AI RESEARCH / insurance
AI Consulting for Insurance Companies.
Identify where AI can reduce operating costs, accelerate insurance workflows, and improve decision-making without automating processes that still require human judgment.
AI FOR INSURANCE /
Where AI Can Create Value in Insurance
Insurance companies are applying artificial intelligence across claims, underwriting, fraud detection, customer service, document processing, and internal operations. The opportunity is not simply to automate more. It is to identify where AI can produce a measurable financial or operational return without introducing unnecessary risk.
Claims Processing
AI can assist with claim intake, document classification, information extraction, damage assessment, claim triage, and repetitive administrative work, allowing adjusters to focus more attention on complex claims.
Underwriting
AI can help underwriting teams analyze large volumes of structured and unstructured information, identify relevant risk factors, summarize submissions, and support faster risk assessment.
Fraud Detection
Machine learning can analyze claims and transaction data for unusual patterns, relationships, and anomalies that may warrant further investigation by fraud and special investigations teams.
Insurance Document Automation
AI can extract, classify, summarize, and route information from applications, policies, claims documents, emails, invoices, reports, and other high-volume insurance paperwork.
Customer Service
AI can assist service teams with routine policy questions, claim status requests, knowledge retrieval, call summaries, email drafting, and other repetitive customer interactions.
Internal Operations
Insurance companies can also evaluate AI for reporting, employee knowledge search, quality assurance, workflow management, compliance support, and other administrative processes.
AI IN INSURANCE: BY THE NUMBERS
What AI Research Shows in the Insurance Industry
Artificial intelligence is already being applied across insurance claims, underwriting, customer service, document processing, fraud detection, and other insurance operations. The results below come from independent industry research, regulatory surveys, and documented insurer implementations.
23 Days
Faster Complex Liability Assessment
Aviva deployed more than 80 AI models across its claims operation. McKinsey reports that the insurer reduced the average time required to assess liability in complex cases by 23 days.
65%
Fewer Customer Complaints
McKinsey reports that Aviva’s AI-enabled claims transformation reduced customer complaints by 65% while improving claims-routing accuracy by 30%. Aviva also reported more than £60 million in savings from its motor claims transformation in 2024.
20% to 40%
Lower Customer Onboarding Costs
McKinsey reports that domain-level AI transformations in insurance have produced 20% to 40% reductions in the cost of onboarding new customers. The firm also reports measurable improvements in sales conversion, premium growth, and claims accuracy.
76%
Insurers Reporting Generative AI Implementation
Deloitte surveyed 200 U.S. insurance executives and found that 76% reported implementing generative AI in one or more business functions. The figure was 82% among life and annuity respondents and 70% among property and casualty respondents.
88%
Auto Insurers Using or Exploring AI
According to the National Association of Insurance Commissioners, 88% of 193 responding private-passenger auto insurers reported using, planning to use, or planning to explore AI or machine learning.
WHAT COULD AI DO FOR YOUR INSURANCE OPERATION?
Find the Insurance AI Opportunities Worth Pursuing
These results show what AI can accomplish in insurance, but they do not mean every insurer, workflow, or AI investment will produce the same return.
Triaium’s research and advisory team examines your claims, underwriting, customer service, document workflows, administrative costs, existing technology, and operational bottlenecks to identify where AI could create measurable value.
We evaluate potential savings, implementation costs, data requirements, operational risks, and expected return before recommending an investment. We also identify decisions and workflows where human judgment should remain central.