Launching New Insurance Products Faster with a Customizable PAS

Insurers need to launch products faster.

Many still can't. Legacy systems, slow change processes and expensive development mean that an idea for an innovative insurance product often waits months before anyone can sell it. The system usually decides how premiums are calculated and which channels a product can be sold through, and changing either is a project.

The bottleneck is rarely the idea. It's the distance between a product described in a document and a product that's configured, priced and ready to quote.

That's the gap Openkoda is built to close. Openkoda is a customizable policy administration system (PAS): a complete insurance platform that your team configures, often with AI, and extends in code only where it wants to.

Why Start From a Customizable PAS

Starting a new product on a blank codebase means rebuilding what every insurer already has: a policy data model, rating, quoting, documents, billing, claims, roles and an audit trail. Starting on a rigid off-the-shelf system avoids that, but then your product has to fit the vendor's template.

A customizable PAS sits between the two. The whole insurance lifecycle already runs, and products, pricing, rules, workflows, forms and documents are configuration rather than code. When something genuinely bespoke is needed, the platform can be extended on the same foundations.

You can work with Openkoda in two ways. Your own team can configure the platform, or you can bring in Openkoda's team, who offer custom insurance software development services. Every plan includes five complimentary days of configuration analysis, in which the team models your products, pricing, rules and workflows with you.

Ready-Made Modules You Start With

Openkoda groups its modules into two sets.

Build & configure

• AI Product Builder: describe a product or a change in plain English, review a typed before-and-after, and approve it.
• Rating Engine: multi-dimension factor tables, formulas, taxes, fees and commissions, tested on sample risks and rolled out by effective date.
• Workflow Engine: submission, quote, underwriting, issuance and renewal flows on a visual canvas, simulated before they go live.
• Forms & Distribution: branded quote-and-buy forms, embeddable widgets, client and broker portals, and a submission API on every form.

Run the lifecycle

• Policy Administration: quote, bind, issue, endorse, renew, cancel and reinstate, with a 360° view of every policy.
• Underwriting Dashboards: rules that auto-issue clean business, a shared referral queue and an AI underwriting copilot.
• Claims Processing: guided FNOL, configurable routing, reserves, payment approvals, recoveries and fraud flags.
• Billing & Payments: payment plans, automated dunning, card payments and commissions.
• Document Generator: branded templates with merge fields, plus AI that reads inbound documents for a person to confirm.
• Reporting & Dashboards: plain-English questions about your book, and dashboards per role.
• Bordereaux Reporting: premium and claims bordereaux on each program's schedule, in Excel or CSV.
• Team Collaboration: chat, tasks and notifications attached to the record.

A few things run across all of them:

• Versioning. A product moves from Draft to Current to Superseded, and policies in force keep the version they were bound under.
• Standards and security. An ACORD-aligned data model, strict tenant isolation, role-based access and an automatic audit trail.
• Integration. REST APIs, webhooks, per-form submission APIs and an MCP server that lets assistants such as Claude work with the platform, with every call permission-scoped and audited.
• Ownership. Openkoda Managed Cloud or on-premise, with your configuration and data exportable at any time.

Example: Setting Up a New Insurance Product With the AI Product Builder

Say you're launching pet insurance. Here's how it goes from a requirements document to a configured product.

1. Write the requirements the way you already do. An ordinary Word document covering what you insure (the animal's species, age and whether it's microchipped), the covers and their limits, the pricing, the underwriting rules, and what should happen when an application arrives. One tip: put facts in tables, and give every age band an explicit from and to.

2. Hand it to the AI Product Builder. Open an empty product, attach the document and add one line of instruction, such as "configure this product from the attached requirements, and ask each question on the quote form only once."

3. Read the proposal before anything changes. You get a summary, then one row per change, showing what the product says today and what it would say after. Changes the builder can't apply yet are greyed out with a reason, so nothing fails silently.

4. Apply what you agree with. Untick anything you disagree with and apply the rest. In Openkoda's walkthrough, a single pass configured the risk item, three covers, the rating tables and formula, the quote form, two underwriting rules and the workflow.

5. Check the result component by component.

• Compare the rating tables with your document cell by cell, and check that the age bands have no gaps or overlaps.
• Price a test quote and do the arithmetic once. For a five-year-old dog on Essential cover with dental: 240 × 1.00 × 1.00, plus a 12% dental loading, gives 268.80. Add a 25.00 fee and 12% tax on 293.80, and the total is 329.06.
• Test the underwriting rule boundaries. If the document says "decline anything older than twelve", a twelve-year-old should still get a quote.
• Follow each branch of the workflow (quote, refer, decline) to an end.

The AI takes care of transcription, not review. What used to be a week of manual setup becomes an hour of checking.

Later changes work the same way. An instruction like "add windscreen cover to Motor and re-rate it" becomes a reviewable change-set. Applying it creates a new product version, so existing policies aren't affected.

Here's the whole process on video:

What You Can Launch on Openkoda

The same platform supports very different products and operating models:

• Embedded Insurance: embed quote-and-buy in a partner's checkout, priced in real time from the partner's data.
• MGA Programs: run each binder with its own authority limits, commissions and carrier-ready bordereaux.
• P&C Products: run personal, commercial and specialty lines on one core and one data model.
• New Product Launches: pilot a product with real customers, then scale what works without re-platforming.
• Legacy Modernization: move one line of business at a time while the old system keeps running.

To see every step of the pet insurance example in detail, read the full tutorial:

How to Configure a New Insurance Product With Openkoda AI Product Builder

Comments

[Mohsin Majestic]

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