From Black Box to Glass Box: Implementing Explainable AI in Insurance Underwriting
Author(s): Jalees Ahmad
Publication #: 2603032
Date of Publication: 26.03.2026
Country: United States
Pages: 1-7
Published In: Volume 12 Issue 2 March-2026
DOI: https://doi.org/10.62970/IJIRCT.v12.i2.2603032
Abstract
The insurance industry is currently navigating a profound structural transformation, moving away from historical, manual-intensive underwriting toward a digital-first paradigm powered by artificial intelligence and high-dimensional data analytics. While machine learning (ML) architecture specifically deep learning and ensemble methodologies—have demonstrated the ability to reduce operational costs by up to 50% and quote cycle times by 90%, their inherent opacity presents a formidable barrier to full-scale adoption. This white paper examines the transition from "black box" systems to "glass box" architectures through the systematic implementation of Explainable AI (XAI) frameworks. By synthesizing current research on local and global interpretability techniques, such as SHAP and LIME, this analysis evaluates the technological mechanisms required to satisfy rigorous global regulatory standards, including the EU AI Act and the NAIC Principles on AI. The study further explores the necessity of human-in-the-loop (HITL) governance to reconcile the tensions between predictive accuracy and ethical accountability. Ultimately, the findings suggest that the long-term viability of AI in insurance underwriting is predicated on achieving a "predict and prevent" model that prioritizes transparency as highly as accuracy, thereby fostering institutional trust and consumer confidence in the modern algorithmic economy.
Keywords: Explainable AI (XAI), Insurance Underwriting, SHAP, LIME, Regulatory Compliance, Human-in-the-Loop (HITL), Risk Assessment, Model Transparency, Algorithmic Fairness.
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