How AI is Revolutionizing Insurance Premiums, Fraud Detection, and Underwriting

AI is significantly transforming the insurance sector by enhancing premium calculation accuracy, coverage explanation, and fraud detection. According to José Luis Bernal, chief digital, data, and innovation officer at MAPFRE USA, AI introduces greater predictability in premiums through advanced technologies that also improve transparency for customers. This development addresses long-standing customer demands for clarity on premium pricing. Automation powered by AI continues to reduce operational costs while simultaneously improving fraud detection methods. Bernal emphasizes that effective fraud detection utilizes a combination of machine learning, graph databases, and shared industry datasets, enabling insurers to avoid penalizing honest customers due to fraudulent activities elsewhere. AI-driven tools are also improving policy comprehension by allowing customers to query their coverage specifics in an accessible manner. In homeowners' insurance, AI facilitates more accurate valuation of home contents, reducing guesswork and streamlining quoting for both customers and agents. The increasing complexity and pace of emerging risks, such as climate and cyber threats, prompt AI to enhance underwriting accuracy and forecasting. While this may lead to greater price volatility at policy renewal, improved data models justify these adjustments, potentially increasing regulatory scrutiny regarding pricing flexibility in the U.S. market. Bernal notes that the U.S. insurance regulatory environment focuses more on solvency and reserves than pricing methods, allowing insurers more latitude in pricing strategies. The volatility driven by AI insights may also generate demand for new insurance products that stabilize premium rates over time. Despite AI's potential, many insurers lack the foundational technology infrastructure, such as cloud systems and robust API integrations, necessary to fully leverage AI tools. Data management challenges, particularly around unstructured data and the need for orchestration layers, hinder cohesive automation implementation. Bernal advocates a strategic, bottom-up innovation approach that emphasizes iterative testing and return on investment (ROI) measurement before scaling AI solutions. He envisions insurers adopting modular, cloud-first platforms interoperable with specialized vendors' technology, facilitating personalized and cost-efficient services at scale.