By Akur8Learning Objectives
- Understand how Akur8 speeds up insurance pricing and reserving while keeping models explainable
- Learn why "glass-box" transparency matters for regulated rate models
- Recognize that actuaries validate and own the filings the platform helps produce
What Is Akur8?
Akur8 is an actuarial platform that uses machine learning to automate the building and calibration of insurance pricing, rating, and reserving models. Traditionally, actuaries build these models by hand over weeks — a careful, iterative process constrained by the need to justify every coefficient to regulators. Akur8's engine automates the heavy lifting, generating and calibrating models far faster, while deliberately keeping them transparent so an actuary can see and explain exactly how each variable affects the price. It targets the core tension in insurance pricing: modern machine learning is fast and powerful, but regulators will not approve rates they cannot understand.
Akur8 was founded in 2018 and is headquartered in Paris, with a growing presence in North America. The company raised a $120 million Series C round led by growth-equity firm One Peak, with insurance-software maker Guidewire among its backers, and expanded into reserving through an acquisition. It is used by hundreds of insurers and thousands of actuaries worldwide, making it one of the category-leading names in actuarial AI.
💡Key Concept
Glass-box machine learning: Many high-accuracy models are "black boxes" — they predict well but cannot explain why, which is unacceptable for regulated insurance rates. Akur8's approach is deliberately transparent, or "glass-box": it uses machine learning to find patterns automatically but produces models whose structure and variable effects an actuary can read, justify, and file with regulators.
✅Tip
Visit Akur8: akur8.com — built for insurers, managing general agents, and actuarial teams; enterprise pricing by quote.
Core Capabilities
Automated Pricing and Rating Models
Akur8 auto-builds and calibrates the generalized-linear-model-style structures actuaries use for pricing, testing many variable combinations quickly. What might take an actuary weeks of manual iteration is compressed into a fraction of the time without sacrificing readability.
Transparent Model Output
Every model Akur8 produces is inspectable — actuaries can see each variable's contribution and adjust it — so the output is defensible in a rate filing rather than an opaque score. This transparency is the platform's central design principle.
Reserving
Beyond pricing, Akur8 supports reserving — estimating the funds an insurer must hold for future claims — bringing the same fast, transparent modeling to a second core actuarial workflow and connecting it to the pricing side.
Deployment and Collaboration
The cloud platform lets actuarial teams collaborate on models, manage versions, and move approved models toward production rating engines, standardizing what is often a fragmented, spreadsheet-heavy process.
Strengths
- Speed with transparency — delivers the pace of machine learning while producing models actuaries can read and justify.
- Regulator-ready by design — the glass-box output is built to satisfy the explainability that rate approval requires.
- Broad adoption — used by hundreds of insurers and thousands of actuaries across many countries, a strong validation signal.
- Pricing and reserving in one platform — covers two core actuarial workflows rather than just one.
Limitations & Considerations
- Actuaries validate and own the filings — Akur8 accelerates modeling, but a qualified actuary must review, sign off on, and defend every rate to regulators. The platform does not remove professional accountability.
- Regulatory explainability is non-negotiable — the transparency requirement is precisely why black-box alternatives are unsuitable here; even Akur8's output must still be justified line by line in many jurisdictions.
- Enterprise scope and cost — it is a large-scale platform for insurers and actuarial teams, not a lightweight tool, and requires meaningful onboarding and data integration.
- Garbage-in risk — model quality depends on the insurer's historical data, and biased or incomplete data can produce models that look clean but price poorly.
Best Use Cases
| Task | Why Akur8 |
|---|---|
| Building pricing models faster | Automates and calibrates rating models in a fraction of manual time |
| Passing regulatory review | Produces transparent models actuaries can justify and file |
| Modernizing reserving | Applies the same fast, explainable modeling to reserve estimates |
| Standardizing actuarial workflow | Cloud collaboration replaces fragmented spreadsheet processes |
Getting Started
- Engage Akur8 through its website and scope the pricing or reserving workflows you want to modernize.
- Integrate historical policy and claims data and configure the platform for your actuarial team.
- Use the engine to build and calibrate models, reviewing each variable's transparent contribution.
- Have qualified actuaries validate the models, then prepare and file rates with the relevant regulators.
Key Takeaways
- Akur8 is a transparent machine-learning actuarial platform for insurance pricing, rating, and reserving.
- Its glass-box design gives actuaries machine-learning speed while keeping models explainable to regulators.
- It is used by hundreds of insurers and thousands of actuaries worldwide and is backed by a $120 million Series C led by One Peak.
- The platform accelerates modeling, but actuaries validate and own every rate filing, and data quality drives model quality.
