Building Trust in Insurance with Explainable AI

Objectives to cover:

• Introduction: Overview of Explainable AI (XAI) and its relevance in the insurance sector.
• Challenges in Insurance: Issues of transparency, trust, and regulatory compliance in traditional processes.
• How Explainable AI Works: Mechanisms and applications of XAI in insurance models.
• Transparency in Underwriting: Role of XAI in creating clear and fair policy underwriting.
• Enhancing Claims Processing: Benefits of explainability in resolving and managing claims.
• Building Customer Trust: Bridging the gap between complex algorithms and customer understanding.
• Mitigating Bias: Ensuring fairness and reducing algorithmic bias in insurance decision-making.
• Regulatory Compliance: XAI’s alignment with laws and its role in meeting industry standards.
• Conclusion: Future of XAI in insurance and recommendations for adoption.

Reference:  IEEE style