Typewise for insurance

AI assistance in insurance service: where assistance ends and authority begins

Candidate assistance workflows for knowledge, intake and drafting. Declarations, claims decisions and advice stay with your people.

This page describes a deliberately narrow scope of candidate designs. It makes no claim about regulatory compliance and is not legal advice; your compliance and risk owners decide what may be processed where and what may go live.

Insurance service combines two things few other sectors do to the same degree: highly sensitive personal data under supervision, and customers who contact you at the worst moment of their year. A claim after an accident is not a billing question. Any AI scope has to respect both.

The conservative starting scope is assistance: helping staff find the current conditions when they change every year and colleagues turn over; preparing drafts a person checks; taking a claim intake in the order a stressed claimant can manage. Decisions, declarations and advice stay with people. Typewise agents read and write in connected CRM, ERP and ITSM systems, sensitive actions wait for approval, hand-offs carry the full context, and every action is logged. Whether any workflow below can run end to end in your organisation depends on what your systems expose and on the data behind them, so each is presented as a candidate design to evaluate, with what it needs, not as a promised capability.

Candidate workflows to evaluate

Assistance workflows to evaluate

Internal knowledge on policy conditions

Candidate design

Let staff retrieve the current general conditions and product information quickly, which matters when conditions change annually and new colleagues need to be reliable from week one.

What it needs from your systems

The conditions and product documents in a maintained source; a decision from your data-protection and risk owners on what may be processed where (a pilot can run on complex but non-confidential documents).

What stays with people

Which documents may be processed, and where, sits with your data-protection and risk owners.

Claim intake designed for the claimant

Candidate design

Guide the first notification of a claim in the order a person in a stressful situation can manage, collecting what is needed and acknowledging what happens next.

What it needs from your systems

An agreed intake structure per claim type and a destination in the claims system that handlers work in.

What stays with people

Coverage, liability and settlement decisions stay with claims handlers; the design structures the intake, it does not assess.

Drafts with disciplined review

Candidate design

Prepare replies to routine service requests for a person to check and send.

What it needs from your systems

A review design that is a real control: power-user trials, regression scenarios after knowledge or model changes, and spot checks on sent drafts.

What stays with people

Review is a control only if it is measured; a reviewer who copies and sends without checking is not a safeguard.

Channel learning

Candidate design

Show where customers leave a self-service or app path and explicitly ask for another channel, so you learn which concerns need a person without forcing them through an app.

What it needs from your systems

Instrumentation of the self-service path and the alternative channels, which is analytics work before it is AI work.

What stays with people

App-first does not mean app-only; the human channel stays open.

Operating boundaries

What the insurer keeps

  • Identity is verified by your systems, not asserted in the conversation: account data and account changes require an authenticated customer.
  • Every action the AI may take is granted per action as read, recommend, draft or execute, and enforced where the action happens, not only in the instructions.
  • Sensitive actions wait for a named approver; hand-offs carry the full context to a person who owns the next step, including outside service hours.
  • Every step is logged, so your team can audit what was looked up, what was proposed and what was done.
  • Declarations, claims decisions, advice and anything with legal effect stay with people; the AI does not act on a policy or a claim.
  • References are matched to data sensitivity: a sales-facing reference is not evidence for a regulated process; ask for the use case that matches yours.
How it is enforced

Permissions, approvals and an audit trail.

Agents read and write only in the systems and scopes you connect. Sensitive actions wait for approval, hand-offs carry the full context, and every action is logged. Hosting, access control and certifications are described on the security page.

Security and compliance →See how it works →
Rollout considerations

What a rollout has to take into account.

  1. Step 1

    Agree the data boundary first

    Decide with your risk owners what may be processed, where, and under which assessment, before a pilot. Non-sensitive but complex documents are a legitimate first test while cloud readiness is assessed.

  2. Step 2

    Use a legacy replacement as the redesign window

    If an ageing email or service system is being replaced, discovery can shape the future workflow rather than copying the old one into the new tool.

  3. Step 3

    Make review a measured control

    Track what reviewers change and sample what they did not, so “human review” is evidence rather than an assumption.

  4. Step 4

    Pair capacity with simplification

    Temporary hiring and process simplification can run together; an operating-excellence owner is a natural owner of the AI scope as well.

Good questions. Straight answers.

No. The scope on this page is assistance: knowledge retrieval for staff, structured intake and drafts a person checks. Coverage, liability, settlement and advice stay with your people.

A pilot on complex but non-confidential material, such as general conditions, is a reasonable first test. Which data may be processed where is your risk and data-protection owners’ decision.

Only if it is designed as a control: trials with power users, regression checks after changes, and sampling of sent drafts. A reviewer who copies and sends without checking is not a safeguard, and the design should make that visible.

Discuss a scope that fits your supervision.

Book a demo and we’ll walk through these candidate assistance workflows, the data boundary and the review design with your risk owners in the room.