Akur8, an insurtech company that provides a next-generation pricing and reserving platform for non-life insurers, has acquired Matrisk, an AI-focused platform that turns unstructured regulatory filings into searchable market intelligence.
The deal extends Akur8’s pricing and reserving stack and lands alongside the launch of Akur8 Discover, a new product embedded directly into its pricing platform.
Akur8 Discover lets insurers search, analyse, and compare P&C rate and rule filings using AI. The tool structures filings into tables, rules, and source-linked citations, allowing users to benchmark rating strategies, follow rate movements, and track market shifts across states, perils, and lines of business.
Akur8 combines machine learning with actuarial methods to speed up and improve insurance pricing, loss reserving, and rating governance, while keeping models transparent and regulator-friendly.
It’s built for actuaries who already live inside pricing tools, not analysts exporting spreadsheets at midnight.
The product also aims to reduce friction with regulators. By pulling real-time market and regulatory intelligence straight into pricing workflows, Discover helps teams anticipate objections earlier and shorten pricing and filing cycles.
According to Beinsure analysts, this type of embedded context matters more as rate scrutiny tightens and filing timelines compress.
The Matrisk acquisition deepens Akur8’s AI stack, especially around large language models and unstructured data processing.
That capability now sits alongside Akur8’s existing tooling for re-rating, rate selection, demand modelling, and financial forecasting. Less toggling between systems. Fewer blind spots.
Akur8 CEO Samuel Falmagne said the company has always focused on helping insurers build pricing models faster while keeping transparency and control.
Akur8 has always focused on helping insurers build better pricing models faster, with full transparency and control. Matrisk adds a critical new dimension to that mission.
Samuel Falmagne, Akur8 CEO
“By bringing powerful filings search and competitive intelligence into our platform, we can now offer insurers the ability to understand their market context and integrate this information with Akur8’s existing suite of tools for re-rating and rate selection, demand modeling, and financial forecasting,” Samuel Falmagne said.
He described Matrisk as adding a missing layer – competitive and regulatory intelligence that lets insurers understand market context and connect it directly to pricing decisions. He said the combined platform strengthens Akur8’s position with carriers modernising pricing under regulatory pressure.
“This combination reinforces Akur8’s position as the actuarial platform of choice for insurers seeking to modernize pricing while staying aligned with regulatory expectations and competitive pressures. We are delighted to welcome Matrisk to Akur8 as we take this next step forward,” Falmagne noted.
Sergey Filimonov, co-founder of Matrisk, said the company was built on the idea that AI could turn tedious insurance market research into leverage. He pointed to industry data that’s fragmented, messy, and hard to operationalise. Adoption, he said, validated that problem.
Joining Akur8 allows Matrisk to scale faster and reach users directly where rates are built, shifting pricing tools toward end-to-end decision platforms rather than isolated models.
Matrisk was founded to improve access to insurance market data using large language models trained on unstructured public filings.
Inside Akur8, that capability supports actuaries benchmarking rate factors, predicting regulatory pushback, and accelerating pricing and filing workflows. The goal stays practical. Faster decisions. Fewer surprises.
Akur8 targets P&C (non-life) insurers, MGAs, reinsurers and insurtechs that want to modernize their pricing and reserving stack. Its value proposition is speed (often 5-10x faster model builds), better model performance, and strong governance and auditability compared with spreadsheet-based workflows or in-house tooling.
The company positions itself as an actuarial platform rather than a pure data-science tool: models are built and controlled by actuaries, with machine learning used in a constrained, interpretable way that aligns with regulatory expectations (e.g., GLM-style structures, documentation, and audit trails).
