Description: As data continues to become more robust and readily available, there has been a push in the actuarial field to transition away from Excel to SQL and Python native cloud-based platforms. While these platforms are widely used and powerful, they present their own challenges for the actuarial industry, requiring processes to be production-ready from the onset, technical expertise for integration, and robust requirements documentation. Low-code tools provide a unique alternative solution, which works well when the organizations operating model allows for actuarial and IT to work together, giving actuaries the flexibility they need while maintaining a level of control followed by the organization. Make use of demonstrations to showcase low-code tools (such as Alteryx and Dataiku) and their use case in the actuarial space. Learn how these low barrier-to-entry tools can be leveraged to create robust and controlled processes. Discuss how low-code tools can integrate with coding languages such as Python as well as efficiently prepare data for downstream visualization platforms such as Tableau and PowerBI.

Description: The use of data to assess risk is not new to the life insurance industry, but has historically been limited to leveraging structured data. Join us as we discuss the next frontier: unstructured data.

Description: "As digitization and automation rapidly progress in today’s life and health industry, issues with misrepresentation during the insurance application process are also becoming a growing concern for insurers around the globe. A recent survey finds that misrepresentation in application forms costs the average insurance customer an estimated 5% to 10% in higher premiums. In order to reduce this risk, insurers must find and correct areas of misrepresentation, consult post-issue sampling, review the automated underwriting processes, and monitor the quality of financial advisory firms. Learn how predictive disclosure modeling detects and reduces misrepresentation by harnessing readily available underwriting data to analyze issues, helping insurers understand various drivers for disclosure rates and areas of misrepresentation.Advantages of this modeling to various stakeholders in the life insurance journey include improved risk selection, claims experience, and reinsurance terms for insurers. For customers, it leads to increased certainty of coverage, payout of the claim, and ultimately lower premiums on average. By the end of the session, attendees will understand: The disclosure modeling process at a high level The technical details of a standard approach to disclosure modeling: data cleaning, standardization, visualization, and predictive modeling Success stories about how predictive disclosure modeling has impacted insurers"

Description: How can generative artificial intelligence (AI) be leveraged to transform and modernize life insurance? Gain an overview of AI and some of their current use cases in the industry, including the latest trends in the market. Next, you will learn about the current challenges companies face, including costly underwriting procedures, customer acquisition and retention, and complicated application processes. Finally, explore how generative AI can help address these challenges, improve customer experience and help streamline processes to free up more time for higher-level business insights and other company initiatives.

Description: Get up to date on the current and potential use of artificial intelligence (AI) in the health care space. In this interactive session, you will learn how AI is being used today and discover its potential to reshape and revolutionize health care. Take advantage of the opportunity to find out how AI can influence patient experience in every phase of health care delivery, and join the conversation about the challenges and limitations that arise with the use of this technology.

Description: Actuaries today have unique opportunities to analyze, understand and use data for strategic decision making. With nearly unlimited computing power and a broad spectrum of data sources at our fingertips, we are enabled to support our actuarial judgment with comprehensive findings backed up by deep data analyses.

Description: Learn more about the predictive modeling that was applied to industry data for the lapse and mortality experience in the post-level term period. Novel approaches were taken to model the shock lapse, subsequent lapse, and mortality experience. Observe a demonstration of the Tableau tool, which was built to allow users to analyze data on their own.

Description: You’ve written the position papers, updated the models, and designed the required disclosures, but you may be wondering how to explain the results during interim financial reporting periods to company management as well as other stakeholders. This session will explore ways to adapt existing management reporting and offer ideas for new metrics. We will explore various methods for analyzing DAC amortization patterns, market risk benefit reserve patterns, and Liability for Future Benefits (LFPB) movements. In addition, this session will explore ways to integrate changes in LDTI with business planning and other broader business issues such as product pricing considerations and reinsurance strategy.