GENERATIVE BI · SELF-HOSTED · OPEN SOURCE
Insurance Analytics Platform: Self-Hosted, AI-Powered BI for Insurers
An insurance analytics platform that runs inside your own environment. AI text-to-SQL for claims, underwriting, and pricing with full data residency, row-level security, and open-source auditability. Book a demo.
An Insurance Analytics Platform Built for Your Own Environment
An insurance analytics platform turns policy, claims, and actuarial data into decisions an insurer can defend, where the data is regulated personally identifiable information, the rating is examined, and where it lives matters as much as what it shows. Analytify gives carriers, insurtechs, and actuarial teams an AI-powered platform that runs entirely inside their own environment, so analytics for claims, underwriting, and pricing happen without policyholder data ever leaving the carrier’s perimeter. It is the rare combination of generative BI and full data residency that a regulated insurer can actually deploy.
Every large BI vendor will sell an insurer a cloud dashboard. Far fewer will let the carrier keep the data on-premises or in a private VPC, run open-source software its own auditors can inspect, and still get plain-English, AI-driven analysis. That gap, between what insurers are offered and what their compliance and actuarial teams can approve, is exactly what a self-hosted insurance BI platform closes.
See an insurance analytics platform running inside a private VPC, on your terms.
What Is an Insurance Analytics Platform?
The distinction from ordinary self-service analytics is the regulatory weight. A claims dashboard is not just a chart; it is potentially the basis of a rate filing or a market-conduct response. That raises the bar on three things at once: where the data sits, who is allowed to see each row, and whether the carrier can prove how a number was produced. Insurance analytics is the category that treats those three constraints as first-class, not afterthoughts.
Why Insurance Data Needs a Different Kind of BI
Three forces make insurance analytics its own discipline. Fraud is enormous and persistent: the Coalition Against Insurance Fraud estimates insurance fraud costs the United States roughly $308 billion every year, with property and casualty lines a large share of that total. Margins are thin and cyclical: the US property and casualty industry posted a net combined ratio near 92.9% for full-year 2025, its strongest underwriting result in more than a decade, according to AM Best and Triple-I/Milliman figures, which means a few points of loss ratio decide whether a book is profitable. And the data itself is sensitive enough that residency is non-negotiable for many carriers.
The takeaway for a CDO or chief actuary: the upside of AI in insurance is real, and capital is flowing to it, but only if it can be deployed where the data is governed. An analytics platform that forces a cloud upload of policyholder records is a non-starter for the highest-value, most sensitive workloads.
What Can an Insurance Analytics Platform Do? Core Use Cases
Underwriting and risk selection
Score and segment risk at the point of quote, monitor hit ratios and quote-to-bind, and spot adverse selection before it shows up in losses. This is where predictive analytics earns its keep, surfacing the variables that separate profitable risks from the rest.
Claims analytics
The heart of insurance BI: loss ratio by line and segment, claims frequency and severity, cycle time and time to first contact, leakage, and litigation propensity. Insurers using advanced analytics often improve loss ratios by several points, which on a thin combined ratio is the difference between an underwriting profit and a loss.
Fraud detection
Flag anomalous claims patterns, network and ring behavior, and high-risk indicators for special investigation, the analytics that attack a $308 billion problem before payout rather than after.
Pricing and actuarial
Support rate adequacy, rate-level monitoring, and loss-cost trend analysis with governed data the actuarial team can stand behind in a filing, instead of spreadsheets no examiner can trace.
Reserving
Track development triangles, IBNR, and reserve adequacy over time, so finance and actuarial see the same governed numbers and reserve changes carry documented lineage.
Customer LTV, retention, and distribution
Model policyholder lifetime value and churn, measure retention and cross-sell, and analyze agency and distribution performance, the commercial side of insurtech analytics that funds the rest.
Essential Insurance Dashboards and KPIs
A strong claims dashboard tracks the metrics underwriting, actuarial, finance, and the board all watch. These are the core ones.
| KPI | What it measures | Why it matters |
|---|---|---|
| Loss ratio | Incurred losses as a share of earned premium | Core underwriting profitability |
| Combined ratio | Loss ratio plus expense ratio | Whether the book makes an underwriting profit |
| Expense ratio | Underwriting expenses as a share of premium | Operational efficiency |
| Claims frequency | Number of claims per exposure or policy | Risk-selection and trend signal |
| Claims severity | Average cost per claim | Loss-cost and inflation trend |
| Claims cycle time | Time from first notice to settlement | Cost control and customer experience |
| Policy retention rate | Share of policies renewed | Book stability and lifetime value |
| Net promoter score (NPS) | Policyholder satisfaction and loyalty | Retention and distribution health |
| Hit ratio | Quotes that convert to bound policies | Underwriting appetite and pricing fit |
Build these insurance dashboards on data that never leaves your environment.
Claims Analytics Without the Audit Scramble
Claims is where an insurance analytics platform earns its budget. Instead of analysts assembling loss-run and bordereaux reports by hand each cycle, a governed platform computes loss ratio, frequency, severity, and leakage from source data on a defined schedule, with the lineage attached. Two capabilities make this defensible. Row-level security isolates data by line of business, state, agency, and adjuster team, so reviewers and examiners see exactly what they should and nothing more. And strong data governance keeps definitions and lineage documented, which is what turns a claims dashboard into audit evidence rather than a liability. A clear semantic layer means loss ratio is defined once and the same everywhere.
Deployment and Compliance: Where Your Data Lives
This is the section every other insurance analytics page skips, and it is the one that decides the deal. Analytify is a self-hosted BI tool. It runs on-premises, in your private cloud account, or in an air-gapped environment, so regulated policyholder data never leaves your perimeter and never transits a vendor’s cloud. That posture maps directly to NAIC model rules, state insurance data-security regulation, and SOC 2 expectations your security team already answers to.
Because it is open source, the carrier’s own security, model-risk, and audit teams can examine the code directly. For a function governed by vendor-risk and model-governance expectations, an auditable system is materially easier to approve than a black box. Combined with row-level security and full data residency, this is the deployment posture that lets an insurer adopt AI-driven analytics without inheriting cloud-egress risk. The same approach already underpins our regulated-industry work in banking, healthcare, and across financial services.
Ask Your Data in Plain English: AI Text-to-SQL for Insurance
Self-hosting does not mean giving up modern AI. Analytify brings generative BI and AI-powered business intelligence inside the firewall, so a claims or actuarial user can ask a question in plain English and get a governed, auditable SQL query in return, all without the data leaving the environment.
→ Analytify writes the SQL against your claims and premium tables, returns the breakdown, highlights the threshold breaches, and keeps the query visible for audit.
Pairing AI with data residency is the combination no large incumbent leads with, and it is the most citable, most defensible thing an insurer’s analytics stack can offer in 2026. It connects to the warehouses carriers already run, including Snowflake, BigQuery, PostgreSQL, and Microsoft SQL Server, plus policy and CRM systems such as Salesforce.
How Does Analytify Compare to Verisk, SAS, Qlik, and Sisense for Insurance?
The incumbents are capable and well known. Verisk and SAS bring deep insurance data and models, while Qlik and Sisense bring general BI. But for a regulated carrier, the deciding factors are hosting, auditability, and cost, and most of these are cloud-first and priced for lock-in.
For specific side-by-sides, see Analytify vs Tableau, Analytify vs Power BI, Analytify vs Qlik Sense, and Analytify vs Sisense, or review pricing. Carriers that need analytics inside their own products will also want embedded analytics.
| Capability | Verisk / SAS / Qlik / Sisense | Analytify |
|---|---|---|
| Self-hosted, on-prem, or air-gapped | Limited or cloud-first | Yes, by default |
| Open source and auditable code | No | Yes |
| Data residency, no cloud egress | Often requires vendor cloud | Data stays in your environment |
| Row-level security for regulated PII | Varies, add-on | Built in |
| AI text-to-SQL inside your firewall | Cloud-based AI | Runs in your environment |
| Embeddable and white-label for agents and portals | Limited or extra cost | Yes, native |
| Licensing | Per seat, six-figure enterprise | Platform license, unlimited internal users |
Frequently Asked Questions
It is software carriers and insurtechs use to analyze claims, select and price risk, manage reserves, and understand customers. It must meet data-residency, auditability, and row-level security requirements because it handles regulated policyholder data.
Yes. Analytify is self-hosted and can run on-premises, in a private VPC, or air-gapped, so regulated policyholder data never leaves your environment or transits a vendor cloud.
Loss ratio, combined ratio, expense ratio, claims frequency and severity, claims cycle time, policy retention, NPS, and hit ratio. Executives usually want loss ratio, combined ratio, and premium trend in one top row.
It flags anomalous claims patterns, network and ring behavior, and high-risk indicators for special investigation before payout. Insurance fraud costs the United States roughly $308 billion a year, so catching it early is a direct loss-ratio lever.
It supports rate adequacy, rate-level monitoring, and loss-cost trend analysis on governed data with documented lineage, so the actuarial team can defend numbers in a rate filing rather than rebuilding them in spreadsheets.
Yes, and the open code is an advantage. Security and audit teams can inspect exactly what the software does, which supports vendor-risk and model-governance review, while self-hosting keeps policyholder data inside the carrier and aligned with NAIC and SOC 2 expectations.
By tightening risk selection at underwriting, catching leakage and fraud in claims, and trending loss costs for pricing. Carriers using advanced analytics commonly improve loss ratios by several points, which is decisive on a combined ratio near break-even.
SAS, Verisk, and similar enterprise tools are typically six-figure, per-seat, and lock-in. Analytify uses a platform license with unlimited internal users on infrastructure you already run, which is usually far lower in total cost.
See Analytify running on your own data
Book a walkthrough and we will show Analytify against a stack like yours, self-hosted, with no per-seat pricing.
