AI that processes insurance claims from first notice through payout. These systems ingest documents, validate coverage, detect fraud, and auto-decide straightforward claims—learning from adjusters' decisions. The result: faster settlements, lower costs per claim, and adjusters focused on complex cases.

AI Insurance Fraud Shield uses machine learning and industry-wide data to detect suspicious claims, entities, and behaviors in real time across the insurance lifecycle. It scores risk, flags anomalies (including deepfake and synthetic identity attempts), and orchestrates automated investigations through APIs and agents. Insurers reduce loss ratios, cut manual review costs, and accelerate legitimate claim payouts while improving overall fraud resilience.

This AI solution uses AI, telematics, and predictive analytics to continuously assess risk and price insurance policies at a highly granular, individual level. By automating underwriting decisions and dynamically adjusting premiums to real-world behavior, insurers can improve loss ratios, accelerate quote-to-bind cycles, and offer more competitive, personalized products that attract and retain profitable customers.

AI models ingest claims, policy, telematics, medical, image, and network data to detect anomalous patterns and flag suspicious insurance activity in real time. By identifying fraud rings, deepfakes, staged claims, and social engineering attacks before payout, it reduces loss ratios, protects customers, and strengthens regulatory compliance. Carriers gain faster, more accurate claims decisions and can focus investigators on the highest‑risk cases.

AI Insurance Fraud Intelligence analyzes claims, policy, telematics, network, and image data in real time to flag suspicious activity and prioritize high‑risk investigations. It augments SIU teams with pattern detection, social-engineering insights, and cross-claim link analysis to uncover organized fraud rings. This reduces loss ratios, cuts investigation time, and improves the accuracy and fairness of claim payouts.

This AI solution uses AI, machine learning, and generative models to assess insurance risk, extract and analyze underwriting data, and continuously refine risk models in real time. By automating document intake, risk scoring, and decision support, it enables faster, more accurate, and personalized underwriting while reducing loss ratios and improving regulatory compliance.

AI Claims Liability Engine automates assessment of insurance claims by analyzing documents, images, and historical data to estimate fault, coverage applicability, and likely payout ranges. It streamlines claims handling, reduces leakage and fraud risk, and enables more consistent, data-driven liability decisions that accelerate settlement and improve loss ratios.

This AI solution uses AI to triage, validate, and process insurance claims end-to-end across property, casualty, and medical lines. By automating document intake, fraud checks, coverage validation, and payment decisions, it accelerates claim resolution, reduces manual effort, and improves payout accuracy and customer experience.

This AI solution uses AI agents to intake, triage, validate, and route insurance claims across property, casualty, and other lines of business. By automating documentation review, fraud checks, and claims decisions, it shortens cycle times, reduces manual workload, and improves payout accuracy and customer experience for insurers.

This AI solution uses AI-driven analytics and telematics data to evaluate and predict underwriting, pricing, and portfolio performance for insurers. By turning large volumes of structured and behavioral data into actionable insights, it helps carriers optimize risk selection, refine usage-based products, and identify profitable market segments to grow revenue and improve loss ratios.

AI Claims Intake Automation uses machine learning and workflow orchestration to capture, validate, and route insurance claims with minimal human intervention. It ingests omnichannel submissions (photos, forms, emails, FNOL), auto-populates claim systems, and applies business rules to accelerate triage and decisioning. This reduces cycle times, lowers handling costs, and improves customer experience through faster, more accurate claim setup and resolution.

This AI solution uses agentic workflows to automate policy activation, claims intake, and customer interactions across the insurance lifecycle. By coordinating multiple specialized agents to handle data collection, verification, and decision support, it speeds up policy issuance and claims resolution while reducing manual effort and error. Insurers gain higher throughput, lower operating costs, and more consistent customer experiences at scale.

This application area focuses on forecasting key insurance risk drivers—such as asset-liability mismatches and mortality trends—to improve capital planning, pricing, and balance sheet management. It replaces or augments traditional stochastic and actuarial models with faster, more granular, and more adaptive forecasting tools that can handle complex market dynamics and evolving policyholder behavior. The goal is to project future cash flows, liabilities, and capital needs under a wide range of scenarios with higher accuracy and much shorter run times. In practice, this means using advanced models to simulate how assets and liabilities evolve together, and to anticipate changes in mortality and longevity patterns across cohorts, geographies, and time. By providing more reliable projections for ALM and mortality, insurers and pension funds can reduce mispricing and reserving risk, optimize investment strategies, and respond more quickly to shocks such as interest-rate shifts or health crises. This leads to better capital allocation, stronger solvency positions, and more competitive product offerings.