AgentisIQ provides AI‑driven claims processing for property‑and‑casualty insurers, using autonomous AI agents to handle claims end‑to‑end while reducing fraud and operational costs. The platform leverages proprietary datasets from over 10 million claims, IoT streams, and satellite imagery to achieve 95% fraud detection accuracy and adapt continuously through machine learning. By automating the workflow, insurers can lower profit erosion and improve policyholder retention.
Funding
Funding not disclosed
Founders
Product
Problem
Property‑and‑casualty insurers face large financial losses from fraudulent claims, with an estimated $80 billion leaked annually. Detecting fraud manually is time‑consuming and often inaccurate, leading to higher claim costs, policyholder churn, and erosion of profit margins.
Solution
AgentisIQ offers an autonomous AI‑agent platform that manages the entire claims lifecycle, from intake to settlement, without human intervention. The agents ingest structured claim data, IoT sensor feeds, and satellite imagery, applying proprietary models trained on more than 10 million historical claims to assess validity and estimate loss severity. Fraud detection accuracy reaches up to 95 %, enabling insurers to flag suspicious submissions early and reduce payout errors. The system continuously learns from each processed claim, improving its decision‑making and adapting to emerging fraud patterns. Results are delivered through an integrated dashboard that provides real‑time status updates, risk scores, and recommended actions for any remaining manual review. By automating routine tasks and enhancing fraud detection, insurers can lower claim processing costs, retain more policyholders, and protect profit margins.
Target Audience
Primary customers are chief claims officers and claims operations teams at mid‑size to large property‑and‑casualty insurance carriers seeking to automate claim processing and improve fraud detection.
Features
- Autonomous AI agents that execute end‑to‑end claim handling, eliminating manual bottlenecks
- Multi‑modal data ingestion, including claim forms, IoT telemetry, and satellite imagery for comprehensive risk assessment
- Proprietary fraud detection models trained on 10 M+ claims achieving 95 % detection accuracy
- Continuous learning loop that updates models with each processed claim to stay ahead of new fraud tactics
- Real‑time analytics dashboard with claim status, fraud risk scores, and actionable recommendations
- Seamless integration via APIs with existing policy administration and core insurance systems