Content

Fundamentals of AI and Data - Data and Analytics, Machine Learning, and GenAI for Insurance

All the basics you need to know and master as a senior insurance professional! Covering the transformation of raw data into actionable insights, emphasizing the importance of collaboration between executives and data scientists. Discussing effective data collection, the use of multidimensional data, and types of analytics: descriptive, predictive, and prescriptive. The role of A/B testing in refining decisions is highlighted. Basics of Machine Learning and Deep Learning are explained, including converting data into information and the differences between Supervised and Unsupervised Learning. Applications such as computer vision, Natural Language Processing, and speech recognition are covered, along with strategies to avoid overfitting. Deep Learning tools, architectures, activation functions, and training for insurance executives are also discussed. GenAI's technical mechanisms, innovative use cases to improve customer experiences and operations, and ethical and regulatory considerations for implementing GenAI in insurance are emphasized.

AI and Data along the Insurance Value Chain: Customer Excellence, The Machine Age of Marketing and Product Innovation

How can the latest technologies improve your company`s performance along the value chain? Integration of sonic branding, voice tech, and multimodal analytics for deep insights in multisensory marketing. Highlighting the use of bots for intuitive sales experiences and innovative market research in conversational commerce. Advanced Machine Learning applied for UX mapping and predicting customer emotions. The role of data and AI in transforming insurance explored, emphasizing InsurTech’s impact, cultural shifts, and tech integration. The embedding of insurance into digital ecosystems for competitive advantage. Data and AI-driven product innovation in insurance, focusing on how data analytics and AI drive customer-centric products. The use of big data to tailor policies and services for evolving customer needs, along with AI’s role in predictive models for risk assessment and personalized pricing, enhancing service differentiation.

People & Culture in the Digital Future of Insurance

The success of transformation is highly depending on change management and adapting new technology is no exception. How do you bridge skill gaps and foster a data-driven culture in the data age. Emphasizing the scarcity and importance of data scientists in the insurance sector. Highlighting strategies for talent development and acquisition to ensure future readiness in the evolving insurance industry.

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