Historic claims analytics provides a good understanding of various factors that lead to high loss ratios in the past. AI enabled unstructured data analytics help extract key factors hidden inside unstructured claims data and make them available for Underwriting Risk Analysis. This assists in the constant evaluation of underwriting guidelines.

Key Risk Factors and Emerging Risks

Use Case #PRI1

Description: Charlee™ quickly provides risk insights from claims that helps with accurate and efficient risk analysis for Underwriting.

The Charlee Insurance Insights Engine (patent pending) provides a great resource for Underwriting Management Risk Analysis in evaluating and managing portfolios. Previous years of claims data can be leveraged for powerful Artificial Intelligence generated insights for a good understanding of prior losses. These Insights can help an Underwriting Manager develop and monitor an efficient and effective Underwriting strategy, including risk selection and endorsements.

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    Loss Development Analysis

    UseCase #PRI2

    Description: A Loss Triangle analysis chart can be easily created for visualization of various risk indicators extracted from unstructured data.

    Charlee™ will extract various insights hidden in unstructured data to identify key factors leading to various claim development patterns. For example, an Underwriting Manager can use Charlee™ to determine Loss Triangle Patterns on claims that have elements such as distracted driving, wind driven rain and other topics. Additional filters on various claim attributes help gain a deeper understanding of various loss patterns. Understanding these patterns assists in developing effective risk selection as well as appropriate endorsements and overall portfolio management.

    Loss Ratio Analysis by Agent

    Use Case #PRI3

    Description: Underwriting Manager can analyze the Loss Ratios by Agent and State.

    Charlee™ will analyze claims under each agent’s portfolio and reveal key factors related to loss ratios. Various topics from unstructured claims data are extracted and analyzed with these factors. Understanding the agent’s loss ratio patterns help identify the strengths and weaknesses of their portfolios for improved risk selection.