In the EAI Innovation Zone

Enter mock customer information at an interactive station to instantly obtain an AI-generated insurance quote, along with a clear explanation of the main pricing factors.

AI Claims Automation (FNOL & Assessment)

Engage with two demos:

1) Report a minor car accident via a smartphone app using an AI chatbot for FNOL.

2) Upload car damage photos and watch an AI visually assess damage, identify parts, and estimate repair costs.

AI-Driven Personalization

Explore a dashboard showcasing how AI interprets customer data—such as transactions, app usage, and preferences—to proactively recommend pertinent add-ons, like travel insurance before a trip, or tips for risk prevention.

AI for Fraud Detection

Examine a visual network graph where AI uncovers dubious connections and patterns spanning multiple claims, revealing potential organized fraud rings that remain undetectable through manual verification.

Visionary Talks: Hear leaders explore "The Augmented Underwriter and the AI-Powered Agent: Humans and Machines Collaborating in the Future of Insurance," presented by Chief Digital Officers or Futurists.

Panels: Debate crucial topics like "Ethical AI in Insurance: Ensuring Fairness, Transparency, and Accountability in Algorithmic Decision-Making" with Chief Ethics Officers, AI vendors, Consumer Advocates, and Academics.

Deep Dives: Learn from practical applications in sessions like A Case Study: Using Natural Language Processing (NLP) to Extract Insights from Claims Notes and Customer Feedback at Scale, by Heads of Data Science.

Strategic Partnerships: Establishing strategic partnerships is essential to effectively integrate AI technologies into existing workflows. For instance, the Head of Claims or Chief Operating Officer at an insurance company can engage in a collaborative effort with the Sales Director or Product Lead of a reputable AI vendor, working closely alongside procurement experts and technical teams.

Together, they aim to secure a top-tier AI engine that enhances the efficiency and accuracy of motor claims evaluations. This collaborative approach not only leverages the unique expertise of each party but also ensures that the chosen AI solutions align with the company’s operational goals, ultimately leading to improved customer experiences and optimized claim processing times.

Focused Discussions: Address the governance and ethical challenges of AI adoption:

• What level of AI model 'explainability' is required for different insurance decisions?

• How can insurers proactively audit algorithms for bias based on protected characteristics?

• What are regulatory expectations for data governance when using synthetic data for AI training?

• Operational Efficiency: Automates underwriting, claims, and customer service tasks.

• Enhanced Risk Assessment: Improves pricing accuracy and identifies emerging risks.

• Superior Customer Experience: Enables hyper-personalization and proactive engagement.

• Advanced Fraud Detection: Identifies complex fraudulent activities more effectively.

• Data-Driven Insights: Extracts valuable intelligence from structured and unstructured data.