A vendor comparison guide for insurance pricing software

Insurance carriers face a widening performance gap between technology leaders and laggards, driven largely by how effectively they've modernized their pricing operations. The right platform selection directly impacts loss ratio improvement, underwriting speed, and the ability to replace fragmented spreadsheet workflows with governed, scalable alternatives. This guide maps eight vendors across three archetypes to help CUOs, actuaries, and transformation leaders match platform capabilities to their book complexity, quote volume, and strategic objectives.

What is insurance pricing software?

Insurance pricing software constitutes the operational layer that deploys, governs, and monitors pricing logic in production across lines of business and channels. According to the NAIC framework, these platforms support efficient and transparent ratemaking, often through predictive modeling techniques for rate determination. This differs from pure actuarial modeling tools, which provide broader analytical capabilities for capital adequacy, reserving, and enterprise risk assessment.

Modern pricing platforms have evolved into enterprise-grade systems that handle model building, rate deployment, and integration with policy administration systems as separate but connected components. Some tools focus narrowly on model building or rate optimization, while others operate as comprehensive underwriting decision platforms that span the entire pricing workflow.

For most insurers, the main competitor to any pricing software remains Excel and email. Any software investment ultimately aims to replace these fragmented spreadsheets with governed, scalable alternatives that can keep pace with market demands.

Three archetypes of insurance pricing solutions

The insurance pricing software market segments into three core archetypes based on use case and complexity. Understanding these categories helps narrow the field before evaluating individual vendors.

High-touch commercial pricing platforms

These platforms are purpose-built for complex commercial, specialty, and reinsurance lines where rich risk data and bespoke structures demand sophisticated tooling. They support end-to-end workflows spanning submission triage, actuarial modeling for rate development, underwriter interfaces for applying pricing, referral rules, and portfolio performance analytics. According to Celent's analysis, commercial and specialty lines rely more heavily on underwriter expertise and judgment compared to personal lines, where automated underwriting has seen greater success. This explains why these platforms emphasize flexibility and underwriter collaboration. hyperexponential represents this archetype.

High-volume personal lines and retail engines

These systems are optimized for mass-market auto, home, and retail-style decisioning where throughput matters more than customization. They emphasize real-time pricing, elasticity modeling, and omnichannel quoting capabilities. Novarica's research shows these platforms prioritize optimization at scale for high-volume personal lines where individual underwriter judgment plays a smaller role. Earnix and Radar fall into this category, though Radar also maintains Lloyd's market deployments.

Spreadsheet converters and Excel accelerators

These tools take a fundamentally different approach by focusing on rapidly converting existing Excel raters into web apps or APIs while preserving the underlying spreadsheet logic. Optalitix and Swallow represent this category. While the approach offers a faster path to digitization, a core limitation is worth noting upfront: the same effort required to migrate spreadsheets to a converter platform could instead complete a full transformation with a platform approach, positioning the carrier far better for the long term. This reframe applies throughout the spreadsheet converter category. Excel handles simple rating tables well enough, but breaks down as pricing structures, datasets, and regulatory requirements grow. Large workbooks become slow, error-prone, and difficult to govern, with limited auditability and heavy manual validation.

How eight vendors compare across lines, governance, and deployment

The following profiles examine each vendor's core capabilities, where they fall short, and which carrier profiles they serve best.

hyperexponential

hyperexponential operates as an underwriting decision platform that unifies three distinct operational stages: Submission Triage, Pricing & Rating, and Portfolio Intelligence. The platform is designed specifically for complex commercial, specialty, and reinsurance lines, and currently manages over $50 billion in GWP annually across 50+ global customers, including Aviva, Beazley, and Convex, with 100% customer retention. hyperexponential has a growing set of solutions to support underwriter decision-making including a composable underwriter UI in Pricing & Rating, Ingestion Agent, Underwriting Agent, and Submission Triage.

The platform delivers value across three interconnected pillars:

• Build better models faster. Actuaries use a Python-native environment where they can build, test, and deploy models 10x faster than legacy systems, with full version control and reusable components that eliminate redundant work.

• Accelerate the underwriting workflow. The Ingestion Agent ensures underwriters access relevant data at the point of pricing, which eliminates re-keying and reduces quote-to-bind time by up to 50%.

• Enable better pricing decisions. Automatic data capture records every action in the platform and surfaces it as input for portfolio analysis, benchmarks, and what-if analyses.

The platform incorporates a governed AI approach through an Actuarial Agent that accelerates model building while maintaining human oversight, addressing the growing demand for AI capabilities without sacrificing transparency.

The trade-off with hyperexponential is that the platform requires commitment to a Python-based approach. Teams without existing Python capabilities will need to invest in training, though hyperexponential provides structured learning paths and the Actuarial Agent can assist with Excel-to-Python conversion. Implementation typically requires more upfront investment than spreadsheet converters, though this investment enables capabilities that converter approaches cannot deliver.

Carriers and MGAs seeking a unified decision layer across triage, pricing, and portfolio intelligence with Python-native actuarial control will find hyperexponential well-suited to their needs, provided they're prepared for the platform commitment this approach requires.

Earnix

Earnix provides real-time pricing optimization and customer decisioning for high-volume personal lines and retail insurance. The platform has established proven scale in personal auto, home, and retail bancassurance, combining strong price elasticity modeling with demand-based optimization and an omnichannel rating engine built for throughput.

The platform has significant limitations when applied to commercial lines. These gaps become apparent when carriers attempt to use Earnix for risks requiring underwriter judgment:

• Earnix lacks the ability to build an underwriter-facing UI, which represents a major gap for complex commercial lines requiring underwriter oversight and judgment.

• The low-code approach severely limits customization and granularity, making it difficult to adapt the platform at the level of detail required for complex risk underwriting.

• Organizations that need to build underwriter front-ends according to their specific workflows cannot do so within Earnix.

These characteristics make Earnix well-suited for large personal lines carriers prioritizing price optimization at scale, particularly in environments where underwriter judgment plays a minimal role in the pricing process.

Radar

Radar operates as a configurable rating engine offered by Willis Towers Watson (WTW), with established presence in the Lloyd's and London market. The platform is optimized primarily for personal lines, though it maintains deployments across specialty segments. Its core value proposition centers on configurable rate tables and rules that don't require heavy development investment, and the recent Radar 5 release introduced generative AI capabilities.

The platform has notable limitations that prospective buyers should understand. These constraints affect both day-to-day usability and long-term flexibility:

• The UI is widely considered difficult to use by underwriters, lacking basic functionality like tabbing between cells that modern interfaces take for granted.

• Consultancy lock-in presents an ongoing concern: anything outside standard actions requires going back to WTW and paying consultancy fees for custom implementations, which means actuaries don't end up truly owning the tool they depend on.

• Models are largely built in proprietary languages not used anywhere else, which creates vendor dependency and limits transferable skills compared to Python-native alternatives.

Lloyd's syndicates seeking familiar tooling within the Duck Creek ecosystem will find Radar a reasonable starting point, though they should factor ongoing consultancy requirements and proprietary language constraints into their total cost of ownership calculations.

Akur8

Akur8 focuses on ML-driven rate optimization and transparent pricing models, primarily serving the personal lines market. According to Celent's assessment, the platform offers transparent AI features and automated GLM development designed explicitly for actuarial teams. The platform's core strength lies in showing actuaries exactly how models are built, which addresses growing regulatory and internal governance demands for explainability.

The platform's primary focus is model building rather than full deployment and governance, which shapes how it fits into a carrier's technology stack. Akur8 typically feeds downstream rating engines rather than serving as an end-to-end platform, so carriers should expect to pair it with other systems for deployment, underwriter workflows, and portfolio analytics.

Personal lines actuarial teams seeking ML-assisted model building with strong explainability will find that Akur8 addresses their specific needs well, particularly if they already have deployment infrastructure in place.

Quantee

Quantee positions itself as an early-stage, AI-native dynamic pricing platform for P&C and health insurance. According to Tracxn data, the company has raised $700,000 in seed funding, reflecting its newer entrant status. The platform emphasizes modern cloud architecture with rapid deployment and is designed for actuaries to iterate quickly on pricing models without the constraints of legacy systems.

As a newer entrant, Quantee has a smaller enterprise footprint and is less proven at scale for large, complex commercial portfolios. Portfolio intelligence and triage capabilities are less mature than those offered by established platforms, and carriers may need additional tools to achieve full underwriting workflow coverage.

Agile MGAs and smaller carriers that prioritize speed-to-market and modern architecture without legacy constraints will find Quantee worth evaluating, particularly if they're comfortable being an earlier adopter.

Lumnion

Lumnion targets pricing and rating specifically for SME and mid-market commercial insurance, occupying a middle ground between personal lines engines and complex commercial platforms. According to Tracxn data, the company has raised approximately $1.09 million in total funding. The platform is designed to handle higher-volume commercial and SME books with streamlined rating workflows suited to standardized products.

The trade-off for this mid-market focus is reduced flexibility. The platform offers less capability for highly bespoke commercial or specialty structures, and governance and portfolio analytics capabilities vary in depth. Lumnion may struggle to scale to full enterprise complexity for large multinational carriers, and its ecosystem integrations are narrower than those of more established platforms.

Swallow

Swallow specializes in converting Excel-based raters into managed, API-accessible applications. The platform enables rapid digitization of existing Excel pricing logic while preserving the familiar spreadsheet structure that actuaries already know, offering a lower initial investment than full platform transformation.

The limitations discussed in the spreadsheet converter category apply here. According to regulatory standards, including ASOP No. 56 and NAIC requirements, Excel-based systems require additional procedures and documentation for audit trails, version control, and governance. Because Swallow transfers the technical debt embedded in Excel rather than modernizing the underlying logic, these governance challenges persist even after conversion.

Carriers needing quick Excel digitization as a bridge solution during longer transformation initiatives may find Swallow useful for that interim period, but should plan for eventual platform migration rather than treating conversion as a destination.

Optalitix

Optalitix takes a similar approach to Swallow, focusing on turning Excel models into web applications and APIs with actuarial-focused tooling. The platform is purpose-built for actuarial Excel models and enables rapid conversion with minimal rework of existing logic, allowing organizations to expose Excel-based raters via APIs for integration without undertaking a full platform transformation.

The same category limitations apply: converting Excel models to web format does not remedy underlying governance deficiencies. The system remains built on an Excel foundation, which is unwieldy, difficult to edit, and rooted in VBA. Python remains superior for complex modeling, and the platform cannot surface captured data as an input for benchmarks and model iteration the way true platforms can. Governance typically lives outside the system in documents and email rather than being native to the platform.

Actuarial teams with heavy Excel investment seeking interim digitization will find that Optalitix addresses their immediate needs. Still, they should recognize that this path does not lead to long-term transformation.

Side-by-side vendor comparison

With these vendor profiles in mind, the next step is determining which platform archetype fits your specific situation.

How to select pricing software based on your book and team capabilities

Before evaluating specific vendors, you need to understand which organizational variables matter most for your situation. Four factors typically drive the decision:

• Complexity of risks. Bespoke commercial and specialty lines favor high-touch platforms that support underwriter judgment and provide flexibility for non-standard structures. Standardized personal and SME risks suit high-volume engines optimized for throughput rather than customization.

• Volume of quotes. Carriers processing millions of quotes annually need throughput-optimized engines that can handle scale without degradation. Those handling thousands of quotes with high judgment requirements benefit more from platforms that support underwriter workflows and provide pricing flexibility within actuarial guardrails.

• Geography and lines complexity. Multi-region, multi-line operations benefit from platforms with portfolio intelligence that can aggregate exposure and track performance across the entire book. Single-market operations with focused product lines may find that lighter solutions meet their needs without the overhead of enterprise platforms.

• Team skills. Code-based platforms unlock flexibility and reduce vendor dependency. Modern platforms like hyperexponential use Python with actuarial-specific libraries designed for pricing professionals, not software engineers, and include Python training as part of implementation. According to a reinsurance transformation case study, the learning curve is manageable due to Python's readable syntax. AI-assisted tools can convert existing Excel logic to Python automatically, so teams don't have to rebuild models from scratch or lose the institutional knowledge embedded in legacy spreadsheets. Excel-native teams should consider training investment and whether building Python capabilities represents a strategic priority for the organization.

Once you understand these variables, three evaluation criteria help distinguish between vendors.

Excel migration paths

Most carriers start from a common position: fragmented Excel raters scattered across regions and products that are difficult to control, audit, and update. The migration path you choose has significant long-term implications.

Direct conversion approaches like Optalitix and Swallow enable rapid digitization while preserving existing spreadsheet logic. These tools deliver quick wins but transfer technical debt rather than eliminating it, leaving you with an Excel foundation that doesn't scale.

Structured migration to platforms like hyperexponential takes a different approach, involving logic re-implementation in governed, version-controlled Python environments. For teams ready to migrate, hyperexponential's Actuarial Agent assists Excel-to-Python conversion while preserving existing logic. Guided migration paths help teams become productive within weeks. This path requires a higher initial investment but delivers long-term maintainability and unlocks capabilities like automatic data capture that spreadsheet converters fundamentally cannot provide.

Actuarial governance and model risk

Different archetypes handle governance in fundamentally different ways, which matters increasingly as regulatory scrutiny intensifies.

End-to-end platforms provide native version control, role-based access, approval workflows, and immutable audit logs that capture every change and override. High-volume engines tend to focus governance on deployment rules and access control, with varying levels of model transparency depending on the vendor. Spreadsheet converters commonly require external documents and email for governance rather than offering native platform capabilities, which creates gaps that auditors increasingly question.

According to Solvency II requirements and NAIC mandates, regulatory pressure increasingly demands demonstrable and comprehensive model governance built into the platform itself rather than bolted on through manual processes.

AI and automation capabilities

Vendors apply AI differently depending on their core focus. Understanding these distinctions helps match capabilities to needs:

• Submission triage uses AI-powered intake to extract, normalize, and prioritize submissions by appetite. This capability appears in platforms like hyperexponential, where it connects directly to downstream pricing and portfolio analytics.

• Model development acceleration uses AI assistants to help actuaries build models faster by automating routine tasks while maintaining human oversight.

• Decisioning automation uses ML-driven pricing optimization to automate rate-setting at scale. This is a strength of high-volume engines, though the trade-off often involves less transparency than actuaries require for regulatory compliance and internal governance.

The right combination of these capabilities depends on your specific operational profile.

Matching your situation to a platform archetype

These variables and criteria map to typical scenarios. The following patterns emerge from how carriers with different profiles select platforms:

• Complex specialty books with underwriting-driven decisions find hyperexponential well-suited to their needs, provided they can invest in the platform approach.

• Mass-market auto and home with high quote volumes align well with high-volume engines like Earnix.

• Simple rating logic needing quick digitization can work with spreadsheet converters as bridge solutions during broader transformation initiatives.

• Lloyd's syndicates seeking rating engine capabilities often start with WTW's Radar, factoring in ongoing consultancy requirements as part of their decision, but often move on to more sophisticated integrated platforms in future.

Whichever scenario matches your situation, the key is aligning platform capabilities with your operational reality rather than forcing a mismatch.

Compare your current stack to a unified approach

The performance gap between technology leaders and laggards continues widening. Top performers achieve 19.3% expense ratios while bottom performers struggle at 40.3%, according to McKinsey-LIMRA benchmarking.

Ready to assess hyperexponential as your underwriting decision platform, unifying pricing, triage, and portfolio intelligence? Request a tailored demo or run an assessment comparing your current technology stack against a consolidated hx platform deployment.

FAQs

How is hyperexponential different from Earnix?

hyperexponential is built for complex commercial and specialty lines, offering Python flexibility, transparent governance, and unified triage, pricing, and portfolio intelligence capabilities in a single platform. Earnix focuses on high-volume personal lines with ML-driven price optimization and lacks an underwriter-facing UI. The choice between them depends on risk complexity, volume profile, and whether underwriters need front-end tools for judgment-intensive decisions.

When are spreadsheet converters like Optalitix or Swallow appropriate?

Converters work best as bridge solutions during broader transformation initiatives when speed matters more than long-term architecture. They become constraints when pricing complexity grows, governance requirements tighten, or portfolio analytics become critical to competitive positioning.

Can modeling tools like Akur8 or Quantee replace a full pricing platform?

These tools excel at model optimization and rate building, but typically feed downstream systems rather than replacing comprehensive platforms. Most carriers use them alongside platforms that handle deployment, governance, underwriter workflows, and portfolio analytics, treating them as specialized components rather than complete solutions.