Insurance technologies in 2027: How technology is changing the insurance industry
October 29, 2026 9 min read 981 views
Technology in the insurance industry creates value when it improves a defined decision or service without weakening accountability. The priorities for 2027 are governed AI, connected data, faster claims processing, accessible customer platforms, and resilient operations. Insurance companies need to connect these capabilities with reliable evidence, appropriate human review, and the obligations that apply to their products and markets.
The most useful insurance technology trends concern the way insurance companies operate across underwriting, distribution, claims, and support. New software alone will not repair fragmented records or unclear responsibilities. Avenga’s insurance software development services support the engineering and integration work needed to modernize those processes while maintaining continuity for customers and staff.
Key takeaways
Digital transformation should begin with a measurable problem and an accountable owner. Faster service, better information, and operational efficiency matter only when a change works under realistic conditions. The following priorities help distinguish useful insurance technologies from investments whose benefits remain untested.
- Evaluate AI by intended use, error rates, fairness, and review requirements.
- Connect self-service with accurate policy records and a reliable route to human assistance.
- Treat data quality, cybersecurity, and third-party dependencies as operating requirements.
- Measure corrections, complaints, and work transferred to other teams alongside time saved.
Insurance technologies used in the insurance industry
The main technologies in the insurance industry include artificial intelligence, machine learning, workflow automation, cloud platforms, APIs, mobile applications, and connected sensors. Their role depends on the line of business and the decisions they support. Insurance systems need to preserve the meaning of data across each handoff, whether a customer changes a policy, submits a document, or asks for an explanation.
A 2025 health-insurer survey reported that 84% of 93 participating companies used AI or machine learning in some capacity. Conducted under the authority of 16 U.S. states, it is evidence about that sample, not a universal adoption rate across the insurance sector. Adoption also does not establish that every deployment delivers good outcomes.
Insurance technology trends: Main benefits for insurers
Useful benefits include less duplicate entry, clearer service information, and more consistent handling of routine tasks. Insurers can use connected records to reduce reconciliation work and help employees find the evidence behind a decision. Evaluate those improvements against a baseline, including exceptions and rework. A process that completes quickly but creates incorrect records or more complaints has not demonstrated a sound business benefit.
Technology in the insurance sector can also improve customer access through a portal or mobile app. Customers may be able to retrieve insurance policies, update contact details, or track a request without calling. That convenience depends on accessibility, clear language, identity verification, and accurate status information. Digital-only insurance still needs effective support when a customer cannot resolve an issue online.
How AI is used in insurance operations
AI in insurance can support document classification, evidence retrieval, and preparation of summaries for an employee to review. Generative AI is useful for drafting or explaining information, but it may omit conditions or produce statements unsupported by the source. AI tools should retrieve approved material, expose their sources where practical, and escalate uncertainty rather than invent coverage terms or promises.
For an AI-assisted underwriting process, define which inputs are permitted and who approves the outcome. AI models should be evaluated on representative cases and monitored as products, customers, or source data change. Avenga’s AI services connect use-case design with evaluation and integration, helping teams use AI solutions within explicit operational and decision boundaries.
How technology improves insurance claims processing
Claims processing improves when information arrives in a usable form and exceptions reach the right person. A digital intake service can check for missing fields, organize documents, and route a case to a qualified handler. It should not treat incomplete evidence as proof that a claim lacks merit. Preserve the distinction between collecting information, recommending a next step, and authorizing a decision.
Connect notifications with the actual state of insurance claims, not a separate communication spreadsheet. Record what has been received, what is still required, and who owns the next action. Insurance providers should test accessibility and failure handling as carefully as the happy path. Measure completion time, repeat contacts, correction rates, and complaints to establish whether automation improves the service.
Modernize insurance software, data, and AI around reliable decisions, connected services, and accountable delivery.
How analytics supports risk assessment and fraud detection
Data analytics can support risk assessment by organizing relevant evidence and identifying patterns for investigation. Fraud detection systems may prioritize unusual activity, but an anomaly is not proof of fraud. Test false positives and the impact of an incorrect flag, and provide a documented review path. Keep data provenance and decision records so staff can explain why information was used and how a case was handled.
Connected data is a prerequisite for reliable analytics and AI across the insurance value chain. Agree on identifiers, definitions, ownership, and quality thresholds before expanding model coverage. Avenga’s data services support the foundations needed to connect policy, claims, and customer information without losing the context required for analysis and operational review.
Telematics, connected devices, and usage-based insurance
Usage-based insurance uses relevant behavioral or usage data to inform a product’s pricing or service design. The NAIC’s 2027 insurtech overview describes telematics, smart-home sensors, and other connected tools used in insurance products. Their presence does not remove the need to assess data accuracy, consumer understanding, and applicable restrictions on collecting and using personal information.
Insurance companies can collect more detailed data through digital technologies, but collection should follow a defined purpose. Explain what is measured, how it affects insurance premiums, and what happens when a device fails or a reading is disputed. Product teams should assess whether an input is relevant and permitted rather than assuming that more granular data automatically produces fairer or more accurate pricing.
Digital platforms, self-service, and the customer experience
Digital platforms work best when customers can move between online insurance services and human support without repeating their history. Connect policy administration, identity, documents, and case management before adding a new interface. A portal should show insurance offers and policy terms consistently across channels, with appropriate controls over changes, payments, and access to sensitive records.
Technologies like blockchain are optional architectural choices, not prerequisites for trust or accurate transactions in the insurance business. Evaluate shared-ledger proposals against simpler alternatives, participant governance, privacy, and correction requirements. Technological innovation is valuable when it solves a concrete coordination problem. It should not add an operating dependency merely because a tool is described as innovative insurance technology.
Challenges insurers face when adopting new technologies
The main challenges are legacy integration, data quality, security, model risk, and the ability to support changed processes. EIOPA’s 2025 AI opinion clarifies a risk-based and proportionate approach to governance and supervision under insurance-sector legislation. Its supervisory context matters: the opinion is not a substitute for assessing the requirements applicable to a particular organization or use case.
Operational resilience also depends on external technology providers. The EBA’s DORA reporting guidance explains that the act became applicable on January 17, 2025, and that financial entities within scope need registers of ICT third-party arrangements. Assess scope and local obligations with appropriate specialists. Avenga’s cybersecurity services support risk assessment and protection across connected operations.
| Investment | Evidence to request | Measure before scaling |
| AI assistance | Representative testing and approval rules | Errors and correction workload |
| Claims automation | Exception routing and traceable case records | Repeat contacts and resolution time |
| Customer portal | Accessible journeys and accurate policy integration | Completion and abandonment rates |
| Connected data | Ownership, quality checks, and recovery tests | Reconciliation effort and missing records |
Different insurance models require different evaluation criteria. Life insurance, property cover, and commercial policies involve distinct data, customer needs, and consequences of error. Before adopting AI technology, test whether AI and machine learning improve risk assessment for the intended population without introducing unacceptable bias. AI systems need documented inputs, review procedures, and performance thresholds; their role in the insurance industry should follow the decision they support, not the availability of a model.
The trends in the insurance industry should be assessed against that operating context. The traditional insurance sector can use technology to transform fragmented servicing without replacing every core platform. When assessing insurance tech trends, ask how a change would enhance customer access, preserve explanations, and support staff handling exceptions. Technological progress is reshaping the insurance business only when the organization can sustain the resulting service and resolve mistakes.
Will AI replace insurance jobs?
AI can change the tasks people perform, but a technical capability does not establish a reliable forecast of employment. An automated summary may reduce document-reading time while increasing the need for validation, exception handling, and model oversight. Plan role changes with employees and measure total workload, not just the tasks removed from one queue. Complex decisions and customer disputes still require clear accountability.
An insurance AI project needs a precise boundary between assistance and decision authority. Reliable source data, representative testing, and a clear route to human review should be part of the design, so faster processing does not make errors harder to detect or decisions harder to explain.
Olena Domanska, AI Engineering Manager at Avenga
FAQ
Conclusion: The future of insurance technology
The trends in insurance favor connected information, useful automation, and accountable decisions. New technologies should improve a specific service without obscuring the evidence or responsibilities behind it. To assess your software, integration, or AI priorities against practical delivery needs, Contact Avenga and define a suitable starting point.
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