5 Biggest Mistakes in Insurance Software Projects

budget was spent, the system went live, the project was declared a success — and then, quietly, over the following years, the organisation discovers what it actually bought.

Not bad software, necessarily. Just software that made certain futures impossible.

Mistake #1: Buying for today’s products, not tomorrow’s ideas

Every core system evaluation tends to follow the same pattern: map the vendor’s features to your current product portfolio, run a gap analysis, and score the shortlist.

It feels rigorous — even methodical and data-driven.

The problem is that it optimizes for what already exists and unintentionally penalizes imagination.

The question that almost never gets asked during procurement is: how long would it take us to launch a product we haven’t even designed yet?

Not just a variation of an existing policy, but a truly new, innovative product. Think of parametric coverage triggered by weather data, a micro-policy embedded in a mobility app, or a usage-based commercial product priced by the hour. These may sound complex, but they’re no longer hypothetical — they’re already live in markets where your competitors are actively capturing premium.

The challenge is that procurement, by design, can’t effectively evaluate the unknown. You can’t score a vendor against a product that doesn’t yet exist. So committees default to optimizing for present-day fit — and call it due diligence.

The system goes live. It performs well. But two years later, when the product team introduces something genuinely new, the response is familiar: vendor project, change request, twelve to eighteen months of delay. By then, the opportunity has already passed.

The companies quietly pulling ahead aren’t always bigger or better funded. They’re the ones where actuaries and product managers can independently configure new calculation rules, coverage structures, and workflows — without submitting a ticket.

Mistake #2: Letting the vendor’s data model quietly become your business model

In many ways, this is an extension of the first mistake — and they share the same root cause.

When you choose a platform based primarily on how well it fits today’s product structures, you’re also — often without realizing it — buying into its assumptions about how insurance data should be organized.

This is the most insidious issue because it stays invisible until it suddenly becomes critical.

Every insurance platform comes with a predefined data model — a set of entities, relationships, and embedded assumptions about how insurance should work. Within 18 months of go-live, your processes, reporting, integrations, and even your team’s thinking have quietly adapted to fit that model.

Custom fields start to pile up. Workarounds turn into permanent, documented processes. New employees are taught “how the system works” instead of “how the business works,” because over time, the two have blurred into one.

No one plans for this — it simply happens when you don’t control the data model.

The real problem only surfaces when something genuinely new needs to be built.

And as mentioned earlier, these “new” categories are already here: parametric products, embedded micro-policies, usage-based commercial lines. It’s worth emphasizing this — specialty and niche insurance is no longer a fringe trend. It’s one of the fastest-growing segments, fueled by climate risk, digital distribution, and businesses whose needs don’t fit traditional policy structures.

MGAs are emerging around highly specific, single-risk categories — and capacity is following them.

This is exactly where the data model limitation becomes a serious constraint.

A new specialty product doesn’t just require updated pricing or wording — it often demands entirely new data structures. A different way to represent the insured entity. A new relationship between trigger and loss event. A data type the original system was never designed to handle.

On a flexible platform, this becomes a design discussion. On a rigid one, it turns into a multi-year transformation program — by which point the market opportunity is usually gone, or already claimed by someone else.

Mistake #3: Confusing “open API” with “genuinely open architecture”

Every modern insurance platform claims to have open APIs.

After all, it’s right there in the brochure.

What those brochures rarely explain is how deep that openness actually goes — whether it’s just a thin REST layer sitting on top of a locked, proprietary data model, or a truly composable architecture where your developers can interact with any business object freely, without vendor involvement.

“The real test of integration isn’t what you connected on day one. It’s how much effort it takes to connect something new on day 500.”

This distinction only becomes clear when you need to integrate something new: a fraud detection engine, a third-party telematics feed, or a claims AI solution that didn’t even exist when you signed the contract.

At that moment, “open API” either translates into a quick, straightforward configuration task — or a six-figure professional services engagement.

And the reality is: those outcomes are far from evenly distributed across vendors.

Mistake #4: Treating cloud SaaS as the default — without running the numbers

For most of the past decade, the financial logic was straightforward: avoid upfront capital expenditure, pay as you scale, and let someone else manage the infrastructure.

That argument made sense when cloud pricing was consistently declining and regulatory pressures were relatively light.

Today, neither of those assumptions reliably holds.

For insurers with mature and predictable workloads — which includes most established P&C and life portfolios — private cloud or on-premises deployment can often be more cost-effective over a five-year horizon. This becomes clear once you factor in per-seat fees, data egress costs, and the professional services needed to tailor a multi-tenant SaaS product to real business needs.

Let’s make this more tangible. A mid-sized insurer running a core platform with a major SaaS vendor might spend €80–150k annually on platform fees alone — before customization, integrations, or premium support tiers are even included. Now compare that with a dedicated server environment on something like Hetzner. Over five years, with a platform you fully own and control, the difference can easily fund an entire in-house development team.

“Cloud-native” and “SaaS” have become shorthand for modern and credible in vendor marketing. That’s not entirely wrong — but it’s also far from the full picture.

A platform that can be deployed cleanly on your own infrastructure, in a data center of your choosing, within a jurisdiction your regulators trust — and that you can scale and adapt on your own terms — is often a far more strategic asset than it first appears.

Mistake #5: Signing away data sovereignty without realizing it

Many enterprise software contracts have become highly sophisticated at one thing: making it expensive to leave.

That’s the essence of vendor lock-in — not through aggressive legal terms, but through deep architectural dependency.

Data export, for example, is often throttled, priced per record, restricted to proprietary formats, or simply unclear until you explicitly ask. By the time you consider moving away, your switching costs are already substantial.

The symptoms tend to show up gradually:

• You can’t easily run your own analytics on your own claims data without going through the vendor’s reporting layer
• A migration project that should take six months stretches to eighteen, because the data format is vendor-specific and opaque
• You enter renewal negotiations from a position of weakness, knowing that walking away is operationally painful and financially costly

Some insurers are now deliberately seeking platforms built on open-source foundations — where the data model is yours, the code is yours, and the exit path is real, not theoretical.

That’s not just a philosophical stance. It fundamentally shifts the balance of power in every vendor conversation.

What a different approach looks like

The issues described above all point to the same underlying problem: they stem from platforms that treat flexibility as a premium add-on, rather than a core foundation.

A configurable data model, genuine deployment freedom, and open-source code you truly own — these aren’t “nice-to-haves.” They are the essential characteristics of enterprise-grade software that make insurance product prototyping and deployment viable at scale.

This is the thinking behind **Openkoda** — an open-source-based insurtech platform designed specifically with these constraints in mind.

Every calculation rule, workflow, and data structure is configurable by design, so launching a new product becomes a product decision, not a vendor negotiation.

Because the platform is built on an open-source foundation with no proprietary lock-in, insurers retain full ownership of both their code and their data — and can deploy in any environment that actually aligns with their financial and regulatory requirements.

Closing thoughts

The decisions that shape an insurer’s technology future are rarely made at the dramatic moment — they’re made quietly, in procurement meetings, when the contract looks reasonable and the go-live date feels more urgent than the exit clause.

The cost of those decisions compounds slowly, and by the time it’s visible, it belongs to a different leadership team than the one that made them.

Building with flexibility from the start is the only way to keep future options genuinely open.