human override rate on referred cases, and reasons
- QA sampling results on a random selection of automated decisions
Model and data health
- Drift in input data distributions
- Drift in model scores
- Data quality exceptions and extraction confidence trends
Fairness and compliance
- Testing for unfair discrimination appropriate to the jurisdiction and line
- Complaints and regulatory inquiries involving automated decisions
Illustrative example: an MGA writing small commercial package business might automate quotes for a narrow class list under $25,000 premium. A monthly pack showing referral rates, QA results on a sample of automated quotes, and early loss frequency against the manually underwritten book gives both the MGA’s leadership and its capacity provider a clear view of whether to widen or tighten the authority.
Start narrow, widen with evidence
The most successful automation programs begin with a deliberately small scope: one line, a short list of classes, conservative limits, aggressive referral triggers. They run in shadow mode first — automation produces decisions that are compared with underwriters’ decisions but not used.
When agreement is consistently high and disagreements are understood, the program moves to live decisions with heavy QA sampling. Authority expands only when monitoring data supports it, and each expansion goes through the same approval process.
This progression gives executives what they need to sign off: not a promise that the system is accurate, but evidence, accumulated over time, that it behaves within agreed limits.
Align with recognized frameworks
Regulators and auditors increasingly expect insurers to manage AI under a documented governance program. The NAIC’s model bulletin on insurers’ use of AI systems, adopted by many US states, emphasizes governance, risk management, and documentation. Frameworks such as the NIST AI RMF and ISO/IEC 42001 provide structure for policies, roles, and controls. Mapping your automated underwriting controls to these frameworks makes executive sign-off easier because it shows the program is aligned with external expectations, not just internal preference.
The executive question, answered
When the CUO asks “can I stand behind this?”, the answer should be concrete: here is the authority it operates under, here are the referral rules, here is the evidence for every decision, here is how we monitor it, and here is how we pull it back if anything drifts. That’s automation executives can sign off on.
See how your underwriting controls stack up. The Aketud Underwriting Workflow Assessment reviews your submission flow, authority structure, and referral rules and identifies where automation can safely start — and what controls it needs.
