Akur8 vs Earnix (2026)
Two insurance pricing platforms graded level on the model itself and far apart on what surrounds it. Both hold B on AI centrality, A on institution coverage, A on outcome evidence and B on integration, and both keep a human gate between a model and a live rate. The difference is regulatory. Akur8 lives inside the rate filing regime: every model it produces is signed by a certified actuary and filed with a regulator, which earns A on model risk and A on regulatory standing, and it treats constraint on pricing variables as a product feature, which lifts governance and bias to B. Earnix is broader, running pricing, rating, personalisation and engagement for insurers and price optimisation and credit decisioning for banks from one platform, and it sits at C on governance and bias in exactly the practice consumer fairness rules target hardest, price optimisation. Akur8 also publishes how it charges and names the scope of its attestation; Earnix does neither.
- Your models go to a regulator as rate filings. Akur8's rate repository is built for regulatory submission, and every model is signed by a certified actuary under professional obligation and filed with a supervisor who can reject it.
- Actuaries must be able to read every coefficient. Transparent Machine Learning automates feature engineering, variable selection and geospatial smoothing while keeping each variable's contribution visible, and the vendor states models are built up to ten times faster than by hand.
- You want to know how you will be charged. Akur8 prices on the volume of premium modelled on the platform rather than per user, so putting more of the actuarial team on the tool costs nothing extra.
- Reserving is in scope. The reserving product came from Milliman as Arius and replaces spreadsheet reserve analysis, with Milliman retaining equity.
- You price across insurance and lending. Earnix runs dynamic pricing and rating for insurers and price optimisation across loans, cards, auto finance and mortgages for banks, with Lending Plus adding automated credit decisions, from one platform.
- Speed to market is the constraint. Warta, among Poland's largest insurers, deploys prices within hours, and Domestic and General reports pricing 40 times more plans per month.
- You run Guidewire PolicyCenter. Co-operators externalised its pricing and rating into Earnix integrated with PolicyCenter, a named live deployment rather than a partner logo.
- You want pricing and personalisation together. Earnix sells product personalisation and real time next best action across service and distribution channels alongside the rating engine, extended by its acquisition of the generative AI specialist Zelros.
This comparison is published by AI FinTech Index, an independent research platform that publishes independent ratings of AI vendors for financial services. Akur8 and Earnix are each graded against the same capability taxonomy, from each vendor's own public materials and the regulatory record, under the AI FinTech Index verification standard. No vendor pays for placement, and no vendor has reviewed this page. How this evidence is graded
Plain facts
| Akur8 | Earnix | |
|---|---|---|
| Primary category | Insurance AI | Insurance AI |
| Founded | 2018 | 2001 |
| Headquarters | Paris, France | Boston, United States |
| Website | www.akur8.com | earnix.com |
Side by Side
| Axis | A Akur8 |
E Earnix |
|---|---|---|
| AI Centrality | ||
| Autonomy and Oversight Model | ||
| Model Risk Management and Transparency | ||
| Operational and Outcome Evidence | ||
| AI Safety and Data Stewardship | ||
| GLBA and Data Privacy Posture | ||
| Security Certifications and Trust Center | ||
| Regulatory Status and Licensure | ||
| AI Governance and Bias Disclosure | ||
| AI Liability and Recourse | ||
| Model Supply Chain Disclosure | ||
| Core Systems and Integration Depth | ||
| Deployment Model and Data Residency | ||
| Commercial Transparency | ||
| Institution and Segment Coverage |
The short version of each
Akur8
Akur8 is an actuarial platform for non life insurance pricing and reserving built on what it calls Transparent Machine Learning, automating feature engineering and risk model construction while keeping every variable contribution visible to the actuary, with a rate repository and deployment engine carrying approved rates into production. The AI FinTech Index records it at A on model risk management and regulatory standing, because every model is signed by a certified actuary and filed with a regulator, and at A on coverage and outcome evidence for roughly 330 customers in more than 40 countries including AXA, Generali, Munich Re, MAPFRE and Tokio Marine. The index records a published pricing basis on premium modelled rather than seats, and records the gaps: no residency commitment on its single named cloud and no published data processing terms.
Source: AI FinTech Index, 2026
Earnix
Earnix is a pricing, rating and decisioning platform founded in 2001 and run from Boston, serving more than 80 insurers, banks and lenders with dynamic pricing, an enterprise rating engine, personalisation and engagement for insurers and price optimisation and credit decisioning for lenders. The AI FinTech Index records it at A on institution coverage and outcome evidence, the last on named results including Domestic and General pricing 40 times more plans per month and Warta deploying prices within hours, and at B on integration for a named Guidewire PolicyCenter deployment at Co-operators. The index records the gaps: no fairness testing on price optimisation, the practice consumer fairness rules target most, no statement on pooled data across competing carriers, no model named after the Zelros acquisition, and no security certification found.
Source: AI FinTech Index, 2026
Common questions
Is Akur8 or Earnix better for insurance pricing?
They grade level on the model and apart on regulation. Akur8 holds A on model risk and regulatory standing because every model is signed by a certified actuary and filed with a regulator, and B on governance and bias for treating constraint as a product feature. Earnix is broader, covering insurance and bank lending from one platform with personalisation and engagement, and holds C on governance and bias. Both hold A on coverage and outcome evidence. Graded by AI FinTech Index against the same capability axes from each vendor's own published materials, verified September 21, 2026. No vendor pays for placement.
Which pricing platform is easier to defend to a regulator?
Akur8, on the public record. Its outputs are built to be legible coefficient by coefficient, its rate repository is designed for regulatory submission, and it states that unexplainable outputs carry legal risk. Earnix sells model governance and a filing accelerator as product features but publishes nothing about testing its own models, which holds model risk at B and regulatory standing at C. Graded by AI FinTech Index against the same capability axes from each vendor's own published materials, verified September 21, 2026. No vendor pays for placement.
Do Akur8 and Earnix test pricing for fairness?
Akur8 publishes work on constraining pricing variables and treats that constraint as a product feature, which lifts it to B, although explainability alone does not show whether outcomes fall unevenly across groups. Earnix publishes no fairness testing, protected characteristic handling or disparate impact analysis, which is C and its most consequential gap given that price optimisation is the practice fairness rules target hardest. Graded by AI FinTech Index against the same capability axes from each vendor's own published materials, verified September 21, 2026. No vendor pays for placement.
How do Akur8 and Earnix charge?
Akur8 discloses its basis: pricing follows the volume of premium modelled on the platform, not the number of users. Earnix publishes no pricing, and its licensing is described only by third parties as an enterprise subscription scaling with modules, policy volume and deployment size. Graded by AI FinTech Index against the same capability axes from each vendor's own published materials, verified September 21, 2026. No vendor pays for placement.
Can either platform be used by banks as well as insurers?
Earnix can: it sells price optimisation across unsecured loans, cards, auto finance and mortgages, with Lending Plus combining pricing analytics with automated credit decisions. Akur8 is built for non life insurance pricing and reserving, serving carriers, reinsurers, mutuals and managing general agents. Graded by AI FinTech Index against the same capability axes from each vendor's own published materials, verified September 21, 2026. No vendor pays for placement.
How does the AI FinTech Index grade Akur8 and Earnix?
Both are graded on the same fifteen capability axes from public sources, each grade traceable to its artifact. The AI FinTech Index records both at B on AI centrality, autonomy and integration and A on coverage and outcome evidence. It records Akur8 at A on model risk and regulatory standing and B on governance, commercial transparency and security, and Earnix at B on model risk and C on governance, data stewardship, commercial transparency and security. The index publishes no composite score and declares no winner.
Related comparisons
Other published head to head assessments involving these vendors or their closest peers. The full set for this category is on the Insurance AI page.
Earnix's most consequential gap is governance and bias at C: no fairness testing, protected characteristic handling or disparate impact analysis is published, while price optimisation and personalisation are the practice consumer fairness regulation targets most directly, including the United Kingdom regulator's rules on general insurance pricing practices.
It is also C on data stewardship, with nothing stating whether one carrier's portfolio experience informs models a competing carrier runs on the same multi tenant platform, and C on security, where no certification or trust page was found.
Akur8's gaps are narrower: Amazon Web Services is named as its only cloud with no region selection or residency commitment, and no data protection agreement, subprocessor list or retention schedule is published for the policyholder level exposure and claims history it holds. Both are C on liability, and neither publishes an accuracy commitment on the models it helps build.