AI Use Cases in Insurance

This AI solution analyzes structured and unstructured data to predict claim severity, flag likely escalations, and streamline catastrophe claim tagging thereby accelerating first notice of loss and improve reinsurance accuracy. The pilot of the in-house "CATIA" tool uncovered 500 additional catastrophe claims across five events, saving $1.4 million while enabling faster crisis response.

"Chatty" is a generative AI-based chatbot that helps customers navigate insurance policies, ask coverage questions, and initiate claims directly on the website. It tackles the problem of long call queues and limited digital self-service by delivering fast, multilingual support and reducing the need for phone-based assistance.

BRIAN is a generative AI assistant used by underwriters to quickly retrieve answers from complex underwriting manuals. It provides concise, source-based responses to user queries and includes direct links to the relevant sections in documents. Initially deployed for Property and Casualty lines, BRIAN helps streamline daily tasks, reduce manual search time, and support onboarding of junior underwriters. It is now scaling across business lines and is positioned for broader application across operations.

A generative AI-powered chatbot provides instant answers to customer insurance questions via digital channels. It replaces a rule-based chat solution and uses LLM-based models to understand queries, retrieve relevant information from product documents, and generate natural-language responses. The chatbot is available 24/7. Initially launched for general queries, it is being expanded to deliver personalized answers based on the customer's specific policy data.

An AI system that enables real-time detection of insurance fraud by combining machine learning with advanced analytics. It leverages a robust data platform and model deployment pipeline to analyze structured and unstructured data—such as audio, images, and claim metadata—flagging anomalies and monitoring model performance to continuously improve detection accuracy.

Streamlines data-heavy claims processes by summarizing case details, flagging inconsistencies, and drafting next steps to reduce workload and improve accuracy.

This solution addresses the need for a secure, enterprise-grade generative AI platform accessible by insurance employees. It enables staff to generate, summarize, translate, and draft text and code quickly—all within a safeguarded environment, avoiding data leakage and ensuring compliance with GDPR and internal policies

This generative AI chatbot enhances customer service by delivering 24/7 personalized, accurate answers to policy and coverage questions. It solves the limitations of scripted bots by generating unique responses based on company-specific data—reducing call volumes and wait times while preventing misinformation.

A generative AI assistant that optimised commercial underwriting by guiding users through product design, configuration, and pricing steps via natural-language prompts. It solves the complexity and inefficiency of traditional underwriting by streamlining the creation and adaptation of insurance products.

A generative AI platform integrated into Lemonade's customer experience operations. It automatically responds to customer emails and chat messages using large language models, resolving over 30% of incoming inquiries without any human intervention.

An AI assistant listens to live calls between advisers and customers, identifying keywords and prompting advisers with relevant questions or coverage reminders—such as electric vehicle or e-scooter policies. The system aims to ensure that advisers mention suitable coverages and accurately gather information during conversations.

ClaimsGenAI processes incoming corporate insurance claims—tens of thousands per year—by analyzing large volumes of structured and unstructured documents (e.g., loss reports, emails) to classify content, extract key information, and flag potential recovery or fraud opportunities. The tool includes a chatbot to support handlers with governance queries and workflows, keeping human experts in control of all decisions.

A machine learning and natural language processing system analyzes employees' existing skills and matches them with personalized training and career path suggestions. The aim is to support workforce upskilling and align talent development with business needs.

A deep learning model uses over 70 customer variables to assess the likelihood of major traffic accidents. The model helps underwriters make more accurate risk assessments and supports risk-adjusted pricing strategies in motor insurance.

The AI Claims Copilot ingests documents, images, invoices, contracts, and incident details to provide claims handlers with concise summaries, highlight inconsistencies, suggest next steps, and draft context-aware communications. It addresses the challenge of manual data triage and repetitive administrative tasks, enabling faster and more precise claims handling.

An AI-driven pricing tool integrates structured and unstructured data—such as climate projections, customer risk profiles, and historical claims—to support advanced risk modeling. It specifically incorporates climate risk variables to improve pricing accuracy and align premiums with emerging environmental threats.

HDI Global has developed an internal generative AI system, HDI-GPT, designed to extract information from unstructured texts and images in real time. It addresses the challenge of manually interpreting large volumes of policy documents, claims descriptions, or legal texts by enabling employees to query and retrieve relevant insights quickly while keeping a human-in-the-loop oversight.

An AI-powered system processes claims documents by automatically digitizing and structuring unstructured content—such as PDFs, images, and text—to create AI-native claims workflows. It addresses the inefficiency of manual document handling and enables scalable, automated claims intake across different markets and business lines.

An AI platform automates the intake of unstructured documents—such as PDFs, images, and emails—by extracting and structuring relevant data for downstream processing. It enables faster triage of submissions and claims, reduces manual data entry, improves fraud detection, and supports scalable deployment across business lines.

A generative AI tool creates hundreds of tailored ad variations—such as audio scripts—based on historical campaign data. It then optimizes and deploys the most effective variants in real time to enhance engagement and campaign ROI.

A generative AI system uses OpenAI's GPT to draft claims-related emails. It focuses on reducing insurance-specific jargon and adopts a more empathetic, customer-centered tone. The system addresses issues with human-written communications being unclear or accusatory and takes over initial writing while allowing human review.

An AI assistant embedded in Outlook and Teams that pulls real-time CRM data and sales insights into users' workflows. It solves the problem of data staleness and time wasted toggling between apps by enabling quick updates, contact linking, and relationship summaries directly within productivity tools.