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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Big data in insurance is not one product or one scoring system. Insurers and insurance-technology firms use driving telemetry, mileage, wellness activity, pharmacy records, claims histories, geospatial property information and other datasets to support pricing, underwriting, fraud review, claims readiness, customer service and marketing.
A numbering problem affects the familiar “29 Big Data Insurance Companies to Know” list. The directly surfaced Built In article is titled “28 Big Data Insurance Companies to Know” and shows an update date of July 22, 2026, while a Built In directory snippet reports “29 Big Data Insurance Companies to Know” with a September 2, 2026 update. Because the two results do not reveal a reconciled list, the examples below are best read as the companies and programs described by Built In, not as a verified current ranking or a definitive count.
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What “big data” means in insurance
In this context, big data means large, varied or frequently updated information that can be analyzed to make insurance decisions. A traditional application might contain age, address, vehicle, property and loss history. A data-intensive operation can add real-time driving signals, mileage, wearable activity, pharmacy information, satellite or geographic data, customer interactions and external risk indicators.
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The important distinction is the decision being supported. A telematics program may evaluate driving behavior for an auto discount; a pharmacy dataset may inform health-insurance operations; a geospatial model may help prepare for a catastrophe; and an agency platform may improve commercial quoting. These uses affect different people and carry different consent, accuracy and fairness questions.
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A 2023 bibliometric analysis in the Journal of Financial Management, Markets and Institutions reviewed 191 Scopus-indexed articles published from 1976 through 2021. Its subjects included privacy, data warehousing, risk management and fraud detection. That publication count describes academic literature, not industry size, insurer performance or compliance.
Where insurers apply data
| Function | Typical information | Decision or outcome | Who is affected |
|---|---|---|---|
| Pricing and risk selection | Driving behavior, mileage, claims history, property characteristics | Premiums, eligibility or risk segmentation | Applicants and policyholders |
| Underwriting | Application fields, pharmacy or health information, commercial submissions, geospatial data | Whether and on what terms to insure | Applicants, agents and underwriters |
| Claims and fraud review | Loss details, images, location, prior claims and behavioral patterns | Claims triage, investigation and preparation | Claimants, adjusters and insurers |
| Customer service | Account activity, interactions and policy history | Personalized support, retention or recommendations | Policyholders and service teams |
| Marketing and product design | Customer segments, response data and pricing inputs | Offers, communications and coverage configuration | Customers, agents and product teams |
The same dataset can serve more than one purpose, but a description of data use does not establish that every customer participates, receives a lower price or experiences a measurable benefit.
Examples of big-data insurance companies and providers
Driving behavior and usage-based auto insurance
These examples use telematics, app-based assessments or mileage information. Availability, device requirements, geographic eligibility, scoring rules and discount terms can change, so a current policy document is more authoritative than a general company list.
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- State Farm — Drive Safe & Save: a usage-based auto program associated with driving information and mileage.
- Safeco — Rewind: listed as an example of using driving-related information in an auto-insurance program.
- Metromile: described through a pay-per-mile model, where mileage is central to the insurance approach.
- Root: uses an app-based driving assessment as part of its insurance process.
- Progressive — Snapshot: a telematics example that evaluates driving-related signals.
- Nationwide — SmartRide: another program built around driving behavior and related telematics.
These models illustrate different pricing questions: how far a vehicle is driven, how it is driven, or both. They should not be treated as interchangeable. The available description does not establish identical data retention, consent choices, discount guarantees or surcharge rules across the programs.
Life, health and wellness data
Life and health applications involve more sensitive information than a mileage calculation, and the examples below represent different consent and pricing contexts.
- John Hancock: its wellness-linked life-insurance example connects insurance with health or activity-related participation.
- Ladder: presented as a digitally oriented life-insurance application process that uses data to streamline applications.
- Haven Life: described as using data-informed pricing in life insurance.
- Vitality: uses biometric and lifestyle information in a wellness-oriented insurance model.
- Aetna: cited for using insurance and pharmacy data in a health-insurance setting.
“Uses health data” does not tell you whether information changes an individual premium, supports care management, verifies eligibility, or is used for another operational purpose. Applicants should read the notice describing data sources, permission, retention and permitted uses before enrolling or authorizing access.
Claims, risk and underwriting operations
Several names in the list are not consumer insurance programs. They are technology providers or analytics businesses whose tools may be used by insurers, agents or commercial customers.
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- Amica: uses geographic information system (GIS) capabilities in disaster preparation.
- SambaSafety: presented as a provider of risk analytics, including information relevant to driving or commercial risk management.
- Gradient AI: provides predictive analytics for insurance workflows.
- Openly: combines analytics with geospatial property data in property-insurance operations.
- Bold Penguin: uses AI-assisted submission data and commercial quoting workflows.
A software provider’s involvement does not mean it sets a customer’s premium. The insurer, underwriting rules, regulatory requirements and data-quality controls determine how an output is acted upon.
Customer experience, provider information and marketing
Big-data work often happens after a policy is sold or outside an individual risk score.
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- Liberty Mutual’s Solaria Labs: cited for data-oriented experiments and customer or risk applications.
- BCBS Axis: an example involving provider information and health-insurance data.
- Allianz: described as using data to personalize travel offers.
- GEICO: uses data-informed marketing in the example list.
- Prudential: applies analytics to personalize customer service.
- Applied Systems: provides agency analytics that can support insurance-business operations.
- Northwestern Mutual: is included for analytics work in a life-insurance context.
Personalization can make a service more relevant, but it can also influence which offers a person sees or how a service team prioritizes interactions. The available account does not provide a company-by-company audit of those effects.
Investment and product configuration
- Cigna Ventures: described as investing in healthcare-technology ventures, connecting an insurer with data-rich health innovation.
- Esurance: its former Coverage Rules Engine is cited as an example of configuring coverage from customer and pricing information. Because the source describes it as former, do not assume that product remains available.
These cases show why a “company list” can mix insurers, technology vendors, investment arms and historical products. They should be labeled by role instead of presented as equivalent insurance offerings.
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| Data category | Possible examples | Potential use | Key question for the customer |
|---|---|---|---|
| Telematics | Speed patterns, braking, time of day, phone interaction or trip data | Usage-based pricing and driving-risk assessment | Which signals are collected, and can participation affect the price upward or only provide a discount? |
| Mileage and usage | Miles driven or frequency of vehicle use | Pay-per-mile or low-usage pricing | How is mileage measured and reconciled if the device fails? |
| Wellness and biometrics | Activity, biometric measures or lifestyle information | Wellness engagement and life or health programs | What is optional, and who can access the information? |
| Health and pharmacy | Insurance records, prescriptions or pharmacy transactions | Underwriting, care operations or eligibility decisions | What notice explains the purpose, retention and sharing? |
| Claims and customer history | Prior losses, conversations, payments and policy changes | Fraud review, service and retention | How can an error be corrected or challenged? |
| Property and geospatial | Location, building characteristics, hazard maps or imagery | Property underwriting and catastrophe preparation | What happens when mapped information is incomplete or outdated? |
| Commercial submissions | Application fields, business details and agent-submitted documents | Commercial quotes and underwriting workflows | Which party is responsible for verifying the submitted data? |
How a data-driven insurance decision typically works
- Collection: Information enters from an application, a connected device, a wearable, a pharmacy or claims system, an agent, a public record or a third-party provider.
- Preparation: The insurer or vendor standardizes formats, removes duplicates and checks whether required fields are missing. Bad input can produce a bad model output.
- Modeling: Statistical or machine-learning systems identify patterns associated with a target such as loss frequency, fraud likelihood, service need or likely quote characteristics.
- Human or rules-based action: An underwriting rule, pricing tier, fraud queue, service prompt or marketing segment uses the output. A model score is not necessarily the final decision.
- Monitoring and correction: Responsible programs test accuracy, monitor disparate outcomes, update models and provide a process for correcting inaccurate personal information.
This sequence is a framework, not proof that every named company follows each step or offers an appeal. Ask the specific insurer how automated tools are used in your policy or claim.
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Potential benefits
- Usage-based pricing can align a premium more closely with measured mileage or driving behavior for some customers.
- More complete property and weather information can help insurers prepare for disasters and prioritize resources.
- Automated submission and quoting tools can reduce repetitive data entry for agents and commercial applicants.
- Claims analytics may identify suspicious patterns earlier or route straightforward claims more efficiently.
- Service and marketing analytics can make communications more relevant to a customer’s situation.
Questions and possible harms
- Transparency: A customer may not know which variables influenced a quote, discount or claim review.
- Disparate impact: A seemingly neutral proxy, such as location or purchasing pattern, can produce unequal outcomes across groups.
- Cross-subsidization: More granular pricing can change who pays more or less compared with pooled insurance rates.
- Privacy: Continuous location, health or behavioral collection may reveal more than is necessary for the stated purpose.
- Data quality: A faulty device, stale map or incorrect record can create an inaccurate assessment.
- Security and sharing: Information may pass among an insurer, technology vendor, agent or service provider under contracts the customer does not see.
The Built In discussion raises these ethical issues but does not resolve them company by company. A long list of data uses is therefore not a fairness certification.
Questions to ask before joining a data-based program
- What exactly is collected? Request the categories, collection frequency, device permissions and whether raw data or only derived scores are retained.
- What decision does it affect? Clarify whether the output can change eligibility, the premium, a discount, a claim review, service treatment or only marketing.
- Is participation optional? Ask what happens if you decline, withdraw permission, change devices or stop sharing data.
- Who receives the information? Look for insurers, affiliates, agents, analytics vendors, healthcare providers and other named recipients.
- How long is it kept? Check retention periods, deletion rights and whether information remains after a policy ends.
- How are errors challenged? Identify the contact channel and documents needed to correct a wrong mileage reading, pharmacy record, property attribute or claim flag.
- Are automated decisions reviewed? Ask whether a person can reconsider a decision and how the insurer tests accuracy and unequal effects.
How to interpret the “29 companies” headline
The number should be treated cautiously. The directly surfaced article at Built In’s insurance big-data article is titled “28 Big Data Insurance Companies to Know” and is marked updated July 22, 2026. A separate Built In directory snippet reports a September 2, 2026 update under “29 Big Data Insurance Companies to Know,” but does not publish an authoritative reconciled list in the available material.
That discrepancy matters because company programs, corporate ownership and product names change. Some entries are insurers, some are business-to-business providers, one example is explicitly described as a former product, and program availability can vary by state or market. Use the list as a map of data-use patterns, then confirm a current offering, privacy notice and participation terms with the company before relying on it.
Bottom line for readers
Big data has become a layer across insurance rather than a single category of company. Telematics can measure driving; wellness programs can connect activity to life or health coverage; geospatial and claims analytics can support underwriting and disaster preparation; and agency, service and marketing systems can use data without directly setting an individual risk score.
The practical test is not whether a company says it uses “big data.” It is whether you can identify the information collected, the decision it supports, the parties who receive it, the consequences of opting out and the route for correcting errors. Because the available Built In listings disagree on whether the current headline count is 28 or 29, verify the live list and each program’s present terms rather than treating the number as a fixed industry ranking.
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