A Hybrid Machine Learning Framework For Explainable Insurance Risk Assessment

Abstract

Insurance Providers Increasingly Rely On Analytical Solutions To Improve Underwriting Consistency, Reduce Processing Time, And Optimize Risk Assessment. Traditional Underwriting Approaches Often Depend On Manual Review And Expert Judgment, Resulting In Variability Across Decisions. This Work Presents An Enhanced Smart Underwriting System That Combines Supervised Learning, Explainable Artificial Intelligence, And Actuarial Risk Profiling. Applicant Data Is Processed Through Machine Learning Models To Determine Insurance Eligibility Based On Demographic, Lifestyle, And Health-related Attributes. To Improve Transparency, SHAP (SHapley Additive ExPlanations) Is Integrated To Quantify The Contribution Of Individual Variables Toward Each Approval Decision. Following Approval, Accepted Customer Profiles Are Analyzed Using Unsupervised K-Means Spatial Clustering To Identify Hidden Risk Patterns And Classify Policyholders Into Tier 1, Tier 2, And Tier 3 Risk Segments. These Segments Can Be Directly Associated With Premium Pricing Strategies And Underwriting Recommendations. Experimental Evaluation Demonstrates That The Proposed Approach Delivers Accurate Approval Decisions While Providing Interpretable Explanations And Granular Actuarial Risk Categorization.

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