Industries
Data and AI for insurance
Insurers run on data about risk, policies and claims. We help organize it on AWS, Microsoft Azure or Google Cloud and apply AI where people stay in control of decisions.
Why data and AI matter in insurance
Insurance is an information business. Pricing, underwriting, claims and service depend on data that is often spread across policy administration systems, broker portals, claims platforms and documents. When that data is consistent and well governed, teams can see risk and customers more clearly and respond faster. Because decisions affect customers directly, the way AI-assisted steps are governed matters as much as what they do.
Typical use cases
- Claims intake and triage that reads forms, photos and correspondence, with adjusters deciding.
- Submission and policy document extraction that feeds underwriting workbenches.
- Underwriting analytics that are reproducible and explainable to reviewers.
- Fraud signal review in which investigators examine flagged cases.
- Service assistants that answer from approved policy wording and hand over to staff.
- A single view of the policyholder for brokers, agents and service teams.
- Reserving and actuarial data pipelines with lineage and controlled changes.
- Regulatory and management reporting with traceable figures.
Data foundations we typically build
Insurance foundations usually begin with a lakehouse or data warehouse that consolidates policy, claims, billing and broker data from several administration systems using change data capture and batch loads. Master data management gives each policyholder, broker and risk a consistent identity, and a metadata catalog and business glossary settle definitions such as earned premium and loss ratio. Documents, photos and recordings are stored with metadata so that retrieval-augmented generation (RAG) assistants can cite their sources. A semantic layer supports shared reporting, and lineage links every figure to its origin.
Where ARCHR can help
- Data Modernization: consolidating policy, claims and customer data under clear ownership.
- Analytics & Reporting: shared definitions for underwriting, claims and distribution reporting.
- AI, Agents & Machine Learning: document intake, claims triage and service assistants, with adjusters deciding.
- Governance, Security & Privacy: model documentation, access controls and audit trails for AI-assisted steps.
- Strategy & Design: roadmaps sequenced around legacy constraints and the regulatory calendar.
Governance and privacy context
Insurers typically face prudential and market conduct oversight that varies by jurisdiction: for example Office of the Superintendent of Financial Institutions (OSFI) and provincial regulators in Canada, state insurance departments in the United States, and European Insurance and Occupational Pensions Authority (EIOPA) and national supervisors in Europe. Personal information is subject to privacy law such as Personal Information Protection and Electronic Documents Act (PIPEDA), Quebec Law 25, General Data Protection Regulation (GDPR) or state laws, and health and claims data are especially sensitive. We design to support alignment through human review of consequential decisions, explainable steps, retained evidence and careful data access. Your compliance and legal advisers determine what applies to you. Our article on OSFI model risk and AI adds background on one Canadian regime.
Method
What an engagement could look like
Discovery & Strategy
Map policy, claims and distribution data, and agree one workflow and its success measures.
Pilot & Validation
Pilot on historical claims or documents, with adjusters reviewing results before anything is relied on.
Build & Scale
Extend in stages with monitoring, access controls and documented oversight.
Optimize & Evolve
Review performance, feedback and drift, and plan the next workflow.
Scenarios
Illustrative scenarios
These scenarios show how an organization could approach a related problem. They are not client results.
AI-assisted claims handling
How claims intake and triage could be supported, with adjusters deciding.
Read the scenarioIllustrative scenarioAI-assisted know your customer (KYC) onboarding
How customer verification could combine document review with analyst control.
Read the scenarioIllustrative scenarioAI and data governance
How a growing firm could scale governance with its data.
Read the scenarioThis page is general information about ARCHR services. It describes typical challenges and how an engagement could be designed. It is not legal, financial or medical advice, and it does not assure compliance with any regulation. Regulators and laws are named as context only.
FAQ
Frequently asked questions
Can AI make claims or underwriting decisions?
Our designs assist qualified people rather than replace them. AI can summarize, extract and flag, while adjusters and underwriters make and record the decisions.
Do you work with legacy policy administration systems?
Yes. We design around the systems you have and modernize in stages, adding components only where there is a clear need.
How is customer data protected?
With least-privilege access, encryption, logging and documented retention, designed with your privacy team and with PIPEDA and applicable provincial law in mind.
How do you address fairness in pricing and underwriting models?
By documenting model inputs, testing for unintended bias, keeping explanations available to reviewers and keeping people accountable for decisions. Your actuarial and compliance teams set the standards.
Let's talk about your data and AI priorities
Tell us what you are trying to solve. We aim to reply within one business day.