Reducing Insurance Product Development Time and Cost With AI-Ready PAS

Why a Customizable, AI-Enabled PAS Beats a Blank Canvas

Let's say you've got an idea for a new insurance product.

Maybe it's a usage-based auto policy priced on telematics data, a parametric travel product that pays out automatically when a flight is delayed, or cyber cover built for small businesses.

All of these can be good business. The hard part is getting them live while the opportunity is still there.

Rigid legacy cores turn every change into a project. A new rating factor goes into the vendor's queue, a new type of insured object needs a data model change, and a new distribution partner needs an integration. Building from scratch avoids those constraints, but it takes a long time and a large budget.

Over the last few years, many teams have tried a third route: low-code application platforms. They make building screens, forms and workflows much faster. But there's a catch that's easy to miss. A low-code platform helps you build an insurance system faster, but you still have to build the insurance system. The policy lifecycle, rating, endorsements, billing, claims, documents and carrier reporting remain your project.

There's a fourth starting point that has become much more practical: a customizable, AI-enabled policy administration system (PAS). Instead of starting from a toolkit, you start from a working insurance core and reshape it.

Here's how the four options compare:

Classic Insurance Product Development

Building an insurance system from scratch typically takes 12 to 24 months, with several teams working in parallel. It usually runs like this:

• Discovery and requirements (1-3 months): market research, regulatory analysis and defining what the system must do.
• Core development (3-6 months): building policy, rating, underwriting, billing and claims, plus integrations with existing systems.
• Security and compliance (2-4 months): access control, audit trails, data protection and regulatory reporting.
• Front end and channels (2-4 months): customer portals, broker screens, quote forms and partner APIs.
• Testing and stabilization (3-6 months): integration testing, performance tuning and fixing what only shows up with real data.

Low-code shortens some of these phases, but it doesn't remove them. The insurance-specific logic still has to be designed and built on top of generic components, and someone has to maintain it afterwards.

Product Development on a Customizable PAS

With a customizable PAS, the core phases above already exist on day one. The project becomes configuring products, connecting systems and testing with real data.

Take the parametric flight-delay product. On Openkoda, it could come together like this:

• Configure the product with AI. Write the requirements the way you normally would: the insured trip, the delay threshold, payout amounts, pricing and eligibility. Hand the document to the AI Product Builder, which proposes the risk attributes, covers, rating tables, underwriting rules, quote form and workflow as a change-set you review before anything is applied.
• Price it in the rating engine. Rating tables and formulas live in a visual editor, are tested on sample trips and roll out by effective date.
• Automate the payout. A flight-status feed sends delay events to Openkoda's REST API. A workflow triggered by the event checks the policy, opens the claim and issues the payment, with every run logged.
• Sell where travelers book. Publish the quote form as an embeddable widget on a travel booking site, or let the partner call the form's submission API. The price is calculated in real time and payment is taken in the same flow.
• Service it digitally. Policy documents and emails come from templates your team edits, and customers see their policy and claims in the client portal.

After launch, changes follow the same path. Lower the delay threshold, add missed-connection cover or adjust pricing for a route: describe the change, review it, test it and publish a new version. Policies already sold keep the terms they were bound under.

Where AI Fits, and Where It Doesn't

AI in Openkoda follows one rule: it drafts, and a person approves.

• AI Product Builder turns plain-English briefs and requirements documents into typed change-sets drawn from a fixed catalogue of operations, never free-form code.
• Reporting AI answers questions about your book in plain English and saves the answer as a report you can put on a dashboard.
• Underwriting copilot summarises referred cases and highlights the factors that matter. The underwriter makes the call.
• AI document reading extracts details from submission attachments for a person to confirm.
• MCP server lets assistants such as Claude work with the platform, limited to the user's own permissions, with every action confirmed and audited.

For a regulated business, that design matters. Faster configuration is only useful if every change can be reviewed, versioned and explained afterwards.

Here's the AI Product Builder configuring a product from a requirements document:

What's Already in the Box

The classic argument for development platforms was that some features are common to every enterprise insurance application: user management, security, audit trails, dashboards and integrations. You shouldn't have to build them. That's still true, but a customizable PAS goes one step further. The insurance logic itself is already there.

• Build and configure: AI Product Builder, Rating Engine, a Workflow Engine with simulation before go-live, and Forms & Distribution with branded forms, embeddable widgets, portals and submission APIs.
• Run the lifecycle: Policy Administration, Underwriting, Claims, Billing & Payments, Document Generator, Reporting & Dashboards, Bordereaux Reporting and Team Collaboration.
• Across the platform: product versioning, strict data isolation, field-level role-based access, a full audit trail, an ACORD-aligned data model, REST APIs, webhooks and an MCP server, on managed cloud or on-premise.

Customization doesn't stop at configuration. When a product needs something the platform doesn't do out of the box, such as a proprietary pricing model, it can be built as an extension on the same foundations, by your developers or by Openkoda's team.

Best Use Cases

• New product launches: pilot a product with real customers, then scale it without re-platforming.
• Embedded insurance: sell inside partner checkouts, priced in real time from the partner's data.
• MGA programs: run each binder with its own authority limits, commissions and carrier-ready bordereaux.
• Legacy modernization: run alongside the old core and move one line of business at a time.

Your Bottom Line: Start From a Working Core

A customizable, AI-enabled PAS changes the starting point. Instead of building the core faster, you start with one that already works and spend your time on what makes your product different.

That's where development time and cost actually come down. Nobody rebuilds policy administration, claims and billing. The people who own the product make the changes. And every change is versioned and auditable.

Whether you're launching a new product, setting up claims management or policy management, opening embedded channels or modernizing a legacy system, starting from a customizable PAS lets you move quickly without boxing yourself in.