Last Updated July 31, 2026
The short version:
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The most critical logic in insurance still lives in Excel. Capital models, product raters, and illustration engines are built there because it is where actuarial teams work fastest.
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Getting that logic into production is where teams stall. Recoding takes months or years, and deadlines, competitors, and product launches do not wait for it.
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Coherent Spark turns Excel models into enterprise-grade calculation services in a fraction of that time, with version control, automated regression testing, and full explainability built in rather than bolted on.
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Three insurers used this approach to cut a capital model's runtime from 8.5 hours to 2 minutes ahead of a regulatory deadline, compress an 18-month rater rebuild to 2 months, and launch a fully customizable life product with real-time pricing.
Across the global insurance industry, the most important business logic still lives in Excel. Capital models, product raters, and fully customized illustrations are built and validated there because Excel remains the fastest way for actuarial teams to iterate on complex logic.
The problem starts when that logic has to run in production.
The conventional path is a rebuild, recoding the model into a core system over months or years, at a cost that competes with everything else on the roadmap, and with ownership moving away from the people who understand the logic. Meanwhile the regulatory deadline, the product launch, or the competitor does not wait.
Coherent Spark exists to close that gap.
It gives insurers a way to run the logic they already trust in production in weeks, with the governance, testing, and integration that production demands built in from the start.
What is Coherent Spark?
Coherent Spark is the logic layer of Coherent's platform. It transforms governed Excel models into enterprise-grade calculation services, deployable as REST APIs to any connected system. The calculation engine is deterministic, meaning the same inputs always produce the same outputs, and every model carries Git-style version control with formula-level history, automated regression testing, and full explainability. Business teams keep building in Excel. IT gets a production asset it can govern, monitor, and integrate. Spark endpoints can also be called by AI agents via MCP, which makes governed spreadsheet logic available to agentic workflows under the same controls.
The three cases below span different markets, product lines, and business problems. The architecture that solved them is the same.
Converting a stochastic capital model for regulatory compliance
The problem
A global life insurer needed to convert a complex stochastic asset-liability model, used to run cost-of-guarantee calculations across 1,000 economic scenarios, to meet new international capital regulations.
The model relied heavily on VBA, which made documentation, testing, and governance difficult to evidence, and a full run took 8.5 hours. Recoding it into the company's legacy actuarial system would take months the team did not have.
The approach
The actuarial team tagged the model's inputs and outputs and uploaded it directly to Coherent Spark. Spark produced a fully functional, cloud-based API version of the model, with the visibility IT needed to apply governance and controls.
No developers were required and no logic was rewritten.
The results
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Runtime fell from 8.5 hours to 2 minutes, a 99.6% reduction
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Eliminated the VBA dependency, restoring transparency and testability across the model
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Externalized assumptions with automated data feeds, replacing manual updates
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Built-in governance and template-based reviews, giving the team defensible evidence for regulators
Why it worked
Coherent Spark digitized the model in the time the team actually had, and the deadline was met without a recoding project. The controls and transparency the regulation required came with deployment rather than as a second phase.
Modernizing product raters without an 18-month rebuild
The problem
The third-largest provider of group benefits maintained multiple product raters in Excel. The raters were critical for agent quoting but difficult to govern and scale, and the estimated cost to recode them was $3 million over 18 months. In a market with more than 60 competitors, that timeline put growth plans at risk.
The approach
The team uploaded its existing raters to Coherent Spark, which converted the spreadsheet logic into governed APIs that integrate directly with the provider's digital quoting platforms.
The raters stayed in Excel, under actuarial ownership, with every change versioned and testable before it reached production.
The results
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An 18-month program was delivered in 2 months, an 89% reduction in time to market
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The rebuild was avoided entirely, freeing more than $3 million in projected development cost for other priorities
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New products now reach agents faster in a heavily contested market
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Actuarial teams retained ownership of rater logic in Excel, with governed APIs downstream
Why it worked
Modernization did not require moving the logic away from the people who maintain it. Spark added the enterprise layer around the raters instead of replacing them, so the business gained governed integration without waiting on an IT backlog.
Launching a fully customizable life product with real-time pricing
The problem
A leading life insurer in India wanted to offer a policy customers could design themselves, selecting premium terms, payment schedules, and benefits individually. Supporting that level of customization would have required millions of pre-generated premium rate tables, which its legacy systems could not manage, and its operational processes were built for static products.
The approach
The insurer uploaded two Excel models to Coherent Spark. A benefit illustration model receives customer inputs from front-end systems, and a pricing model calculates premiums in real time based on the customer's selections. Spark deployed both as APIs and handled the data exchange between the front-end platforms and the legacy back end.
The results
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First to market with a fully customizable, non-participating life product
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Premiums calculated in real time from governed models, with no static rate tables to generate or maintain
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New business premium targets reached faster than any prior launch
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The same architecture now powers a second customizable product in the participating category
Why it worked
Real-time calculation replaced millions of static rate tables, which is what made the product possible at all. The actuarial team retained full ownership of the models, IT avoided a system overhaul, and the insurer moved from a rigid product-centric model to a customer-driven one without giving up control of the pricing logic.
Scaling what already works
Each insurer faced a different forcing function, from a regulatory deadline to a competitive market to a product innovation its systems could not support. The pattern underneath is the same. The logic that mattered already existed in Excel, refined by the teams who understand the products best, and the conventional path to production was too slow for the moment the business was in.
Coherent Spark supplied a faster one, and without the tradeoff a shortcut usually implies.
The model stays in Excel under the ownership of the team that built it. Deployment adds version control, regression testing, and audit evidence rather than stripping them away. And because the output is a standard REST API, the logic connects to quoting platforms, policy administration systems, and front ends the insurer already runs.
Insurers that build in Excel are not behind on modernization. The work ahead is deciding which models should scale, and not every one should.
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Coherent Insights maps the Excel estate to find the models that matter
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Coherent Control governs the logic that should stay in Excel
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Coherent Spark industrializes the logic that should scale
The three cases above show that pattern holding across capital modeling, group benefits quoting, and retail product innovation.
