Explainable AI for Insurance
Insurers run AI in underwriting, claims triage, fraud detection, and customer interactions, where state regulators, policyholders, and plaintiffs’ attorneys can challenge every decision. Seekr® gives actuarial, claims, and compliance teams continuous observability and plain-language explainability, so they can show their work on every AI decision.
How Seekr works for insurance
From claims triage through ongoing model monitoring, Seekr gives claims, underwriting, and compliance teams the evidence they need at every stage of the AI lifecycle.
Prepare and govern your data
Turn unstructured policyholder records, claims files, medical documentation, and regulatory filings into training-ready datasets with full lineage. Your data stays in your environment, and every preparation step is logged, so you can show regulators exactly what the model learned from.
Score and source every output
Every output carries a provenance trail and a confidence score, so claims analysts and underwriters can show their work when a DOI examiner or policyholder asks without reconstructing the reasoning after the fact.
Certify before you deploy
Compare candidate models side by side against your own claims scenarios, underwriting criteria, and actuarial benchmarks. Score for unfair or proxy discrimination, accuracy, and failure modes before any model touches a production decision.
Monitor and re-evaluate
Detect model drift as claims patterns shift, regulatory requirements change, or market conditions evolve. Retest against the same criteria on a continuous basis and maintain a documented performance record over time.
Core features built for the regulatory complexity of insurance
Know why each claim was denied or policy underwritten, validate against each state’s requirements, and detect drift and bias as claims and fraud patterns shift.
Model-agnostic explainability
Trace any output to its data and reasoning. When regulators question a denial or an underwriting call, show the evidence trail, not a reconstruction.
Quantified model risk
Define risk profiles for your lines of business, jurisdictions, and compliance needs. Score models against them and catch failures before production.
Flexible deployment
Run in Seekr’s cloud, yours, on premises, or air gapped. Each is designed to keep policyholder data and PHI in your infrastructure.
Continuous evaluation and drift detection
Claims patterns shift as markets, weather, and fraud tactics evolve. Detect drift in accuracy, bias, and risk posture, with audit-ready evidence.
Challenge and correct AI decisions
When your team sees why a claims or underwriting call was made, they can challenge it, correct it, and confirm the fix, with every step documented.
Jurisdiction-specific governance
Set evaluation criteria per state, line of business, or regulatory framework. Test and certify models against each jurisdiction’s requirements.
Seekrflow is the AI operating system for insurance
Launch prebuilt insurance solutions instantly or create your own on a robust, extensible architecture built for scale. SeekrFlow enables full visibility and control over how AI learns, reasons, and delivers results in modern insurance environments.
Request a Demo
Deploy Seekr anywhere
Seekr cloud
Fully managed in the Seekr cloud. Fastest path to production for carriers that want explainable AI without infrastructure overhead.
Fully managed in the Seekr cloud. Fastest path to production for carriers that want explainable AI without infrastructure overhead.
Your cloud
Deploy in your AWS, Azure, or GCP environment. Policyholder data stays in your tenancy under your control.
Deploy in your AWS, Azure, or GCP environment. Policyholder data stays in your tenancy under your control.
On-premises
Deployment for carriers that require physical control over infrastructure, data residency, or air-gapped environments.
Deployment for carriers that require physical control over infrastructure, data residency, or air-gapped environments.
At the edge
Deploy where latency or connectivity constraints require processing closer to the data source. Supports claims processing centers or field adjuster environments.
Deploy where latency or connectivity constraints require processing closer to the data source. Supports claims processing centers or field adjuster environments.
FAQ
How does Seekr help insurers defend AI-assisted claims decisions?
Seekr traces every AI output to the data, reasoning, and model version that produced it, creating an evidence trail that maps to what regulators and policyholders ask for. Instead of reconstructing why a claim was denied after a dispute is filed, your team produces the provenance at the time of the decision. This covers claims triage, fraud detection, underwriting, and any workflow where a regulator, a policyholder, or opposing counsel needs to see how the answer was reached.
Can Seekr run AI on policyholder data and protected health information without that data leaving our environment?
Yes. Seekr deploys in your cloud, your data center, or air gapped. Policyholder records, claims files, and protected health information stay in your infrastructure, and your data is never used to train another organization’s models. You choose the deployment model, and Seekr runs where your data already lives.
How does Seekr handle the fact that insurance is regulated at the state level with different requirements across jurisdictions?
Seekr lets you define risk profiles and evaluation criteria specific to each jurisdiction, line of business, or regulatory framework you operate under. You test and certify models against the requirements that apply in each state and maintain separate evidence records where your compliance team needs them. The platform adapts to your regulatory landscape rather than applying a single national standard.
What happens when claims patterns change and a model drifts after deployment?
Seekr monitors for drift in accuracy, bias, and risk posture on an ongoing basis and flags when a model’s behavior deviates from the criteria it was approved against. You retest against the same framework you used during initial evaluation, which creates a documented performance record over time. When conditions change, you re-evaluate, and the evidence record stays continuous.
How is Seekr different from observability tools we already use?
Observability tools record what happened: which documents were retrieved, which tools were called, how long each step took. Seekr goes further in two ways. First, explainability: tracing which inputs materially drove the output. Second, contestability: once your team understands why a decision was made, they can challenge it, correct it, and confirm the correction, with a documented record of every intervention.
Make every claims and underwriting decision one you can defend
See how Seekr works for insurance claims, underwriting, and compliance in a 30-minute technical briefing.